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  • 1.
    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)
  • 2.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Computational Medical Image Analysis: With a Focus on Real-Time fMRI and Non-Parametric Statistics2012Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Functional magnetic resonance imaging (fMRI) is a prime example of multi-disciplinary research. Without the beautiful physics of MRI, there wouldnot be any images to look at in the first place. To obtain images of goodquality, it is necessary to fully understand the concepts of the frequencydomain. The analysis of fMRI data requires understanding of signal pro-cessing, statistics and knowledge about the anatomy and function of thehuman brain. The resulting brain activity maps are used by physicians,neurologists, psychologists and behaviourists, in order to plan surgery andto increase their understanding of how the brain works.

    This thesis presents methods for real-time fMRI and non-parametric fMRIanalysis. Real-time fMRI places high demands on the signal processing,as all the calculations have to be made in real-time in complex situations.Real-time fMRI can, for example, be used for interactive brain mapping.Another possibility is to change the stimulus that is given to the subject, inreal-time, such that the brain and the computer can work together to solvea given task, yielding a brain computer interface (BCI). Non-parametricfMRI analysis, for example, concerns the problem of calculating signifi-cance thresholds and p-values for test statistics without a parametric nulldistribution.

    Two BCIs are presented in this thesis. In the first BCI, the subject wasable to balance a virtual inverted pendulum by thinking of activating theleft or right hand or resting. In the second BCI, the subject in the MRscanner was able to communicate with a person outside the MR scanner,through a virtual keyboard.

    A graphics processing unit (GPU) implementation of a random permuta-tion test for single subject fMRI analysis is also presented. The randompermutation test is used to calculate significance thresholds and p-values forfMRI analysis by canonical correlation analysis (CCA), and to investigatethe correctness of standard parametric approaches. The random permuta-tion test was verified by using 10 000 noise datasets and 1484 resting statefMRI datasets. The random permutation test is also used for a non-localCCA approach to fMRI analysis.

  • 3.
    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.))
  • 4.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Signal Processing for Robust and Real-Time fMRI With Application to Brain Computer Interfaces2010Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    It is hard to find another research field than functional magnetic resonance imaging (fMRI) that combines so many different areas of research. Without the beautiful physics of MRI we would not have any images to look at in the first place. To get images with good quality it is necessary to fully understand the concepts of the frequency domain. The analysis of fMRI data requires understanding of signal processing and statistics and also knowledge about the anatomy and function of the human brain. The resulting brain activity maps are used by physicians and neurologists in order to plan surgery and to increase their understanding of how the brain works.

    This thesis presents methods for signal processing of fMRI data in real-time situations. Real-time fMRI puts higher demands on the signal processing, than conventional fMRI, since all the calculations have to be made in realtime and in more complex situations. The result from the real-time fMRI analysis can for example be used to look at the subjects brain activity in real-time, for interactive planning of surgery or understanding of brain functions. Another possibility is to use the result in order to change the stimulus that is given to the subject, such that the brain and the computer can work together to solve a given task. These kind of setups are often called brain computer interfaces (BCI).

    Two BCI are presented in this thesis. In the first BCI the subject was able to balance a virtual inverted pendulum by thinking of activating the left or right hand or resting. In the second BCI the subject in the MR scanner was able to communicate with a person outside the MR scanner, through a communication interface.

    Since head motion is common during fMRI experiments it is necessary to apply image registration to align the collected volumes. To do image registration in real-time can be a challenging task, therefore how to implement a volume registration algorithm on a graphics card is presented. The power of modern graphic cards can also be used to save time in the daily clinical work, an example of this is also given in the thesis.

    Finally a method for calculating and incorporating a structural based certainty in the analysis of the fMRI data is proposed. The results show that the structural certainty helps to remove false activity that can occur due to head motion, especially at the edge of the brain.

  • 5.
    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.))
  • 6.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Josephson, Camilla
    Linköping University, Department of Management and Engineering, Economics. Linköping University, Faculty of Arts and Sciences.
    Johannesson, Magnus
    Stockholm School of Economics.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Does Parametric fMRI Analysis with SPM Yield Valid Results? - An Empirical Study of 1484 Rest Datasets2012In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 61, no 3, p. 565-578Article in journal (Refereed)
    Abstract [en]

    The validity of parametric functional magnetic resonance imaging (fMRI) analysis has only been reported for simulated data.Recent advances in computer science and data sharing make it possible to analyze large amounts of real fMRI data. In this study,1484 rest datasets have been analyzed in SPM8, to estimate true familywise error rates. For a familywise significance threshold of5%, significant activity was found in 1% - 70% of the 1484 rest datasets, depending on repetition time, paradigm and parametersettings. This means that parametric significance thresholds in SPM both can be conservative or very liberal. The main reason forthe high familywise error rates seems to be that the global AR(1) auto correlation correction in SPM fails to model the spectra ofthe residuals, especially for short repetition times. The findings that are reported in this study cannot be generalized to parametricfMRI analysis in general, other software packages may give different results. By using the computational power of the graphicsprocessing unit (GPU), the 1484 rest datasets were also analyzed with a random permutation test. Significant activity was thenfound in 1% - 19% of the datasets. These findings speak to the need for a better model of temporal correlations in fMRI timeseries.

