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  • 1.
    Abbott, Rebecca
    et al.
    Northwestern Univ, IL 60611 USA.
    Peolsson, Anneli
    Linköping University, Department of Medical and Health Sciences, Division of Physiotherapy. Linköping University, Faculty of Medicine and Health Sciences.
    West, Janne
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Medicine and Health Sciences.
    Elliott, James M.
    Northwestern Univ, IL 60611 USA; Univ Queensland, Australia; Zurich Univ Appl Sci, Switzerland.
    Åslund, Ulrika
    Linköping University, Department of Medical and Health Sciences, Division of Physiotherapy. Linköping University, Faculty of Medicine and Health Sciences.
    Karlsson, Anette
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    The qualitative grading of muscle fat infiltration in whiplash using fat and water magnetic resonance imaging2018In: The spine journal, ISSN 1529-9430, E-ISSN 1878-1632, Vol. 18, no 5, p. 717-725Article in journal (Refereed)
    Abstract [en]

    BACKGROUND CONTEXT: The development of muscle fat infiltration (MFI) in the neck muscles is associated with poor functional recovery following whiplash injury. Custom software and time-consuming manual segmentation of magnetic resonance imaging (MRI) is required for quantitative analysis and presents as a barrier for clinical translation. PURPOSE: The purpose of this work was to establish a qualitative MRI measure for MFI and evaluate its ability to differentiate between individuals with severe whiplash-associated disorder (WAD), mild or moderate WAD, and healthy controls. STUDY DESIGN/SETTING: This is a cross-sectional study. PATIENT SAMPLE: Thirty-one subjects with WAD and 31 age-and sex-matched controls were recruited from an ongoing randomized controlled trial. OUTCOME MEASURES: The cervical multifidus was visually identified and segmented into eighths in the axial fat/water images (C4-C7). Muscle fat infiltration was assessed on a visual scale: 0 for no or marginal MFI, 1 for light MFI, and 2 for distinct MFI. The participants with WAD were divided in two groups: mild or moderate and severe based on Neck Disability Index % scores. METHODS: The mean regional MFI was compared between the healthy controls and each of the WAD groups using the Mann-Whitney U test. Receiver operator characteristic (ROC) analyses were carried out to evaluate the validity of the qualitative method. RESULTS: Twenty (65%) patients had mild or moderate disability and 11 (35%) were considered severe. Inter- and intra-rater reliability was excellent when grading was averaged by level or when frequency of grade II was considered. Statistically significant differences (pamp;lt;.05) in regional MFI were particularly notable between the severe WAD group and healthy controls. The ROC curve, based on detection of distinct MFI, showed an area-under-the curve of 0.768 (95% confidence interval 0.59-0.94) for discrimination of WAD participants. CONCLUSIONS: These preliminary results suggest a qualitative MRI measure for MFI is reliable and valid, and may prove useful toward the classification of WAD in radiology practice. (C) 2017 Elsevier Inc. All rights reserved.

  • 2.
    Abramian, David
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    REFACING: RECONSTRUCTING ANONYMIZED FACIAL FEATURES USING GANS2019In: 2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019), IEEE , 2019, p. 1104-1108Conference paper (Refereed)
    Abstract [en]

    Anonymization of medical images is necessary for protecting the identity of the test subjects, and is therefore an essential step in data sharing. However, recent developments in deep learning may raise the bar on the amount of distortion that needs to be applied to guarantee anonymity. To test such possibilities, we have applied the novel CycleGAN unsupervised image-to-image translation framework on sagittal slices of T1 MR images, in order to reconstruct, facial features from anonymized data. We applied the CycleGAN framework on both face-blurred and face-removed images. Our results show that face blurring may not provide adequate protection against malicious attempts at identifying the subjects, while face removal provides more robust anonymization, but is still partially reversible.

  • 3.
    Akbarian-Tefaghi, Ladan
    et al.
    Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience, UCL Institute of Neurology, London, UK.
    Akram, Harith
    Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience, UCL Institute of Neurology, London, UK.
    Johansson, Johannes
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Zrinzo, Ludvic
    Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience, UCL Institute of Neurology, London, UK.
    Kefalopoulou, Zinovia
    Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience, UCL Institute of Neurology, London, UK.
    Limousin, Patricia
    Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience, UCL Institute of Neurology, London, UK.
    Joyce, Eileen
    Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience, UCL Institute of Neurology, London, UK.
    Hariz, Marwan
    Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience, UCL Institute of Neurology, London, UK.
    Wårdell, Karin
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Foltynie, Tom
    Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience, UCL Institute of Neurology, London, UK.
    Refining the Deep Brain Stimulation Target within the Limbic Globus Pallidus Internus for Tourette Syndrome2017In: Stereotactic and Functional Neurosurgery, ISSN 1011-6125, E-ISSN 1423-0372, Vol. 95, no 4, p. 251-258Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Deep brain stimulation (DBS) in patients with severe, refractory Tourette syndrome (TS) has demonstrated promising but variable results thus far. The thalamus and anteromedial globus pallidus internus (amGPi) have been the most commonly stimulated sites within the cortico-striato thalamic circuit, but an optimal target is yet to be elucidated.

    OBJECTIVES: This study of 15 patients with long-term amGPi DBS for severe TS investigated whether a specific anatomical site within the amGPi correlated with optimal clinical outcome for the measures of tics, obsessive compulsive behaviour (OCB), and mood.

    METHODS: Validated clinical assessments were used to measure tics, OCB, quality of life, anxiety, and depression before DBS and at the latest follow-up (17-82 months). Electric field simulations were created for each patient using information on electrode location and individual stimulation parameters. A subsequent regression analysis correlated these patient-specific simulations to percentage changes in outcome measures in order to identify any significant voxels related to clinical improvement.

    RESULTS: A region within the ventral limbic GPi, specifically on the medial medullary lamina in the pallidum at the level of the AC-PC, was significantly associated with improved tics but not mood or OCB outcome.

    CONCLUSIONS: This study adds further support to the application of DBS in a tic-related network, though factors such as patient sample size and clinical heterogeneity remain as limitations and replication is required.

  • 4.
    Ali, Zaheer
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Islam, Anik
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Sherrell, Peter
    Imperial Coll London, England.
    Le-Moine, Mark
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Lolas, Georgios
    Univ Athens, Greece.
    Syrigos, Konstantinos
    Univ Athens, Greece.
    Rafat, Mehrdad
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Jensen, Lasse
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Clinical Pharmacology.
    Adjustable delivery of pro-angiogenic FGF-2 by alginate: collagen microspheres2018In: BIOLOGY OPEN, ISSN 2046-6390, Vol. 7, no 3, article id UNSP bio027060Article in journal (Refereed)
    Abstract [en]

    Therapeutic induction of blood vessel growth (angiogenesis) in ischemic tissues holds great potential for treatment of myocardial infarction and stroke. Achieving sustained angiogenesis and vascular maturation has, however, been highly challenging. Here, we demonstrate that alginate: collagen hydrogels containing therapeutic, pro-angiogenic FGF-2, and formulated as microspheres, is a promising and clinically relevant vehicle for therapeutic angiogenesis. By titrating the amount of readily dissolvable and degradable collagen with more slowly degradable alginate in the hydrogel mixture, the degradation rates of the biomaterial controlling the release kinetics of embedded proangiogenic FGF-2 can be adjusted. Furthermore, we elaborate a microsphere synthesis protocol allowing accurate control over sphere size, also a critical determinant of degradation/release rate. As expected, alginate: collagen microspheres were completely biocompatible and did not cause any adverse reactions when injected in mice. Importantly, the amount of pro-angiogenic FGF-2 released from such microspheres led to robust induction of angiogenesis in zebrafish embryos similar to that achieved by injecting FGF-2-releasing cells. These findings highlight the use of microspheres constructed from alginate: collagen hydrogels as a promising and clinically relevant delivery system for pro-angiogenic therapy.

  • 5.
    Alirezaie, Marjan
    et al.
    Institutionen för naturvetenskap och teknik , Örebro Universitet.
    Hammar, Karl
    Högskolan i Jönköping, JTH, Datateknik och informatik.
    Blomqvist, Eva
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering.
    Nyström, Mikael
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Ivanova, Valentina
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
    SmartEnv Ontology in E-care@home2018In: SSN 2018 - Semantic Sensor Networks Workshop: Proceedings of the 9th International Semantic Sensor Networks Workshopco-located with 17th International Semantic Web Conference (ISWC 2018) / [ed] Maxime Lefrançois, Raúl Garcia Castro, Amélie Gyrard, Kerry Taylor, CEUR-WS , 2018, Vol. 2213, p. 72-79Conference paper (Refereed)
    Abstract [en]

    In this position paper we briefly introduce SmartEnv ontology which relies on SEmantic Sensor Network (SSN) ontology and is used to represent different aspects of smart and sensorized environments. We will also talk about E-carehome project aiming at providing an IoT-based health-care system for elderly people at their homes. Furthermore, we refer to the role of SmartEnv in Ecarehome and how it needs to be further extended to achieve semantic interoperability as one of the challenges in development of autonomous health care systems at home.