  • 7.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    4D Medical Image Processing with CUDA2012Conference paper (Other academic)
    Abstract [en]

    Learn how to do 4D image processing with CUDA, especially for medical imaging applications. In this session we will give a couple of examples of how 4D image processing can take advantage of the computational power of the GPU. We will present how to use the GPU for functional magnetic resonance imaging (fMRI) analysis and true 4D image denoising. Most of our examples use the GPU both to speedup the analysis and to visualize the results.

  • 8.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    A Functional Connectivity Inspired Approach to Non-Local fMRI Analysis2012In: Proceedings of the 19th IEEE International Conference on Image Processing (ICIP), 2012, IEEE conference proceedings, 2012, p. 1245-1248Conference paper (Other academic)
    Abstract [en]

    We propose non-local analysis of functional magnetic resonanceimaging (fMRI) data in order to detect more brain activity.Our non-local approach combines the ideas of regularfMRI analysis with those of functional connectivity analysis,and was inspired by the non-local means algorithm thatcommonly is used for image denoising. We extend canonicalcorrelation analysis (CCA) based fMRI analysis to handlemore than one activity area, such that information fromdifferent parts of the brain can be combined. Our non-localapproach is compared to fMRI analysis by the general linearmodel (GLM) and local CCA, by using simulated as well asreal data.

  • 9.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Fast Random Permutation Tests Enable Objective Evaluation of Methods for Single Subject fMRI Analysis2011In: International Journal of Biomedical Imaging, ISSN 1687-4188, E-ISSN 1687-4196Article in journal (Refereed)
    Abstract [en]

    Parametric statistical methods, such as Z-, t-, and F-values are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks. First, it is assumed that the observed data are Gaussian distributed and independent; assumptions that generally are not valid for fMRI data. Second, the statistical test distribution can be derived theoretically only for very simple linear detection statistics. With non-parametric statistical methods, the two limitations described above can be overcome. The major drawback of non-parametric methods is the computational burden with processing times ranging from hours to days, which so far have made them impractical for routine use in single subject fMRI analysis. In this work, it is shown how the computational power of cost-efficient Graphics Processing Units (GPUs) can be used to speed up random permutation tests. A test with 10 000 permutations takes less than a minute, making statistical analysis of advanced detection methods in fMRI practically feasible. To exemplify the permutation based approach, brain activity maps generated by the General Linear Model (GLM) and Canonical Correlation Analysis (CCA) are compared at the same significance level. During the development of the routines and writing of the paper, 3-4 years of processing time has been saved by using the GPU.

  • 10.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    fMRI Analysis on the GPU - Possibilities and Challenges2012In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 105, no 2, p. 145-161Article in journal (Refereed)
    Abstract [en]

    Functional magnetic resonance imaging (fMRI) makes it possible to non-invasively measure brain activity with high spatial resolution.There are however a number of issues that have to be addressed. One is the large amount of spatio-temporal data that needsto be processed. In addition to the statistical analysis itself, several preprocessing steps, such as slice timing correction and motioncompensation, are normally applied. The high computational power of modern graphic cards has already successfully been used forMRI and fMRI. Going beyond the first published demonstration of GPU-based analysis of fMRI data, all the preprocessing stepsand two statistical approaches, the general linear model (GLM) and canonical correlation analysis (CCA), have been implementedon a GPU. For an fMRI dataset of typical size (80 volumes with 64 x 64 x 22 voxels), all the preprocessing takes about 0.5 s on theGPU, compared to 5 s with an optimized CPU implementation and 120 s with the commonly used statistical parametric mapping(SPM) software. A random permutation test with 10 000 permutations, with smoothing in each permutation, takes about 50 s ifthree GPUs are used, compared to 0.5 - 2.5 h with an optimized CPU implementation. The presented work will save time forresearchers and clinicians in their daily work and enables the use of more advanced analysis, such as non-parametric statistics, bothfor conventional fMRI and for real-time fMRI.

  • 11.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Improving CCA based fMRI Analysis by Covariance Pooling - Using the GPU for Statistical Inference2011Conference paper (Other academic)
    Abstract [en]

    Canonical correlation analysis (CCA) is a statistical methodthat can be preferable to the general linear model (GLM) for analysisof functional magnetic resonance imaging (fMRI) data. There are,however, two problems with CCA based fMRI analysis. First, it is notfeasible to use a parametric approach to calculate an activity thresholdfor a certain signi cance level. Second, two covariance matrices need tobe estimated in each voxel, from a rather small number of time samples.We recently solved the rst problem by doing random permutation testson the graphics processing unit (GPU), such that the null distribution ofany maximum test statistics can be estimated in the order of minutes. Inthis paper we consider the second problem. We extend the idea of variancepooling, that previously has been used for the GLM, to covariancepooling to improve the estimates of the covariance matrices. Our GPUimplementation of random permutation tests is used to calculate signicance thresholds, which are needed to compare the di erent activitymaps in an objective way. The covariance pooling results in more robustestimates of the covariance matrices. The number of signi cantly activevoxels that are detected (thresholded at p = 0.05, corrected for multiplecomparisons) is increased with 40 - 120% (if 8 mm smoothing is appliedto the covariance estimates). Too much covariance pooling can howeverresult in a loss of small activity clusters, 7-10 mm of smoothing givesthe best results. The calculations that were made in order to generatethe results in this paper would have taken a total of about 65 days witha Matlab implementation and about 10 days with a multithreaded Cimplementation, with our multi-GPU implementation they took about 2hours. By using fast random permutation tests, suggested improvementsof existing methods for fMRI analysis can be evaluated in an objective way.

  • 12.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology.
    On Structural Based Certainty for Robust fMRI AnalysisManuscript (preprint) (Other academic)
    Abstract [en]

    We present a method for obtaining and using a structural based certainty for robust functional magnetic resonance imaging (fMRI) analysis. In the area of fMRI it is common to see brain activity maps with activity at the edge of the brain. It is however a known fact that activity close to the edge of the brain can be due to head movement, since the voxels close to the edge will have a higher variance if they switch between being outside and inside the brain. To some extent this can be remedied by aligning each volume to a reference volume, by the means of volume registration. However, the problem with fMRI volumes is that the slices in the volume normally are taken at different timepoints, and motion between the slices can occur. We calculate a structural based certainty for each voxel, from a high resolution T1-weighted volume, and incorporate this certainty into the statistical analysis of the fMRI data. We show that our certainty approach removes a lot of false activity, both on simulated data and on real data.

  • 13.
    Eklund, Anders
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Phase Based Volume Registration Using CUDA2010In: Acoustics Speech and Signal Processing (ICASSP), 2010, IEEE , 2010, p. 658-661Conference paper (Refereed)
    Abstract [en]

    We present a method for fast phase based registration of volume data for medical applications. As the number of different modalities within medical imaging increases, it becomes more and more important with registration that works for a mixture of modalities. For these applications the phase based registration approach has proven to be superior. Today there seem to be two kinds of groups that work with medical image registration, one that works with refining of the registration algorithms and one that works with implementation of more simple algorithms on graphic cards for speeding up the algorithms. We put the work from these groups together and get the best from both worlds. We achieve a speedup of 10-30 compared to our CPU implementation, which makes fast phase based registration possible for large medical volumes.

  • 14.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    True 4D Image Denoising on the GPU2011In: International Journal of Biomedical Imaging, ISSN 1687-4188, E-ISSN 1687-4196, Vol. 2011Article in journal (Refereed)
    Abstract [en]

    The use of image denoising techniques is an important part of many medical imaging applications. One common application isto improve the image quality of low-dose, i.e. noisy, computed tomography (CT) data. The medical imaging domain has seen atremendous development during the last decades. It is now possible to collect time resolved volumes, i.e. 4D data, with a number ofmodalities (e.g. ultrasound (US), CT, magnetic resonance imaging (MRI)). While 3D image denoising previously has been appliedto several volumes independently, there has not been much work done on true 4D image denoising, where the algorithm considersseveral volumes at the same time (and not a single volume at a time). By using all the dimensions, it is for example possibleto remove some of the time varying reconstruction artefacts that exist in CT volumes. The problem with 4D image denoising,compared to 2D and 3D denoising, is that the computational complexity increases exponentially.In this paper we describe a novel algorithm for true 4D image denoising, based on local adaptive filtering, and how to implementit on the graphics processing unit (GPU). The algorithm was applied to a 4D CT heart dataset of the resolution 512 x 512 x 445 x 20.The result is that the GPU can complete the denoising in about 25 minutes if spatial filtering is used and in about 8 minutes if FFTbased filtering is used. The CPU implementation requires several days of processing time for spatial filtering and about 50 minutesfor FFT based filtering. Fast spatial filtering makes it possible to apply the denoising algorithm to larger datasets (compared to ifFFT based filtering is used). The short processing time increases the clinical value of true 4D image denoising significantly.