  • 6.
    Alonso, Fabiola
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Deep brain stimulation: Patient-specific modelling, simulation and visualization of  DBS electric field2019Conference paper (Other (popular science, discussion, etc.))
  • 7.
    Alonso, Fabiola
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Models and Simulations of the Electric Field in Deep Brain Stimulation: Comparison of Lead Designs, Operating Modes and Tissue Conductivity2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Deep brain stimulation (DBS) is an established surgical therapy for movement disorders such as Parkinson’s disease (PD) and essential tremor (ET). A thin electrode is implanted in a predefined area of the brain with the use of stereotactic neurosurgery. In the last few years new DBS electrodes and systems have been developed with possibilities for using more parameters for control of the stimulation volume.

    In this thesis, simulations using the finite element method (FEM) have been developed and used for investigation of the electric field (EF) extension around different types of DBS lead designs (symmetric, steering) and stimulation modes (voltage, current). The electrode surrounding was represented either with a homogeneous model or a patient-specific model based on individual preoperative magnetic resonance imaging (MRI). The EF was visualized and compared for different lead designs and operating modes.

    In Paper I, the EF was quantitatively investigated around two lead designs (3389 and 6148) simulated to operate in voltage and current mode under acute and chronic time points following implantation.Simulations showed a major impact on the EF extension between postoperative time points which may explain the clinical decisions to change the stimulation amplitude weeks after implantation. In Paper II, the simulations were expanded to include two leads having steering function (6180, Surestim1) and patient-specific FEM simulations in the zona incerta. It was found that both the heterogeneity of the tissue and the operating mode, influence the EF distribution and that equivalent contact configurations of the leads result in similar EF. The steering mode presented larger volumes in current mode when using equivalent amplitudes. Simulations comparing DBS and intraoperative stimulation test using a microelectrode recording (MER) system (Paper III), showed that several parallel MER leads and the presence of the non-active DBS contacts influence the EF distribution and that the DBS EF volume can cover, but also extend to, other anatomical areas.

    Paper IV introduces a method for an objective exploitation of intraoperative stimulation test data in order to identify the optimal implant position in the thalamus of the chronic DBS lead. Patient-specific EF simulations were related to the anatomy with the help of brain atlases and the clinical effects which were quantified by accelerometers. The first results indicate that the good clinical effect in ET is due to several structures around the ventral intermediate nucleus of the thalamus.

  • 8.
    Alonso, Fabiola
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Vogel, Dorian
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Wårdell, Karin
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Hemm-Ode, Simone
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Institute for Medical and Analytical Technologies and Department of Biomedical Engineering, University of Applied Sciences and Art Northwestern Switzerland.
    Comparison between intraoperative and chronic and deep brain stimulation2017Conference paper (Refereed)
    Abstract [en]

    INTRODUCTION

    The success of the deep brain stimulation (DBS) therapy relies primarily in the localization of the implanted electrode, implying the need of utmost accuracy in the targeting process. Intraoperative microelectrode recording and stimulation tests are a common procedure before implanting the permanent DBS lead to determine the optimal position with a large therapeutic window where side effects are avoided and the best improvement of the symptoms is achieved. Differences in dimensions and operating modes exist between the exploration and the permanent DBS electrode which might lead to different stimulation fields, even when ideal placement is achieved. The aim of this investigation is to compare the electric field (EF) distribution around the intraoperative and the chronic electrode, assuming that both have exactly the same position.

    METHODS

    3D models of the intraoperative exploration electrode and the chronically implanted DBS lead 3389 (Medtronic Inc., USA) were developed using COMSOL 5.2 (COMSOL AB, Sweden). Patient-specific MR images were used to determine the conductive medium around the electrode. The exploration electrode and the first DBS contact were set to current and voltage respectively (0.2mA(V) - 3 mA(V) in 0.1 mA(V) steps). The intraoperative model included the grounded guide tube used to introduce the exploration electrode; for the chronic DBS model, the outer boundaries were grounded and the inactive contacts were set to floating potential considering a monopolar configuration. The localization of the exploration and the chronic electrode was set according to the planned trajectory. The EF was visualized and compared in terms of volume and extension using a fixed isocontour of 0.2 V/mm.

    RESULTS

    The EF distribution simulated for the exploration electrode showed the influence of the parallel trajectory and the grounded guide tube. For an amplitude of e.g. 2 mA/2 V, the EF extension of the intraoperative was 0.6 mm larger than the chronic electrode at the target level; the corresponding difference in volume was 76.1 mm3.

    CONCLUSION

    Differences in the EF shape between the exploration and the chronic DBS electrode have been observed using patient-specific models. The larger EF extension obtained for the exploration electrode responds to its higher impedance and the use of current controlled stimulation. The presence of EF around the guide tube and the influence of the parallel trajectory require further experimental and clinical evaluation.

  • 9.
    Alonso, Fabiola
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Zsigmond, Peter
    Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Surgery in Linköping. Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Department of Neurosurgery.
    Wårdell, Karin
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Influence of Virchow-Robin spaces in the Electric Field Distribution in Subthalamic Nucleus Deep Brain Stimulation2019Conference paper (Refereed)
    Abstract [en]

    Objectives: Previous investigations have shown the appearance of cysts i.e. Virchow-Robin spaces (VR) in the basal ganglia and their relationship with parkinsonian symptoms [1-3]. Simulations [4]using the finite element method (FEM) suggests that VR affects the electric field around deep brain stimulation (DBS) electrodes. The aim of the study was to evaluate how the electric field is modified by the presence of cysts in the STN. Methods: The effect of cysts on the electric field around the DBS lead placed in the STN was evaluated using FEM. 3D patient-specific brain models were built with COMSOL 5.2 (COMSOL AB, Sweden) and an in-house developed software [5] to convert a T2 weighted MRI of Parkinsonian patients (ethics approval no: 2012/434-3) into electrical conductivity matrix readable by FEM software. VR was classified as CSF [6]assigning a high electrical conductivity (2.0 S/m). The stimulation amplitudes were set to the clinically programmed values. Depending on the lead used, the stimulation was set to voltage control (3389) or current control (6180, ring mode). The coordinates corresponding to the lowest (first) electrode and the third higher up in the lead, taken from the postoperative CT electrode artefact, were used to localize the leads in the brain model [7]. The electric field was visualized with a 0.2V/mm isosurface. Results: Simulations showed that the electric field distribution is affected by the cysts. The higher conductivity at these regions in the vicinity of the electrode redistributes the electric field pushing it away from the cyst. The same effect occurs regardless of the operating mode or the lead design as long as the directional lead is configured in ring mode. Conclusions: The use of patient-specific models has shown the importance of considering nuances of the patients’ anatomy in the STN. This information can be used to determine the stimulation parameter and to support the analysis of side effects induced by the stimulation. The potential advantage of directional leads can also be assessed by including in the model patient-specific data.

  • 10.
    Andersson, Thord
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Dept. of C4ISR, Swedish Defence Research Agency, Linköping, Sweden, .
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Geodesic registration for interactive atlas-based segmentation using learned multi-scale anatomical manifolds2018In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 112, p. 340-345Article in journal (Refereed)
    Abstract [en]

    Atlas-based segmentation is often used to segment medical image regions. For intensity-normalized data, the quality of these segmentations is highly dependent on the similarity between the atlas and the target under the used registration method. We propose a geodesic registration method for interactive atlas-based segmentation using empirical multi-scale anatomical manifolds. The method utilizes unlabeled images together with the labeled atlases to learn empirical anatomical manifolds. These manifolds are defined on distinct scales and regions and are used to propagate the labeling information from the atlases to the target along anatomical geodesics. The resulting competing segmentations from the different manifolds are then ranked according to an image-based similarity measure. We used image volumes acquired using magnetic resonance imaging from 36 subjects. The performance of the method was evaluated using a liver segmentation task. The result was then compared to the corresponding performance of direct segmentation using Dice Index statistics. The method shows a significant improvement in liver segmentation performance between the proposed method and direct segmentation. Furthermore, the standard deviation in performance decreased significantly. Using competing complementary manifolds defined over a hierarchy of region of interests gives an additional improvement in segmentation performance compared to the single manifold segmentation.

  • 11.
    Aserod, Hanne
    et al.
    Univ Bergen, Norway.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Univ Bergen, Norway.
    Designing a mobile system for safety reporting of arthroplasty adverse events2018In: EMBEC and NBC 2017, SPRINGER-VERLAG SINGAPORE PTE LTD , 2018, Vol. 65, p. 571-574Conference paper (Refereed)
    Abstract [en]

    This paper presents a mobile software application development for safety reporting of adverse events within the field of arthroplasty. Proposed user interface enables entry of data specific for adverse events of the knee and hip implants. Besides the patient data, the system supports entry of the event, its classification (serious, non-serious), its follow up, as well as a connection to the database maintained within the Helse Bergen hospital information system. Safety reports can be initiated and retrieved on request and depending on the adjudication of the event; suspected severe events should be followed up until their resolution. Expert evaluation of the first design solution was performed using low fidelity prototype. It has shown that design was relevant, straightforward, done in a way that official reporting would commence. Some users were positive to the reporting, some felt it would demand more work. A comprehensive evaluation with different potential user groups is planned to meet their needs and understand their views.

  • 12.
    Aserod, Hanne
    et al.
    Univ Bergen, Norway.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Univ Bergen, Norway.
    Pharmacovigilance Mobile Tool Design in the Field of Arhroplasty2017In: INFORMATICS EMPOWERS HEALTHCARE TRANSFORMATION, IOS PRESS , 2017, Vol. 238, p. 104-107Conference paper (Refereed)
    Abstract [en]

    Pharmacovigilance is an important part of the patient safety and it has a great appeal to physicians. It is concerned with the safety of medical devices and treatments in the light of understanding the risks and dangers based on the already reported safety issues. Internet resources such as the Manufacturer And User Facility Device Experience (MAUDE) web-site are often retrieved due to the lack of internal, local safety databases. The research looked at how Human Computer Interaction could improve user experience. We have designed data entry for safety reporting and pharmacovigilance based on the web-bases system called WebBISS (Web-based implant search system). The expectation is not only to improve usability, but also to stimulate physicians to enter their safety data and become also contributors, and not only users of information. The expert evaluation has been generally positive and encouraged stronger help and error reporting functions. The high fidelity design has given a good impression of the future mobile solution.

  • 13.
    Bardolet Pettersson, Susana
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering.
    Managing imbalanced training data by sequential segmentation in machine learning2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Imbalanced training data is a common problem in machine learning applications. Thisproblem refers to datasets in which the foreground pixels are significantly fewer thanthe background pixels. By training a machine learning model with imbalanced data, theresult is typically a model that classifies all pixels as the background class. A result thatindicates no presence of a specific condition when it is actually present is particularlyundesired in medical imaging applications. This project proposes a sequential system oftwo fully convolutional neural networks to tackle the problem. Semantic segmentation oflung nodules in thoracic computed tomography images has been performed to evaluate theperformance of the system. The imbalanced data problem is present in the training datasetused in this project, where the average percentage of pixels belonging to the foregroundclass is 0.0038 %. The sequential system achieved a sensitivity of 83.1 % representing anincrease of 34 % compared to the single system. The system only missed 16.83% of thenodules but had a Dice score of 21.6 % due to the detection of multiple false positives. Thismethod shows considerable potential to be a solution to the imbalanced data problem withcontinued development.

  • 14.
    Benosman, M. M.
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Tlemcen Univ, Biomed Engn Dept, Tilimsen 13000, Algeria.
    Bereksi-Reguig, F.
    Tlemcen Univ, Algeria.
    Salerud, Göran
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    STRONG REAL-TIME QRS COMPLEX DETECTION2017In: Journal of Mechanics in Medicine and Biology, ISSN 0219-5194, Vol. 17, no 8, article id 1750111Article in journal (Refereed)
    Abstract [en]

    Heart rate variability (HRV) analysis is used as a marker of autonomic nervous system activity which may be related to mental and/or physical activity. HRV features can be extracted by detecting QRS complexes from an electrocardiogram (ECG) signal. The difficulties in QRS complex detection are due to the artifacts and noises that may appear in the ECG signal when subjects are performing their daily life activities such as exercise, posture changes, climbing stairs, walking, running, etc. This study describes a strong computation method for real-time QRS complex detection. The detection is improved by the prediction of the position of R waves by the estimation of the RR intervals lengths. The estimation is done by computing the intensity of the electromyogram noises that appear in the ECG signals and known here in this paper as ECG Trunk Muscles Signals Amplitude (ECG-TMSA). The heart rate (HR) and ECG-TMSA increases with the movement of the subject. We use this property to estimate the lengths of the RR intervals. The method was tested using famous databases, and also with signals acquired when an experiment with 17 subjects from our laboratory. The obtained results using ECG signals from the MIT-Noise Stress Test Database show a QRS complex detection error rate (ER) of 9.06%, a sensitivity of 95.18% and a positive prediction of 95.23%. This method was also tested against MIT-BIH Arrhythmia Database, the result are 99.68% of sensitivity and 99.89% of positive predictivity, with ER of 0.40%. When applied to the signals obtained from the 17 subjects, the algorithm gave an interesting result of 0.00025% as ER, 99.97% as sensitivity and 99.99% as positive predictivity.

  • 15.
    Bergqvist, Niclas
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Nyman, Elin
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. AstraZeneca RandD, Sweden.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Stenkula, Karin G.
    Lund University, Sweden.
    A systems biology analysis connects insulin receptor signaling with glucose transporter translocation in rat adipocytes2017In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 292, no 27, p. 11206-11217Article in journal (Refereed)
    Abstract [en]

    Type 2 diabetes is characterized by insulin resistance, which arises from malfunctions in the intracellular insulin signaling network. Knowledge of the insulin signaling network is fragmented, and because of the complexity of this network, little consensus has emerged for the structure and importance of the different branches of the network. To help overcome this complexity, systems biology mathematical models have been generated for predicting both the activation of the insulin receptor (IR) and the redistribution of glucose transporter 4 (GLUT4) to the plasma membrane. Although the insulin signal transduction between IR and GLUT4 has been thoroughly studied with modeling and time-resolved data in human cells, comparable analyses in cells from commonly used model organisms such as rats and mice are lacking. Here, we combined existing data and models for rat adipocytes with new data collected for the signaling network between IR and GLUT4 to create a model also for their interconnections. To describe all data (amp;gt;140 data points), the model needed three distinct pathways from IR to GLUT4: (i) via protein kinase B (PKB) and Akt substrate of 160 kDa (AS160), (ii) via an AS160-independent pathway from PKB, and (iii) via an additional pathway from IR, e.g. affecting the membrane constitution. The developed combined model could describe data not used for training the model and was used to generate predictions of the relative contributions of the pathways from IR to translocation of GLUT4. The combined model provides a systems-level understanding of insulin signaling in rat adipocytes, which, when combined with corresponding models for human adipocytes, may contribute to model-based drug development for diabetes.

  • 16.
    Bergstrand, Sara
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Nursing Science. Linköping University, Faculty of Medicine and Health Sciences.
    Morales, Maria-Aurora
    CNR Inst Clin Physiol, Italy.
    Coppini, Giuseppe
    CNR Inst Clin Physiol, Italy.
    Larsson, Marcus
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Strömberg, Tomas
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    The relationship between forearm skin speed-resolved perfusion and oxygen saturation, and finger arterial pulsation amplitudes, as indirect measures of endothelial function2018In: Microcirculation, ISSN 1073-9688, E-ISSN 1549-8719, Vol. 25, no 2, article id e12422Article in journal (Refereed)
    Abstract [en]

    Objective: Endothelial function is important for regulating peripheral blood flow to meet varying metabolic demands and can be measured indirectly during vascular provocations. In this study, we compared the PAT finger response (EndoPAT) after a 5-minutes arterial occlusion to that from forearm skin comprehensive microcirculation analysis (EPOS). Methods: Measurements in 16 subjects with varying cardiovascular risk factors were carried out concurrently with both methods during arterial occlusion, while forearm skin was also evaluated during local heating. Results: Peak values for EPOS skin Perf(conv) and speed-resolved total perfusion after the release of the occlusion were significantly correlated to the EndoPAT RHI (rho =.68, P = .007 and rho =.60, P = .025, respectively), mainly due to high-speed blood flow. During local heating, EPOS skin oxygen saturation, SO2, was significantly correlated to RHI (rho = .62, P =.043). This indicates that SO2 may have diagnostic value regarding endothelial function. Conclusions: We have demonstrated for the first time a significant relationship between forearm skin microcirculatory perfusion and oxygen saturation and finger PAT. Both local heating and reactive hyperemia are useful skin provocations. Further studies are needed to understand the precise regulation mechanisms of blood flow and oxygenation during these tests.

  • 17.
    Black, David
    et al.
    Medical Image Computing, University of Bremen; Jacobs University, Bremen; Fraunhofer MEVIS, Bremen, Germany.
    Hahn, Horst
    Jacobs University, Bremen; Fraunhofer, MEVIS, Germany.
    Kikinis, Ron
    Brigham and Women's Hospital and Harvard Medical School, Boston, USA.
    Wårdell, Karin
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Haj Hosseini, Neda
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Auditory display for Fluorescence-guided open brain tumor surgery2018In: International Journal of Computer Assisted Radiology and Surgery, ISSN 1861-6410, E-ISSN 1861-6429, Vol. 13, no 1, p. 25-35Article in journal (Refereed)
    Abstract [en]

    PURPOSE:

    Protoporphyrin (PpIX) fluorescence allows discrimination of tumor and normal brain tissue during neurosurgery. A handheld fluorescence (HHF) probe can be used for spectroscopic measurement of 5-ALA-induced PpIX to enable objective detection compared to visual evaluation of fluorescence. However, current technology requires that the surgeon either views the measured values on a screen or employs an assistant to verbally relay the values. An auditory feedback system was developed and evaluated for communicating measured fluorescence intensity values directly to the surgeon.