  • 15.
    Eklund, Anders
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Ohlsson, Henrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ynnerman, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    A Brain Computer Interface for Communication Using Real-Time fMRI2010In: Proceedings of the 20th International Conference on Pattern Recognition, Los Alamitos, CA, USA: IEEE Computer Society, 2010, p. 3665-3669Conference paper (Refereed)
    Abstract [en]

    We present the first step towards a brain computer interface (BCI) for communication using real-time functional magnetic resonance imaging (fMRI). The subject in the MR scanner sees a virtual keyboard and steers a cursor to select different letters that can be combined to create words. The cursor is moved to the left by activating the left hand, to the right by activating the right hand, down by activating the left toes and up by activating the right toes. To select a letter, the subject simply rests for a number of seconds. We can thus communicate with the subject in the scanner by for example showing questions that the subject can answer. Similar BCI for communication have been made with electroencephalography (EEG). The subject then focuses on a letter while different rows and columns of the virtual keyboard are flashing and the system tries to detect if the correct letter is flashing or not. In our setup we instead classify the brain activity. Our system is neither limited to a communication interface, but can be used for any interface where five degrees of freedom is necessary.

  • 16.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Warntjes, Marcel
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Clinical Physiology in Linköping.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Phase Based Volume Registration on the GPU with Application to Quantitative MRI2010Conference paper (Other academic)
    Abstract [en]

    We present a method for fast phase based registration of volume data for medical applications. As the number of different modalities within medical imaging increases, it becomes more and more important with registration that works for a mixture of modalities. For these applications the phase based registration approach has proven to be superior. Today there seem to be two kinds of groups that work with medical image registration, one that works with refining of the registration algorithms and one that works with implementation of more simple algorithms on graphic cards for speeding up the algorithms. We put the work from these groups together and get the best from both worlds. We achieve a speedup of 10-30 compared to our CPU implementation, which makes fast phase based registration possible for large medical volumes.

  • 17.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Forsberg, Daniel
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Using the Local Phase of the Magnitude of the Local Structure Tensor for Image Registration2011In: Image Analysis: 17th Scandinavian Conference, SCIA 2011, Ystad, Sweden, May 2011. Proceedings / [ed] Anders Heyden, Fredrik Kahl, Springer Berlin/Heidelberg, 2011, Vol. 6688, p. 414-423Conference paper (Refereed)
    Abstract [en]

    The need of image registration is increasing, especially in the medical image domain. The simplest kind of image registration is to match two images that have similar intensity. More advanced cases include the problem of registering images of different intensity, for which phase based algorithms have proven to be superior. In some cases the phase based registration will fail as well, for instance when the images to be registered do not only differ in intensity but also in local phase. This is the case if a dark circle in the reference image is a bright circle in the source image. While rigid registration algorithms can use other parts of the image to calculate the global transformation, this problem is harder to solve for non-rigid registration. The solution that we propose in this work is to use the local phase of the magnitude of the local structure tensor, instead of the local phase of the image intensity. By doing this, we achieve invariance both to the image intensity and to the local phase and thereby only use the structural information, i.e. the shapes of the objects, for registration.

  • 18.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Friman, Ola
    Fraunhofer Mevis, Bremen, Germany.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    A GPU accelerated interactive interface for exploratory functional connectivity analysis of FMRI data2011In: Image Processing (ICIP), 2011, IEEE , 2011, p. 1589-1592Conference paper (Refereed)
    Abstract [en]

    Functional connectivity analysis is a way to investigate how different parts of the brain are connected and interact. A common measure of connectivity is the temporal correlation between a reference voxel time series and all the other time series in a functional MRI data set. An fMRI data set generally contains more than 20,000 within-brain voxels, making a complete correlation analysis between all possible combinations of voxels heavy to compute, store, visualize and explore. In this paper, a GPU-accelerated interactive tool for investigating functional connectivity in fMRI data is presented. A reference voxel can be moved by the user and the correlations to all other voxels are calculated in real-time using the graphics processing unit (GPU). The resulting correlation map is updated in real-time and visualized as a 3D volume rendering together with a high resolution anatomical volume. This tool greatly facilitates the search for interesting connectivity patterns in the brain.

  • 19.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Friman, Ola
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Comparing fMRI Activity Maps from GLM and CCA at the Same Significance Level by Fast Random Permutation Tests on the GPU2011Conference paper (Other academic)
    Abstract [en]

    Parametric statistical methods are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks. First, it isassumed that the observed data are Gaussian distributed and independent; assumptions that generally are not valid for fMRI data. Second, the statistical test distribution can be derived theoretically only for very simple linear detection statistics. In this work it is shown how the computational power of the Graphics Processing Unit (GPU) can be used to speedup non-parametric tests, such as random permutation tests. With random permutation tests it is possible to calculate significance thresholds for any test statistics. As an example, fMRI activity maps from the General Linear Model (GLM) and Canonical Correlation Analysis (CCA) are compared at the same significance level.

  • 20.
    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.

  • 21.
    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.

  • 22.
    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.

  • 23.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, Statistics.
    Nichols, Thomas
    Department of Statistics, University of Warwick, England.
    Andersson, Mats
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering.
    Empirically Investigating the Statistical Validity of SPM, FSL and AFNI for Single Subject fMRI Analysis2015In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), IEEE conference proceedings, 2015, p. 1376-1380Conference paper (Refereed)
    Abstract [en]

    The software packages SPM, FSL and AFNI are the most widely used packages for the analysis of functional magnetic resonance imaging (fMRI) data. Despite this fact, the validity of the statistical methods has only been tested using simulated data. By analyzing resting state fMRI data (which should not contain specific forms of brain activity) from 396 healthy con- trols, we here show that all three software packages give in- flated false positive rates (4%-96% compared to the expected 5%). We isolate the sources of these problems and find that SPM mainly suffers from a too simple noise model, while FSL underestimates the spatial smoothness. These results highlight the need of validating the statistical methods being used for fMRI. 

  • 24.
    Eklund, Anders
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Science & Engineering.
    Nichols, Thomas
    University of Warwick, England.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering.
    Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates2016In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 113, no 28, p. 7900-7905Article in journal (Refereed)
    Abstract [en]

    The most widely used task functional magnetic resonance imaging (fMRI) analyses use parametric statistical methods that depend on a variety of assumptions. In this work, we use real resting-state data and a total of 3 million random task group analyses to compute empirical familywise error rates for the fMRI software packages SPM, FSL, and AFNI, as well as a nonparametric permutation method. For a nominal familywise error rate of 5%, the parametric statistical methods are shown to be conservative for voxelwise inference and invalid for clusterwise inference. Our results suggest that the principal cause of the invalid cluster inferences is spatial autocorrelation functions that do not follow the assumed Gaussian shape. By comparison, the nonparametric permutation test is found to produce nominal results for voxelwise as well as clusterwise inference. These findings speak to the need of validating the statistical methods being used in the field of neuroimaging.

  • 25.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology.
    Ohlsson, Henrik
    Linköping University, Department of Electrical Engineering, Electronics System. Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology.
    Rydell, Joakim
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology.
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, The Institute of Technology.
    Balancing an Inverted Pendulum by Thinking A Real-Time fMRI Approach2009Conference paper (Other academic)
    Abstract [en]

    We present a method for controlling a dynamical system using real-time fMRI. The objective for the subject in the MR scanner is to balance an inverse pendulum by activating the left or right hand or resting. The brain activity is classified each second by a neural network and the classification is sent to a pendulum simulator to change the force applied to the pendulum. The state of the inverse pendulum is shown to the subject in a pair of VR goggles. The subject was able to balance the inverse pendulum both with real activity and imagined activity. The developments here have a potential to aid people with communication disabilities e.g., locked in people. It might also be a tool for stroke patients to be ableto train the damaged brain area and get real-time feedback of when they do it right.

  • 26.
    Eklund, Anders
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Ohlsson, Henrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Rydell, Joakim
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Ynnerman, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Using Real-Time fMRI to Control a Dynamical System2009In: ISMRM 17th Scientific Meeting & Exhibition, 2009Conference paper (Refereed)
    Abstract [en]

    We present e method for controlling a dynamical system using real-time fMRI. The objective for the subject in the MR scanner is to balance an inverse pendulum by activating the left or right hand or resting. The brain activity is clasified each second by a neural network and the classification is sent to a pendulum simulator to change the state of the pendulum. The state of the inverse pendulum is shown to the subject in a pair of VR goggles. The subject was able to balance the inverse pendulum during a 7 minute test run.

  • 27.
    Eklund, Anders
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Ohlsson, Henrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Rydell, Joakim
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Ynnerman, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Using Real-Time fMRI to Control a Dynamical System2009Report (Other academic)
    Abstract [en]

    We present e method for controlling a dynamical system using real-time fMRI. The objective for the subject in the MR scanner is to balance an inverse pendulum by activating the left or right hand or resting. The brain activity is clasified each second by a neural network and the classification is sent to a pendulum simulator to change the state of the pendulum. The state of the inverse pendulum is shown to the subject in a pair of VR goggles. The subject was able to balance the inverse pendulum during a 7 minute test run.