    METHODS:

    The auditory display was programmed to map the values measured by the HHF probe to the playback of tones that represented three fluorescence intensity ranges and one error signal. Ten persons with no previous knowledge of the application took part in a laboratory evaluation. After a brief training period, participants performed measurements on a tray of 96 wells of liquid fluorescence phantom and verbally stated the perceived measurement values for each well. The latency and accuracy of the participants' verbal responses were recorded. The long-term memorization of sound function was evaluated in a second set of 10 participants 2-3 and 7-12 days after training.

    RESULTS:

    The participants identified the played tone accurately for 98% of measurements after training. The median response time to verbally identify the played tones was 2 pulses. No correlation was found between the latency and accuracy of the responses, and no significant correlation with the musical proficiency of the participants was observed on the function responses. Responses for the memory test were 100% accurate.

    CONCLUSION:

    The employed auditory display was shown to be intuitive, easy to learn and remember, fast to recognize, and accurate in providing users with measurements of fluorescence intensity or error signal. The results of this work establish a basis for implementing and further evaluating auditory displays in clinical scenarios involving fluorescence guidance and other areas for which categorized auditory display could be useful.

  • 18.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    MRI adipose tissue and muscle composition analysis: a review of automation techniques2018In: British Journal of Radiology, ISSN 0007-1285, E-ISSN 1748-880X, Vol. 91, no 1089, article id 20180252Article, review/survey (Refereed)
    Abstract [en]

    MRI is becoming more frequently used in studies involving measurements of adipose tissue and volume and composition of skeletal muscles. The large amount of data generated by MRI calls for automated analysis methods. This review article presents a summary of automated and semi-automated techniques published between 2013 and 2017. Technical aspects and clinical applications for MRI-based adipose tissue and muscle composition analysis are discussed based on recently published studies. The conclusion is that very few clinical studies have used highly automated analysis methods, despite the rapidly increasing use of MRI for body composition analysis. Possible reasons for this are that the availability of highly automated methods has been limited for non-imaging experts, and also that there is a limited number of studies investigating the reproducibility of automated methods for MRI-based body composition analysis.

  • 19.
    Borga, Magnus
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    West, Janne
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Medicine and Health Sciences.
    Bell, Jimmy
    Westminster University, London, UK.
    Harvey, Nicholas
    University of Southampton, IK.
    Romu, Thobias
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Heymsfield, Steven
    Pennington Biomedical Research Center, Baton Rouge, LA, US.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Advanced MR Analytics AB, Linköping, Sweden.
    Advanced body composition assessment: From body mass index to body composition profiling2018In: Journal of Investigative Medicine, ISSN 1081-5589, E-ISSN 1708-8267, Vol. 66, p. 887-895Article, review/survey (Refereed)
    Abstract [en]

    This paper gives a brief overview of common non-invasive techniques for body composition analysis and a more in-depth review of a body composition assessment method based on fat-referenced quantitative magnetic resonance imaging (MRI). Earlier published studies of this method are summarized, and a previously un-published validation study, based on 4.753 subjects from the UK Biobank imaging cohort, comparing the quantitative MRI method with dual-energy x-ray absorptiometry (DXA) is presented. For whole-body measurements of adipose tissue (AT) or fat and lean tissue (LT), DXA and quantitative MRI show excellent agreement with linear correlation of 0.99 and 0.97, and coefficient of variation (CV) of 4.5 % and 4.6 % for fat (computed from AT) and lean tissue respectively, but the agreement was found significantly lower for visceral adipose tissue, with a CV of more than 20 %. The additional ability of MRI to also measure muscle volumes, muscle AT infiltration and ectopic fat in combination with rapid scanning protocols and efficient image analysis tools make quantitative MRI a powerful tool for advanced body composition assessment. 

  • 20.
    Brannmark, Cecilia
    et al.
    University of Gothenburg, Sweden.
    Lövfors, William
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Komai, Ali M.
    University of Gothenburg, Sweden.
    Axelsson, Tom
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    El Hachmane, Mickael F.
    University of Gothenburg, Sweden.
    Musovic, Saliha
    University of Gothenburg, Sweden.
    Paul, Alexandra
    Chalmers University of Technology, Sweden.
    Nyman, Elin
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. AstraZeneca RandD, Sweden.
    Olofsson, Charlotta S.
    University of Gothenburg, Sweden.
    Mathematical modeling of white adipocyte exocytosis predicts adiponectin secretion and quantifies the rates of vesicle exo- and endocytosis2017In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 292, no 49, p. 20032-20043Article in journal (Refereed)
    Abstract [en]

    Adiponectin is a hormone secreted from white adipocytes and takes part in the regulation of several metabolic processes. Although the pathophysiological importance of adiponectin has been thoroughly investigated, the mechanisms controlling its release are only partly understood. We have recently shown that adiponectin is secreted via regulated exocytosis of adiponectin-containing vesicles, that adiponectin exocytosis is stimulated by cAMP-dependent mechanisms, and that Ca2+ and ATP augment the cAMP-triggered secretion. However, much remains to be discovered regarding the molecular and cellular regulation of adiponectin release. Here, we have used mathematical modeling to extract detailed information contained within our previously obtained high-resolution patch-clamp time-resolved capacitance recordings to produce the first model of adiponectin exocytosis/secretion that combines all mechanistic knowledge deduced from electrophysiological experimental series. This model demonstrates that our previous understanding of the role of intracellular ATP in the control of adiponectin exocytosis needs to be revised to include an additional ATP-dependent step. Validation of the model by introduction of data of secreted adiponectin yielded a very close resemblance between the simulations and experimental results. Moreover, we could show that Ca2+-dependent adiponectin endocytosis contributes to the measured capacitance signal, and we were able to predict the contribution of endocytosis to the measured exocytotic rate under different experimental conditions. In conclusion, using mathematical modeling of published and newly generated data, we have obtained estimates of adiponectin exo- and endocytosis rates, and we have predicted adiponectin secretion. We believe that our model should have multiple applications in the study of metabolic processes and hormonal control thereof.

  • 21.
    Campbell, Walter S.
    et al.
    Univ Nebraska Med Ctr, NE 68198 USA.
    Karlsson, Daniel
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Vreeman, Daniel J.
    Indiana Univ Sch Med, IN 46202 USA.
    Lazenby, Audrey J.
    Univ Nebraska Med Ctr, NE 68198 USA.
    Talmon, Geoffrey A.
    Univ Nebraska Med Ctr, NE 68198 USA.
    Campbell, James R.
    Univ Nebraska Med Ctr, NE USA.
    A computable pathology report for precision medicine: extending an observables ontology unifying SNOMED CT and LOINC2018In: JAMIA Journal of the American Medical Informatics Association, ISSN 1067-5027, E-ISSN 1527-974X, Vol. 25, no 3, p. 259-266Article in journal (Refereed)
    Abstract [en]

    The College of American Pathologists (CAP) introduced the first cancer synoptic reporting protocols in 1998. However, the objective of a fully computable and machine-readable cancer synoptic report remains elusive due to insufficient definitional content in Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) and Logical Observation Identifiers Names and Codes (LOINC). To address this terminology gap, investigators at the University of Nebraska Medical Center (UNMC) are developing, authoring, and testing a SNOMED CT observable ontology to represent the data elements identified by the synoptic worksheets of CAP. Investigators along with collaborators from the US National Library of Medicine, CAP, the International Health Terminology Standards Development Organization, and the UK Health and Social Care Information Centre analyzed and assessed required data elements for colorectal cancer and invasive breast cancer synoptic reporting. SNOMED CT concept expressions were developed at UNMC in the Nebraska LexiconA (c) SNOMED CT namespace. LOINC codes for each SNOMED CT expression were issued by the Regenstrief Institute. SNOMED CT concepts represented observation answer value sets. UNMC investigators created a total of 194 SNOMED CT observable entity concept definitions to represent required data elements for CAP colorectal and breast cancer synoptic worksheets, including biomarkers. Concepts were bound to colorectal and invasive breast cancer reports in the UNMC pathology system and successfully used to populate a UNMC biobank. The absence of a robust observables ontology represents a barrier to data capture and reuse in clinical areas founded upon observational information. Terminology developed in this project establishes the model to characterize pathology data for information exchange, public health, and research analytics.

  • 22.
    Casas Garcia, Belén
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Lantz, Jonas
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Viola, Frederica
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Bolger, Ann F.
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. University of Calif San Francisco, CA USA.
    Carlhäll, Carljohan
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Karlsson, Matts
    Linköping University, Department of Management and Engineering, Applied Thermodynamics and Fluid Mechanics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ebbers, Tino
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Bridging the gap between measurements and modelling: a cardiovascular functional avatar2017In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, article id 6214Article in journal (Refereed)
    Abstract [en]

    Lumped parameter models of the cardiovascular system have the potential to assist researchers and clinicians to better understand cardiovascular function. The value of such models increases when they are subject specific. However, most approaches to personalize lumped parameter models have thus far required invasive measurements or fall short of being subject specific due to a lack of the necessary clinical data. Here, we propose an approach to personalize parameters in a model of the heart and the systemic circulation using exclusively non-invasive measurements. The personalized model is created using flow data from four-dimensional magnetic resonance imaging and cuff pressure measurements in the brachial artery. We term this personalized model the cardiovascular avatar. In our proof-of-concept study, we evaluated the capability of the avatar to reproduce pressures and flows in a group of eight healthy subjects. Both quantitatively and qualitatively, the model-based results agreed well with the pressure and flow measurements obtained in vivo for each subject. This non-invasive and personalized approach can synthesize medical data into clinically relevant indicators of cardiovascular function, and estimate hemodynamic variables that cannot be assessed directly from clinical measurements.