  • 28.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ohlsson, Henrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Rydell, Joakim
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ynnerman, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Using Real-Time fMRI to Control a Dynamical System by Brain Activity Classification2009In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009: 12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part I / [ed] Gerhard Goos, Juris Hartmanis and Jan van Leeuwen, Springer Berlin/Heidelberg, 2009, 1, p. 1000-1008Conference paper (Refereed)
    Abstract [en]

    We present a method for controlling a dynamical system using real-time fMRI. The objective for the subject in the MR scanner is to balance an inverted pendulum by activating the left or right hand or resting. The brain activity is classified each second by a neural network and the classification is sent to a pendulum simulator to change the force applied to the pendulum. The state of the inverted pendulum is shown to the subject in a pair of VR goggles. The subject was able to balance the inverted pendulum during several minutes, both with real activity and imagined activity. In each classification 9000 brain voxels were used and the response time for the system to detect a change of activity was on average 2-4 seconds. The developments here have a potential to aid people with communication disabilities, such as locked in people. Another future potential application can be to serve as a tool for stroke and Parkinson patients to be able to train the damaged brain area and get real-time feedback for more efficient training.

  • 29.
    Eklund, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Warntjes, Marcel
    Linköping University, Department of Medical and Health Sciences, Clinical Physiology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Fast Phase Based Registration for Robust Quantitative MRI2010In: Proceedings of the annual meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2010), 2010Conference paper (Other academic)
    Abstract [en]

    Quantitative magnetic resonance imaging has the major advantage that it handles absolute measurements of physical parameters. Quantitative MRI can for example be used to estimate the amount of different tissue types in the brain, but other applications are possible. Parameters such as relaxation rates R1 and R2 and proton density (PD) are independent of MR scanner settings and imperfections and hence are directly representative of the underlying tissue characteristics. Brain tissue quantification is an important aid for diagnosis of neurological diseases, such as multiple sclerosis (MS) and dementia. It is applied to estimate the volume of each tissue type, such as white tissue, grey tissue, myelin and cerebrospinal fluid (CSF). Tissue that deviates from normal values can be found automatically using computer aided diagnosis. In order for the quantification to have a clinical value, both the time in the MR scanner and the time for the data analysis have to be minimized. A challenge in MR quantification is to keep the scan time within clinically acceptable limits. The quantification method that we have used is based on the work by Warntjes et al.

  • 30.
    Forsberg, Daniel
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Non-Rigid Volume Registration - A CUDA-based GPU Implementation of the Morphon2011Conference paper (Other academic)
    Abstract [en]

    Image registration is frequently used within the medical image domain and where methods with high performance are required. The need for high accuracy coupled with high speed is especially important for applications such as adaptive radiation therapy and image-guided surgery. During the last years, a number of significant projects have been introduced to make the computational power of GPUs available to a wider audience. The most well known project is the introduction of CUDA (Compute Unified Device Architecture). In this paper, we present a CUDA based GPU implementation of a non-rigid image registration algorithm, known as the Morphon, and compare it with a CPU implementation of the Morphon. The achieved speedup, in the range of 51-54x, is also compared with speedups reported from other non-rigid registration methods mplemented on the GPU. These include the Demons algorithm and a mutual information based algorithm.

  • 31.
    Forsberg, Daniel
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Phase-Based Non-Rigid 3D Image Registration - From Minutes to Seconds Using CUDA2011Conference paper (Other academic)
    Abstract [en]

    Image registration is a well-known concept within the medical image domain and has been shown to be useful in a number of dierent tasks. However, due to sometimes long processing times, image registration is not fully utilized in clinical workows, where time is an important factor. During the last couple of years, a number of signicant projects have been introduced to make the computational power of GPUs available to a wider audience, where the most well known is CUDA. In this paper we present, with the aid of CUDA, a speedup in the range of 38-44x (from 29 minutes to 40 seconds) when implementing a phasebased non-rigid image registration algorithm, known as the Morphon, on a single GPU. The achieved speedup is in the same magnitude as the speedups reported from other non-rigid registration algorithms fully ported to the GPU. Given the impressive speedups, both reported in this paper and other papers, we therefore consider that it is now feasible to eectively integrate image registration into various clinical workows, where time is a critical factor.

  • 32.
    Gu, Xuan
    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).
    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).
    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).
    Repeated Tractography of a Single Subject: How High Is the Variance?2017In: Modeling, Analysis, and Visualization of Anisotropy / [ed] Thomas Schultz, Evren Özarslan, Ingrid Hotz, Springer, 2017, p. 331-354Chapter in book (Other academic)
    Abstract [en]

    We have investigated the test-retest reliability of diffusion tractography, using 32 diffusion datasets from a single healthy subject. Preprocessing was carried out using functions in FSL (FMRIB Software Library), and tractography was carried out using FSL and Dipy. The tractography was performed in diffusion space, using two seed masks (corticospinal and cingulum gyrus tracts) created from the JHU White-Matter Tractography atlas. The tractography results were then warped into MNI standard space by a linear transformation. The reproducibility of tract metrics was examined using the standard deviation, the coefficient of variation (CV) and the Dice similarity coefficient (DSC), which all indicated a high reproducibility. Our results show that the multi-fiber model in FSL is able to reveal more connections between brain areas, compared to the single fiber model, and that distortion correction increases the reproducibility.

  • 33.
    Gu, Xuan
    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).
    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).
    Özarslan, Evren
    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).
    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).
    Using the wild bootstrap to quantify uncertainty in mean apparent propagator MRI2019In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 13, article id 43Article in journal (Refereed)
    Abstract [en]

    Purpose: Estimation of uncertainty of MAP-MRI metricsis an important topic, for several reasons. Bootstrap deriveduncertainty, such as the standard deviation, providesvaluable information, and can be incorporated in MAP-MRIstudies to provide more extensive insight.

    Methods: In this paper, the uncertainty of different MAPMRImetrics was quantified by estimating the empirical distributionsusing the wild bootstrap. We applied the wildbootstrap to both phantom data and human brain data, andobtain empirical distributions for theMAP-MRImetrics returnto-origin probability (RTOP), non-Gaussianity (NG) and propagatoranisotropy (PA).

    Results: We demonstrated the impact of diffusion acquisitionscheme (number of shells and number of measurementsper shell) on the uncertainty of MAP-MRI metrics.We demonstrated how the uncertainty of these metrics canbe used to improve group analyses, and to compare differentpreprocessing pipelines. We demonstrated that withuncertainty considered, the results for a group analysis canbe different.

    Conclusion: Bootstrap derived uncertain measures provideadditional information to the MAP-MRI derived metrics, andshould be incorporated in ongoing and future MAP-MRIstudies to provide more extensive insight.

  • 34.
    Gu, Xuan
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    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).
    Nilsson, Markus
    Department of Clinical Sciences, Radiology, Lund UniversityLundSweden.
    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).
    Generating Diffusion MRI Scalar Maps from T1 Weighted Images Using Generative Adversarial Networks2019In: Image Analysis: Lecture Notes in Computer Science / [ed] Felsberg M., Forssén PE., Sintorn IM., Unger J., Springer Publishing Company, 2019, p. 489-498Conference paper (Refereed)
    Abstract [en]

    Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique. Scalar measures, such as FA (fractional anisotropy) and MD (mean diffusivity), quantifying micro-structural tissue properties can be obtained using diffusion models and data processing pipelines. However, it is costly and time consuming to collect high quality diffusion data. Here, we therefore demonstrate how Generative Adversarial Networks (GANs) can be used to generate synthetic diffusion scalar measures from structural T1-weighted images in a single optimized step. Specifically, we train the popular CycleGAN model to learn to map a T1 image to FA or MD, and vice versa. As an application, we show that synthetic FA images can be used as a target for non-linear registration, to correct for geometric distortions common in diffusion MRI.

  • 35.
    Gu, Xuan
    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).
    Sidén, Per
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering.
    Wegmann, Bertil
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. 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).
    Villani, Mattias
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering.
    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).
    Bayesian Diffusion Tensor Estimation with Spatial Priors2017In: CAIP 2017: Computer Analysis of Images and Patterns, 2017, Vol. 10424, p. 372-383Conference paper (Refereed)
    Abstract [en]

    Spatial regularization is a technique that exploits the dependence between nearby regions to locally pool data, with the effect of reducing noise and implicitly smoothing the data. Most of the currently proposed methods are focused on minimizing a cost function, during which the regularization parameter must be tuned in order to find the optimal solution. We propose a fast Markov chain Monte Carlo (MCMC) method for diffusion tensor estimation, for both 2D and 3D priors data. The regularization parameter is jointly with the tensor using MCMC. We compare FA (fractional anisotropy) maps for various b-values using three diffusion tensor estimation methods: least-squares and MCMC with and without spatial priors. Coefficient of variation (CV) is calculated to measure the uncertainty of the FA maps calculated from the MCMC samples, and our results show that the MCMC algorithm with spatial priors provides a denoising effect and reduces the uncertainty of the MCMC samples.

  • 36. Maghsadhagh, Sevil
    et al.
    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).
    Behjat, Hamid
    Department of Biomedical Engineering, Lund University, Lund, Sweden.
    Graph Spectral Characterization of Brain Cortical Morphology2019Conference paper (Refereed)
    Abstract [en]

    The human brain cortical layer has a convoluted morphology that is unique to each individual. Characterization of the cortical morphology is necessary in longitudinal studies of structural brain change, as well as in discriminating individuals in health and disease. A method for encoding the cortical morphology in the form of a graph is presented. The design of graphs that encode the global cerebral hemisphere cortices as well as localized cortical regions is proposed. Spectral metrics derived from these graphs are then studied and proposed as descriptors of cortical morphology. As proof-of-concept of their applicability in characterizing cortical morphology, the metrics are studied in the context of hemispheric asymmetry as well as gender dependent discrimination of cortical morphology.