  • 23.
    Casas Garcia, Belén
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Viola, Frederica
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Bolger, Ann F
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Univ Calif San Francisco, CA USA.
    Karlsson, Matts
    Linköping University, Department of Management and Engineering, Applied Thermodynamics and Fluid Mechanics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Carlhäll, Carljohan
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ebbers, Tino
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Non-invasive Assessment of Systolic and Diastolic Cardiac Function During Rest and Stress Conditions Using an Integrated Image-Modeling Approach2018In: Frontiers in Physiology, ISSN 1664-042X, E-ISSN 1664-042X, Vol. 9, article id 1515Article in journal (Refereed)
    Abstract [en]

    Background: The possibility of non-invasively assessing load-independent parameters characterizing cardiac function is of high clinical value. Typically, these parameters are assessed during resting conditions. However, for diagnostic purposes, the parameter behavior across a physiologically relevant range of heart rate and loads is more relevant than the isolated measurements performed at rest. This study sought to evaluate changes in non-invasive estimations of load-independent parameters of left-ventricular contraction and relaxation patterns at rest and during dobutamine stress. Methods: We applied a previously developed approach that combines non-invasive measurements with a physiologically-based, reduced-order model of the cardiovascular system to provide subject-specific estimates of parameters characterizing left ventricular function. In this model, the contractile state of the heart at each time point along the cardiac cycle is modeled using a time-varying elastance curve. Non-invasive data, including four-dimensional magnetic resonance imaging (4D Flow MRI) measurements, were acquired in nine subjects without a known heart disease at rest and during dobutamine stress. For each of the study subjects, we constructed two personalized models corresponding to the resting and the stress state. Results: Applying the modeling framework, we identified significant increases in the left ventricular contraction rate constant [from 1.5 +/- 0.3 to 2 +/- 0.5 (p = 0.038)] and relaxation constant [from 37.2 +/- 6.9 to 46.1 +/- 12 (p = 0.028)]. In addition, we found a significant decrease in the elastance diastolic time constant from 0.4 +/- 0.04 s to 0.3 +/- 0.03 s = 0.008). Conclusions: The integrated image-modeling approach allows the assessment of cardiovascular function given as model-based parameters. The agreement between the estimated parameter values and previously reported effects of dobutamine demonstrates the potential of the approach to assess advanced metrics of pathophysiology that are otherwise difficult to obtain non-invasively in clinical practice.

  • 24.
    Chan, Yung-Kuan
    et al.
    Natl Chung Hsing Univ, Taiwan.
    Chen, Yung-Fu
    Cent Taiwan Univ Sci and Technol, Taiwan.
    Pham, Tuan
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Chang, Weide
    Calif State Univ Sacramento, CA 95819 USA.
    Hsieh, Ming-Yuan
    Natl Taichung Univ Educ, Taiwan.
    Editorial Material: Artificial Intelligence in Medical Applications in JOURNAL OF HEALTHCARE ENGINEERING, vol , issue , pp2018In: Journal of Healthcare Engineering, ISSN 2040-2295, E-ISSN 2040-2309, article id 4827875Article in journal (Other academic)
    Abstract [en]

    n/a

  • 25.
    Cirillo, Marco Domenico
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Mirdell, Robin
    Linköping University, Department of Clinical and Experimental Medicine, Division of Surgery, Orthopedics and Oncology. Linköping University, Faculty of Medicine and Health Sciences.
    Sjöberg, Folke
    Linköping University, Department of Clinical and Experimental Medicine, Division of Surgery, Orthopedics and Oncology. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Department of Hand and Plastic Surgery.
    Pham, Tuan
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Tensor Decomposition for Colour Image Segmentation of Burn Wounds2019In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, article id 3291Article in journal (Refereed)
    Abstract [en]

    Research in burns has been a continuing demand over the past few decades, and important advancements are still needed to facilitate more effective patient stabilization and reduce mortality rate. Burn wound assessment, which is an important task for surgical management, largely depends on the accuracy of burn area and burn depth estimates. Automated quantification of these burn parameters plays an essential role for reducing these estimate errors conventionally carried out by clinicians. The task for automated burn area calculation is known as image segmentation. In this paper, a new segmentation method for burn wound images is proposed. The proposed methods utilizes a method of tensor decomposition of colour images, based on which effective texture features can be extracted for classification. Experimental results showed that the proposed method outperforms other methods not only in terms of segmentation accuracy but also computational speed.

  • 26.
    Cirillo, Marco Domenico
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Mirdell, Robin
    Linköping University, Department of Clinical and Experimental Medicine, Division of Surgery, Orthopedics and Oncology. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Department of Hand and Plastic Surgery.
    Sjöberg, Folke
    Linköping University, Department of Clinical and Experimental Medicine, Division of Surgery, Orthopedics and Oncology. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Department of Hand and Plastic Surgery.
    Pham, Tuan
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Time-Independent Prediction of Burn Depth using Deep Convolutional Neural Networks2019In: Journal of Burn Care & Research, ISSN 1559-047X, E-ISSN 1559-0488, Vol. 40, no 6, p. 857-863Article in journal (Refereed)
    Abstract [en]

    We present in this paper the application of deep convolutional neural networks, which are a state-of-the-art artificial intelligence (AI) approach in machine learning, for automated time-independent prediction of burn depth. Colour images of four types of burn depth injured in first few days, including normal skin and background, acquired by a TiVi camera were trained and tested with four pre-trained deep convolutional neural networks: VGG-16, GoogleNet, ResNet-50, and ResNet-101. In the end, the best 10-fold cross-validation results obtained from ResNet- 101 with an average, minimum, and maximum accuracy are 81.66%, 72.06% and 88.06%, respectively; and the average accuracy, sensitivity and specificity for the four different types of burn depth are 90.54%, 74.35% and 94.25%, respectively. The accuracy was compared to the clinical diagnosis obtained after the wound had healed. Hence, application of AI is very promising for prediction of burn depth and therefore can be a useful tool to help in guiding clinical decision and initial treatment of burn wounds.

    The full text will be freely available from 2020-06-11 08:35
  • 27.
    Cornet, Ronald
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Univ Amsterdam, Netherlands.
    Infrastructure and Capacity Building for Semantic Interoperability in Healthcare in the Netherlands2017In: BUILDING CAPACITY FOR HEALTH INFORMATICS IN THE FUTURE, IOS PRESS , 2017, Vol. 234, p. 70-74Conference paper (Refereed)
    Abstract [en]

    Over 15 years, a broad spectrum of activities was undertaken to realize a health IT infrastructure in the Netherlands. In this paper we reflect on the history, challenges, accomplishments, changes, and the way forward. It shows that the infrastructure depends on technical, legal, and semantic aspects, which are frequently reciprocally related. It also highlights the fact that the role of health professionals and of patients is increasingly considered as a crucial element.

  • 28.
    Cornet, Ronald
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Univ Amsterdam, Netherlands.
    Hill, Carly
    Univ Amsterdam, Netherlands.
    de Keizer, Nicolette
    Univ Amsterdam, Netherlands.
    Comparison of Three English-to-Dutch Machine Translations of SNOMED CT Procedures2017In: MEDINFO 2017: PRECISION HEALTHCARE THROUGH INFORMATICS, IOS PRESS , 2017, Vol. 245, p. 848-852Conference paper (Refereed)
    Abstract [en]

    Dutch interface terminologies are needed to use SNOMED CT in the Netherlands. Machine translation may support in their creation. The aim of our study is to compare different machine translations of procedures in SNOMED CT. Procedures were translated using Google Translate, Matecat, and Thot. Google Translate and Matecat are tools with large but general translation memories. The translation memory of Thot was trained and tuned with various configurations of a Dutch translation of parts of SNOMED CT, a medical dictionary and parts of the UMLS Metathesaurus. The configuration with the highest BLEU score, representing closeness to human translation, was selected. Similarity was determined between Thot translations and those by Google and Matecat. The validity of translations was assessed through random samples. Google and Matecat translated similarly in 85.4% of the cases and generally better than Thot. Whereas the quality of translations was considered acceptable, machine translations alone are yet insufficient.

  • 29.
    Cros, Olivier
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Department of Otolaryngology, Head & Neck Surgery, Aalborg University Hospital, Denmark.
    Gaihede, Michael
    Department of Otolaryngology, Head & Neck Surgery, Aalborg University Hospital, Denmark; Department of Clinical Medicine, Aalborg University, Denmark.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Surface and curve skeleton from a structure tensor analysis applied on mastoid air cells in human temporal bones2017In: IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 270-274Conference paper (Refereed)
    Abstract [en]

    The mastoid of human temporal bone contains numerous air cells connected to each others. In order to gain further knowledge about these air cells, a more compact representation is needed to obtain an estimate of the size distribution of these cells. Already existing skeletonization methods often fail in producing a faithful skeleton mostly due to noise hampering the binary representation of the data. This paper proposes a different approach by extracting geometrical information embedded in the Euclidean distance transform of a volume via a structure tensor analysis based on quadrature filters, from which a secondary structure tensor allows the extraction of surface skeleton along with a curve skeleton from its eigenvalues. Preliminary results obtained on a X-ray micro-CT scans of a human temporal bone show very promising results.

  • 30.
    Cubo, Ruben
    et al.
    Uppsala Univ, Sweden.
    Åström, Mattias
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Medvedev, Alexander
    Uppsala Univ, Sweden.
    Optimization-Based Contact Fault Alleviation in Deep Brain Stimulation Leads2018In: IEEE transactions on neural systems and rehabilitation engineering, ISSN 1534-4320, E-ISSN 1558-0210, Vol. 26, no 1, p. 69-76Article in journal (Refereed)
    Abstract [en]

    Deep brain stimulation (DBS) is a neurosurgical treatment in, e.g., Parkinsons Disease. Electrical stimulation in DBS is delivered to a certain target through electrodes implanted into the brain. Recent developments aiming at better stimulation target coverage and lesser side effects have led to an increase in the number of contacts in a DBS lead as well as higher hardware complexity. This paper proposes an optimization-based approach to alleviation of the fault impact on the resulting therapeutical effect in field steering DBS. Faulty contacts could be an issue given recent trends of increasing number of contacts in DBS leads. Hence, a fault detection/alleviation scheme, such as the one proposed in this paper, is necessary ensure resilience in the chronic stimulation. Two alternatives are considered and compared with the stimulation prior to the fault: one using higher amplitudes on the remaining contacts and another with alleviating contacts in the neighborhood of the faulty one. Satisfactory compensation for a faulty contact can be achieved in both ways. However, to designate alleviating contacts, a model-based optimization procedure is necessary. Results suggest that stimulating with more contacts yields configurations that are more robust to contact faults, though with reduced selectivity.

  • 31.
    De Biase, Alessia
    et al.
    Division of Statistics and Machine learning, Department of Computer and Information Science, Linkoping University, Linkoping, Sweden, ContextVision AB, Stockholm, Sweden .
    Burlutskiy, Nikolay
    ContextVision AB, Stockholm, Sweden .
    Pinchaud, Nicolas
    ContextVision AB, Stockholm, Sweden.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Deep Learning Data Augmentation Approach to Improve Cancer Segmentation Performance across Different Scanners2019Conference paper (Refereed)
  • 32.
    Durrieu, Lucía
    et al.
    IFIByNE, DFBMC, FCEN, UBA, Buenos Aires, Argentine.
    Johansson, Rikard
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Bush, Alan
    IFIByNE, DFBMC, FCEN, UBA, Buenos Aires, Argentine.
    Janzén, David
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Gollvik, Martin
    Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering.
    Cedersund, Gunnar
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering.
    Colman-Lerner, Alejandro
    IFIByNE, DFBMC, FCEN, UBA, Buenos Aires, Argentine.
    Quantification of nuclear transport in single cells2014Other (Other academic)
    Abstract [en]

    Regulation of nuclear transport is a key cellular function involved in many central processes, such as gene expression regulation and signal transduction. Rates of protein movement between cellular compartments can be measured by FRAP. However, no standard and reliable methods to calculate transport rates exist. Here we introduce a method to extract import and export rates, suitable for noisy single cell data. This method consists of microscope procedures, routines for data processing, an ODE model to fit to the data, and algorithms for parameter optimization and error estimation. Using this method, we successfully measured import and export rates in individual yeast. For YFP, average transport rates were 0.15 sec-1. We estimated confidence intervals for these parameters through likelihood profile analysis. We found large cell-to-cell variation (CV = 0.79) in these rates, suggesting a hitherto unknown source of cellular heterogeneity. Given the passive nature of YFP diffusion, we attribute this variation to large differences among cells in the number or quality of nuclear pores. Owing to its broad applicability and sensitivity, this method will allow deeper mechanistic insight into nuclear transport processes and into the largely unstudied cell-to-cell variation in kinetic rates.

  • 33.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Repliker. ”Öppen vetenskap behöver inte kosta en enda krona”2016In: Dagens Nyheter, ISSN 1101-2447Article in journal (Other (popular science, discussion, etc.))
  • 34.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Öppen vetenskap behöver inte kosta en krona2017In: Svenska Dagbladet, ISSN 1101-2412Article in journal (Other (popular science, discussion, etc.))
  • 35.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Reply to Chen et al.: Parametric methods for cluster inference perform worse for two‐sided t‐tests2019In: Human Brain Mapping, ISSN 1065-9471, E-ISSN 1097-0193, Vol. 40, no 5, p. 1689-1691Article in journal (Other (popular science, discussion, etc.))
    Abstract [en]

    One‐sided t‐tests are commonly used in the neuroimaging field, but two‐sided tests should be the default unless a researcher has a strong reason for using a one‐sided test. Here we extend our previous work on cluster false positive rates, which used one‐sided tests, to two‐sided tests. Briefly, we found that parametric methods perform worse for two‐sided t‐tests, and that nonparametric methods perform equally well for one‐sided and two‐sided tests.

  • 36.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Nichols, Thomas E
    Big Data Institute, University of Oxford, Oxford, United Kingdom, Department of Statistics, University of Warwick, Coventry, United KingdomWellcome Trust Centre for Integrative Neuroimaging (WIN-FMRIB), University of Oxford, Oxford, United Kingdom, .
    Cluster failure revisited: Impact of first level design and physiological noise on cluster false positive rates2019In: Human Brain Mapping, ISSN 1065-9471, E-ISSN 1097-0193, Vol. 40, no 7, p. 2017-2032Article in journal (Refereed)
    Abstract [en]

    Methodological research rarely generates a broad interest, yet our work on the validity of cluster inference methods for functional magnetic resonance imaging (fMRI) created intense discussion on both the minutia of our approach and its implications for the discipline. In the present work, we take on various critiques of our work and further explore the limitations of our original work. We address issues about the particular event‐related designs we used, considering multiple event types and randomization of events between subjects. We consider the lack of validity found with one‐sample permutation (sign flipping) tests, investigating a number of approaches to improve the false positive control of this widely used procedure. We found that the combination of a two‐sided test and cleaning the data using ICA FIX resulted in nominal false positive rates for all data sets, meaning that data cleaning is not only important for resting state fMRI, but also for task fMRI. Finally, we discuss the implications of our work on the fMRI literature as a whole, estimating that at least 10% of the fMRI studies have used the most problematic cluster inference method (p = .01 cluster defining threshold), and how individual studies can be interpreted in light of our findings. These additional results underscore our original conclusions, on the importance of data sharing and thorough evaluation of statistical methods on realistic null data.

  • 37.
    Eklund, Anders
    et al.
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering. Linköping University, Faculty of Arts and Sciences.
    Lindqvist, Martin A
    Department of Biostatistics, Johns Hopkins University, Baltimore, USA.
    Villani, Mattias
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    A Bayesian Heteroscedastic GLM with Application to fMRI Data with Motion Spikes2017In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 155, p. 354-369Article in journal (Refereed)
    Abstract [en]

    We propose a voxel-wise general linear model with autoregressive noise and heteroscedastic noise innovations (GLMH) for analyzing functional magnetic resonance imaging (fMRI) data. The model is analyzed from a Bayesian perspective and has the benefit of automatically down-weighting time points close to motion spikes in a data-driven manner. We develop a highly efficient Markov Chain Monte Carlo (MCMC) algorithm that allows for Bayesian variable selection among the regressors to model both the mean (i.e., the design matrix) and variance. This makes it possible to include a broad range of explanatory variables in both the mean and variance (e.g., time trends, activation stimuli, head motion parameters and their temporal derivatives), and to compute the posterior probability of inclusion from the MCMC output. Variable selection is also applied to the lags in the autoregressive noise process, making it possible to infer the lag order from the data simultaneously with all other model parameters. We use both simulated data and real fMRI data from OpenfMRI to illustrate the importance of proper modeling of heteroscedasticity in fMRI data analysis. Our results show that the GLMH tends to detect more brain activity, compared to its homoscedastic counterpart, by allowing the variance to change over time depending on the degree of head motion.

  • 38.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Nichols, Thomas
    University of Warwick, England.
    How open science revealed false positives in brain imaging2017In: Significance, ISSN 1740-9705, E-ISSN 1740-9713Article in journal (Other (popular science, discussion, etc.))
    Abstract [en]

    A team set out to validate software used in fMRI analysis, but ended up invalidating one of neuroscience's most common testing procedures.

  • 39.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Nichols, Thomas
    Department of Statistics, University of Warwick, UK; WMG, University of Warwick, UK.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Reply to BROWN AND BEHRMANN, COX ET AL., AND KESSLER ET AL.: Data and code sharing is the way forward for fMRI2017In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, p. 1-2Article in journal (Other academic)
    Abstract [en]

    We are glad that our paper (1) has generated intense discussions in the fMRI field (2⇓–4), on how to analyze fMRI data, and how to correct for multiple comparisons. The goal of the paper was not to disparage any specific fMRI software, but to point out that parametric statistical methods are based on a number of assumptions that are not always valid for fMRI data, and that nonparametric statistical methods (5) are a good alternative. Through AFNI’s introduction of nonparametric statistics in the function 3dttest++ (3, 6), the three most common fMRI softwares now all support nonparametric group inference [SPM through the toolbox SnPM (www2.warwick.ac.uk/fac/sci/statistics/staff/academic-research/nichols/software/snpm), and FSL through the function randomise].