  • 37.
    Nguyen, Tan Khoa
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ohlsson, Henrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Hernell, Frida
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ljung, Patric
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Forsell, Camilla
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ynnerman, Anders
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Concurrent Volume Visualization of Real-Time fMRI2010In: Proceedings of the 8th IEEE/EG International Symposium on Volume Graphics / [ed] Ruediger Westermann and Gordon Kindlmann, Goslar, Germany: Eurographics - European Association for Computer Graphics, 2010, p. 53-60Conference paper (Refereed)
    Abstract [en]

    We present a novel approach to interactive and concurrent volume visualization of functional Magnetic Resonance Imaging (fMRI). While the patient is in the scanner, data is extracted in real-time using state-of-the-art signal processing techniques. The fMRI signal is treated as light emission when rendering a patient-specific high resolution reference MRI volume, obtained at the beginning of the experiment. As a result, the brain glows and emits light from active regions. The low resolution fMRI signal is thus effectively fused with the reference brain with the current transfer function settings yielding an effective focus and context visualization. The delay from a change in the fMRI signal to the visualization is approximately 2 seconds. The advantage of our method over standard 2D slice based methods is shown in a user study. We demonstrate our technique through experiments providing interactive visualization to the fMRI operator and also to the test subject in the scanner through a head mounted display.

  • 38.
    Pernet, Cyril
    et al.
    Centre for Clinical Brain Sciences, Edinburgh Imaging, University of Edinburgh, UK.
    Marinazzo, Daniele
    Faculty of Psychological and Educational Sciences, Gent University, Belgium.
    Stippich, Christoph
    Department of Neuroradiology, University Hospital Zürich, Switzerland.
    Beisteiner, Roland
    Department of Neurology, High Field MR Center, Medical University of Vienna, Austria.
    Douw, Linda
    VU University Medical Center, University of Amsterdam, Netherlands.
    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).
    A new repository to share brain tumour data: European Network for Brain Imaging of Tumours2019Conference paper (Refereed)
  • 39.
    Sjölund, Jens
    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). Elekta Instrument, 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).
    Özarslan, Evren
    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).
    Herberthson, Magnus
    Linköping University, Department of Mathematics, Mathematics and Applied Mathematics. Linköping University, Faculty of Science & Engineering.
    Bånkestad, Maria
    RISE SICS, Kista, Sweden.
    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).
    Bayesian uncertainty quantification in linear models for diffusion MRI2018In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 175, p. 272-285Article in journal (Refereed)
    Abstract [en]

    Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Several of the most popular dMRI signal models are expansions in an appropriately chosen basis, where the coefficients are determined using some variation of least-squares. However, such approaches lack any notion of uncertainty, which could be valuable in e.g. group analyses. In this work, we use a probabilistic interpretation of linear least-squares methods to recast popular dMRI models as Bayesian ones. This makes it possible to quantify the uncertainty of any derived quantity. In particular, for quantities that are affine functions of the coefficients, the posterior distribution can be expressed in closed-form. We simulated measurements from single- and double-tensor models where the correct values of several quantities are known, to validate that the theoretically derived quantiles agree with those observed empirically. We included results from residual bootstrap for comparison and found good agreement. The validation employed several different models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI) and Constrained Spherical Deconvolution (CSD). We also used in vivo data to visualize maps of quantitative features and corresponding uncertainties, and to show how our approach can be used in a group analysis to downweight subjects with high uncertainty. In summary, we convert successful linear models for dMRI signal estimation to probabilistic models, capable of accurate uncertainty quantification.

  • 40.
    Wegmann, Bertil
    et al.
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering.
    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).
    Villani, Mattias
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
    Bayesian Heteroscedastic Regression for Diffusion Tensor Imaging2017In: Modeling, Analysis, and Visualization of Anisotropy / [ed] Thomas Schultz; Evren Özarslan; Ingrid Hotz, Springer Publishing Company, 2017, 1, p. 257-282Conference paper (Refereed)
    Abstract [en]

    We propose a single-diffusion tensor model with heteroscedastic noise and a Bayesian approach via a highly efficient Markov Chain Monte Carlo (MCMC) algorithm for inference. The model is very flexible since both the noise-free signal and the noise variance are functions of diffusion covariates, and the relevant covariates in the noise are automatically selected by Bayesian variable selection. We compare the estimated diffusion tensors from our model to a homoscedastic counterpart with no covariates in the noise, and to commonly used linear and nonlinear least squares methods. The estimated single-diffusion tensors within each voxel are compared with respect to fractional anisotropy (FA) and mean diffusivity (MD). Using data from the Human Connectome Project, our results show that the noise is clearly heteroscedastic, especially the posterior variance for MD is substantially underestimated by the homoscedastic model, and inferences from the homoscedastic model are on average spuriously precise. Inferences from commonly used ordinary and weighted least squares methods (OLS and WLS) show that it is not adequate to estimate the single-diffusion tensor from logarithmic measurements.

1 - 40 of 40
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