    Cox et al. (3) correctly point out that the bug in the AFNI function 3dClustSim only had a minor impact on the false-positive rate (FPR). This was also covered in our original paper (1): “We note that FWE [familywise error] rates are lower with the bug-fixed 3dClustSim function. As an example, the updated function reduces the degree of false …

  • 40.
    Ewerlöf, Maria
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Larsson, Marcus
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Salerud, Göran
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Spatial and temporal skin blood volume and saturation estimation using a multispectral snapshot imaging camera2017In: IMAGING, MANIPULATION, AND ANALYSIS OF BIOMOLECULES, CELLS, AND TISSUES XV, SPIE-INT SOC OPTICAL ENGINEERING , 2017, Vol. 10068, article id UNSP 1006814Conference paper (Refereed)
    Abstract [en]

    Hyperspectral imaging (HSI) can estimate the spatial distribution of skin blood oxygenation, using visible to near-infrared light. HSI oximeters often use a liquid-crystal tunable filter, an acousto-optic tunable filter or mechanically adjustable filter wheels, which has too long response/switching times to monitor tissue hemodynamics. This work aims to evaluate a multispectral snapshot imaging system to estimate skin blood volume and oxygen saturation with high temporal and spatial resolution. We use a snapshot imager, the xiSpec camera (MQ022HG-IM-SM4X4-VIS, XIMEA (R)), having 16 wavelength-specific Fabry-Perot filters overlaid on the custom CMOS-chip. The spectral distribution of the bands is however substantially overlapping, which needs to be taken into account for an accurate analysis. An inverse Monte Carlo analysis is performed using a two-layered skin tissue model, defined by epidermal thickness, haemoglobin concentration and oxygen saturation, melanin concentration and spectrally dependent reduced-scattering coefficient, all parameters relevant for human skin. The analysis takes into account the spectral detector response of the xiSpec camera. At each spatial location in the field-of-view, we compare the simulated output to the detected diffusively backscattered spectra to find the best fit. The imager is evaluated for spatial and temporal variations during arterial and venous occlusion protocols applied to the forearm. Estimated blood volume changes and oxygenation maps at 512x272 pixels show values that are comparable to reference measurements performed in contact with the skin tissue. We conclude that the snapshot xiSpec camera, paired with an inverse Monte Carlo algorithm, permits us to use this sensor for spatial and temporal measurement of varying physiological parameters, such as skin tissue blood volume and oxygenation.

  • 41.
    Farzam, Parisa
    et al.
    Barcelona Institute Science and Technology, Spain; Harvard Medical Sch, MA 02129 USA.
    Johansson, Johannes
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Barcelona Institute Science and Technology, Spain.
    Mireles, Miguel
    Barcelona Institute Science and Technology, Spain.
    Jimenez-Valerio, Gabriela
    Bellvitge Biomed Research Institute IDIBELL, Spain.
    Martinez-Lozano, Mar
    Bellvitge Biomed Research Institute IDIBELL, Spain.
    Choe, Regine
    University of Rochester, NY 14627 USA; University of Rochester, NY 14627 USA.
    Casanovas, Oriol
    Bellvitge Biomed Research Institute IDIBELL, Spain.
    Durduran, Turgut
    Barcelona Institute Science and Technology, Spain; ICREA, Spain.
    Pre-clinical longitudinal monitoring of hemodynamic response to anti-vascular chemotherapy by hybrid diffuse optics2017In: Biomedical Optics Express, ISSN 2156-7085, E-ISSN 2156-7085, Vol. 8, no 5, p. 2563-2582Article in journal (Refereed)
    Abstract [en]

    The longitudinal effect of an anti-vascular endothelial growth factor receptor 2 (VEGFR-2) antibody (DC 101) therapy on a xenografted renal cell carcinoma (RCC) mouse model was monitored using hybrid diffuse optics. Two groups of immunosuppressed male nude mice (seven treated, seven controls) were measured. Tumor microvascular blood flow, total hemoglobin concentration and blood oxygenation were investigated as potential biomarkers for the monitoring of the effect of therapy twice a week and were related to the final treatment outcome. These hemodynamic biomarkers have shown a clear differentiation between two groups by day four. Moreover, we have observed that pre-treatment values and early changes in hemodynamics are highly correlated with the therapeutic outcome demonstrating the potential of diffuse optics to predict the therapy response at an early time point. (C) 2017 Optical Society of America

  • 42.
    Felter, Pierre-Loïc
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering.
    Creating hemodynamic atlas of aorta2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Turbulent blood flow is involved in the pathogenesis of several cardiovascular diseases. While it is known that turbulence is present in patients with obstructive disease in the major vessels, the magnitude and impact of turbulence in the normal heart and aorta is still relatively unexplored. Besides, existing analysis method of the blood flow is a labour intensive process and requires excessive amount of time.

    A method to automatically create hemodynamic atlases has been developed, using 4D Flow magnetic resonance imaging (MRI), a powerful tool to measure blood flow characteristics. The resulting atlases show the expected blood flow characteristics in the aorta for a group of similar subjects.

    Application of the method in healthy young and healthy old has shown significant differences in kinetic energy and turbulent kinetic energy in the aortic flow. 

  • 43.
    Forsgren, Mikael
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Karlsson, Markus
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlström, Nils
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Norén, Bengt
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Romu, Thobias
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ignatova, Simone
    Linköping University, Department of Clinical and Experimental Medicine, Divison of Neurobiology. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Clinical pathology.
    Ekstedt, Mattias
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Gastroentorology.
    Kechagias, Stergios
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Gastroentorology.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Model-inferred mechanisms of liver function from magnetic resonance imaging data: Validation and variation across a clinically relevant cohort2019In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 15, no 6, article id e1007157Article in journal (Refereed)
    Abstract [en]

    Estimation of liver function is important to monitor progression of chronic liver disease (CLD). A promising method is magnetic resonance imaging (MRI) combined with gadoxetate, a liver-specific contrast agent. For this method, we have previously developed a model for an average healthy human. Herein, we extended this model, by combining it with a patient-specific non-linear mixed-effects modeling framework. We validated the model by recruiting 100 patients with CLD of varying severity and etiologies. The model explained all MRI data and adequately predicted both timepoints saved for validation and gadoxetate concentrations in both plasma and biopsies. The validated model provides a new and deeper look into how the mechanisms of liver function vary across a wide variety of liver diseases. The basic mechanisms remain the same, but increasing fibrosis reduces uptake and increases excretion of gadoxetate. These mechanisms are shared across many liver functions and can now be estimated from standard clinical images.

    Author summary

    Being able to accurately and reliably estimate liver function is important when monitoring the progression of patients with liver disease, as well as when identifying drug-induced liver injury during drug development. A promising method for quantifying liver function is to use magnetic resonance imaging combined with gadoxetate. Gadoxetate is a liver-specific contrast agent, which is taken up by the hepatocytes and excreted into the bile. We have previously developed a mechanistic model for gadoxetate dynamics using averaged data from healthy volunteers. In this work, we extended our model with a non-linear mixed-effects modeling framework to give patient-specific estimates of the gadoxetate transport-rates. We validated the model by recruiting 100 patients with liver disease, covering a range of severity and etiologies. All patients underwent an MRI-examination and provided both blood and liver biopsies. Our validated model provides a new and deeper look into how the mechanisms of liver function varies across a wide variety of liver diseases. The basic mechanisms remain the same, but increasing fibrosis reduces uptake and increases excretion of gadoxetate.

  • 44.
    Fredriksson, Ingemar
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Perimed AB, Sweden.
    Hultman, Martin
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Strömberg, Tomas
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Larsson, Marcus
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Machine learning in multiexposure laser speckle contrast imaging can replace conventional laser Doppler flowmetry2019In: Journal of Biomedical Optics, ISSN 1083-3668, E-ISSN 1560-2281, Vol. 24, no 1, article id 016001Article in journal (Refereed)
    Abstract [en]

    Laser speckle contrast imaging (LSCI) enables video rate imaging of blood flow. However, its relation to tissue blood perfusion is nonlinear and depends strongly on exposure time. By contrast, the perfusion estimate from the slower laser Doppler flowmetry (LDF) technique has a relationship to blood perfusion that is better understood. Multiexposure LSCI (MELSCI) enables a perfusion estimate closer to the actual perfusion than that using a single exposure time. We present and evaluate a method that utilizes contrasts from seven exposure times between 1 and 64 ms to calculate a perfusion estimate that resembles the perfusion estimate from LDF. The method is based on artificial neural networks (ANN) for fast and accurate processing of MELSCI contrasts to perfusion. The networks are trained using modeling of Doppler histograms and speckle contrasts from tissue models. The importance of accounting for noise is demonstrated. Results show that by using ANN, MELSCI data can be processed to LDF perfusion with high accuracy, with a correlation coefficient R = 1.000 for noise-free data, R = 0.993 when a moderate degree of noise is present, and R = 0.995 for in vivo data from an occlusion-release experiment. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

  • 45.
    Fredriksson, Ingemar
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Perimed AB, Sweden.
    Larsson, Marcus
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Vessel packaging effect in laser speckle contrast imaging and laser Doppler imaging2017In: Journal of Biomedical Optics, ISSN 1083-3668, E-ISSN 1560-2281, Vol. 22, no 10, article id 106005Article in journal (Refereed)
    Abstract [en]

    Laser speckle-based techniques are frequently used to assess microcirculatory blood flow. Perfusion estimates are calculated either by analyzing the speckle fluctuations over time as in laser Doppler flowmetry (LDF), or by analyzing the speckle contrast as in laser speckle contrast imaging (LSCI). The perfusion estimates depend on the amount of blood and its speed distribution. However, the perfusion estimates are commonly given in arbitrary units as they are nonlinear and depend on the magnitude and the spatial distribution of the optical properties in the tissue under investigation. We describe how the spatial confinement of blood to vessels, called the vessel packaging effect, can be modeled in LDF and LSCI, which affect the Doppler power spectra and speckle contrast, and the underlying bio-optical mechanisms for these effects. As an example, the perfusion estimate is reduced by 25% for LDF and often more than 50% for LSCI when blood is located in vessels with an average diameter of 40 aem, instead of being homogeneously distributed within the tissue. This significant effect can be compensated for only with knowledge of the average diameter of the vessels in the tissue. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.

  • 46.
    Fredriksson, Ingemar
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Perimed AB, Sweden.
    Saager, Rolf B.
    University of Calif Irvine, CA USA; University of Calif Irvine, CA USA.
    Durkin, Anthony J.
    University of Calif Irvine, CA USA.
    Strömberg, Tomas
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. University of Calif Irvine, CA USA; University of Calif Irvine, CA USA.
    Evaluation of a multi-layer diffuse reflectance spectroscopy system using optical phantoms2017In: DESIGN AND QUALITY FOR BIOMEDICAL TECHNOLOGIES X, SPIE-INT SOC OPTICAL ENGINEERING , 2017, Vol. 10056, article id UNSP 100560GConference paper (Refereed)
    Abstract [en]

    A fiber probe-based device for assessing microcirculatory parameters, especially red blood cell (RBC) tissue fraction, their oxygen saturation and speed resolved perfusion, has been evaluated using state-of-the-art multi-layer tissue simulating phantoms. The device comprises both diffuse reflectance spectroscopy (DRS) at two source-detector separations (0.4 and 1.2 mm) and laser Doppler flowmetry (LDF) and use an inverse Monte Carlo method for identifying the parameters of a multi-layered tissue model. First, model parameters affecting scattering, absorption and geometrical parameters are fitted to measured DRS spectra, then speed parameters are fitted to LDF spectra. In this paper, the accuracy of the spectral parameters is evaluated. The measured spectral shapes at the two source-detector separations were in good agreement with forward calculated spectral shapes. In conclusion, the multi-layer skin model based on spectral features of the included chromophores, can reliably estimate the tissue fraction of RBC, its oxygen saturation and the reduced scattering coefficient spectrum of the tissue. Furthermore, it was concluded that some freedom in the relative intensity difference between the two DRS channels is necessary in order to compensate for non-modeled surface structure effects.

  • 47.
    Fredriksson, Ingemar
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Perimed AB, Sweden.
    Saager, Rolf B.
    University of Calif Irvine, CA 92715 USA.
    Durkin, Anthony J.
    University of Calif Irvine, CA USA.
    Strömberg, Tomas
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. University of Calif Irvine, CA 92715 USA.
    Evaluation of a pointwise microcirculation assessment method using liquid and multilayered tissue simulating phantoms2017In: Journal of Biomedical Optics, ISSN 1083-3668, E-ISSN 1560-2281, Vol. 22, no 11, article id 115004Article in journal (Refereed)
    Abstract [en]

    A fiber-optic probe-based instrument, designed for assessment of parameters related to microcirculation, red blood cell tissue fraction (f(RBC)), oxygen saturation (S-O2), and speed resolved perfusion, has been evaluated using state-of-the-art tissue phantoms. The probe integrates diffuse reflectance spectroscopy (DRS) at two source-detector separations and laser Doppler flowmetry, using an inverse Monte Carlo method for identifying the parameters of a multilayered tissue model. Here, we characterize the accuracy of the DRS aspect of the instrument using (1) liquid blood phantoms containing yeast and (2) epidermis-dermis mimicking solid-layered phantoms fabricated from polydimethylsiloxane, titanium oxide, hemoglobin, and coffee. The rootmean-square (RMS) deviations for f(RBC) for the two liquid phantoms were 11% and 5.3%, respectively, and 11% for the solid phantoms with highest hemoglobin signatures. The RMS deviation for SO2 was 5.2% and 2.9%, respectively, for the liquid phantoms, and 2.9% for the solid phantoms. RMS deviation for the reduced scattering coefficient (mus), for the solid phantoms was 15% (475 to 850 nm). For the liquid phantoms, the RMS deviation in average vessel diameter (D) was 1 mu m. In conclusion, the skin microcirculation parameters fRBC and SO2, as well as, mu(s) and D are estimated with reasonable accuracy. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.

  • 48.
    Gesicho, Milka B.
    et al.
    Department of Information Science and Media Studies, University of Bergen, Norway.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Were, Martin C.
    Institute of Biomedical Informatics, Moi University, Kenya.
    Critical Issues in Evaluating National-Level Health Data Warehouses in LMICs: Kenya Case Study2017In: Informatics Empowers Healthcare Transformation / [ed] Househ M.S.,Mantas J.,Hasman A.,Gallos P., 2017, Vol. 238, p. 201-204Conference paper (Refereed)
    Abstract [en]

    Low-Middle-Income-Countries (LMICs) are beginning to adopt national health data warehousing (NHDWs) for making strategic decisions and for improving health outcomes. Given the numerous challenges likely to be faced in establishment of NHDWs by LMICs, it is prudent that evaluations are done in relation to the data warehouses (DWs), in order to identify and mitigate critical issues that arise. When critic issues are not identified, DWs are prone to suboptimal implementation with compromised outcomes. Despite the fact that several publications exist on evaluating DWs, evaluations specific to health data warehouses are scanty, with almost none evaluating NHDWs more so in LMICs. This paper uses a systematic approach guided by an evaluation framework to identify critical issues to be considered in evaluating Kenyas NHDW.

  • 49.
    Gesicho, Milka
    et al.
    Univ Bergen, Norway.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Univ Bergen, Norway.
    Task-Based Approach Recommendations to Enhance Data Visualization in the Kenya National Health Data Warehouse2019In: WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING 2018, VOL 1, SPRINGER , 2019, Vol. 68, no 1, p. 467-470Conference paper (Refereed)
    Abstract [en]

    The health sector still lags behind in development of data visualization tools due to the complex nature of health data. Furthermore, due to the volume, velocity and veracity of health data consolidated from various sources, re-presenting them in a way that promotes decision-making while supporting various aspects of human interaction becomes even more challenging. With the plethora of research on improving visualization of integrated health data, focus is shifting from simple charts to novel ways of data re-presentation. Literature also suggests the need for an in-depth exploration on aligning visualizations to tasks, context, and appropriate cognition aspects. We conducted a field study at the Kenya National Health Data Warehouse (KNHDW) in the month of July 2017 to identify the techniques and practices used to visualize data. Two salient tasks performed in the KNHDW were identified in order to explore possibilities of visualizing the data. We then adopted a task-based approach in developing recommendations based on categorical data. These recommendations include (1) use of visualization approaches that promote proper space utilization, and (2) use of leverage points that influence aspects of human cognition process. In addition, the proposed visualizations enable potential users to get a new experience with the data and explore possibilities for visualization. Nevertheless, these recommendations are by no means exhaustive but aim at encouraging best practice in health data visualization in the KNHDW.

  • 50.
    Gharehbaghi, A.
    et al.
    Malardalen Univ, Sweden.
    Sepehri, Amir A.
    CAPIS Biomed Res and Dept Ctr, Belgium.
    Linden, Maria
    Malardalen Univ, Sweden.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    A Hybrid Machine Learning Method for Detecting Cardiac Ejection Murmurs2018In: EMBEC and NBC 2017, SPRINGER-VERLAG SINGAPORE PTE LTD , 2018, Vol. 65, p. 787-790Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel method for detecting cardiac ejection murmurs from other pathological and physiological heart murmurs in children. The proposed method combines a hybrid model and a time growing neural network for an improved detection even in mild condition. Children with aortic stenosis and pulmonary stenosis comprised the patient category against the reference category containing mitral regurgitation, ventricular septal defect, innocent murmur and normal (no murmur) conditions. In total, 120 referrals to a children University hospital participated to the study after giving their informed consent. Confidence interval of the accuracy, sensitivity and specificity is estimated to be 87.2%-88.8%, 83.4%-86.9% and 88.3%-90.0%, respectively.

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