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  • 1.
    Forsgren, Mikael
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Dahlström, Nils
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Karlsson, Markus
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, The Institute of Technology. Linköping University, Department of Clinical and Experimental Medicine, Division of Neuroscience.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Smedby, Örjan
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Health Sciences.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Whole Body Mechanistic Minimal Model for Gd-EOB-DTPA Contrast Agent Pharmacokinetics in Evaluation of Diffuse Liver Disease2014Conference paper (Other academic)
    Abstract [en]

    Purpose: Aiming for non-invasive diagnostic tools to decrease the need for biopsy in diffuse liver disease and to quantitatively describe liver function, we applied a mechanistic pharmacokinetic modelling analysis of liver MRI with Gd-EOB-DTPA. This modelling method yields physiologically relevant parameters and was compared to previously developed methods in a patient group with diffuse liver disease. Materials and Methods: Using data from healthy volunteers undergoing liver MRI, an identifiable mechanistic model was developed, based on compartments described by ordinary differential equations and kinetic expressions, and validated with independent data including Gd-EOB-DTPA concentration measurements in blood samples. Patients (n=37) with diffuse liver disease underwent liver biopsy and MRI with Gd-EOB-DTPA. The model was used to derive pharmacokinetic parameters which were then compared with other quantitative estimates in their ability to separate mild from severe liver fibrosis. Results: The estimations produced by the mechanistic model allowed better separation between mild and severe fibrosis than previously described methods for quantifying hepatic Gd-EOB-DTPA uptake. Conclusions: With a mechanistic pharmacokinetic modelling approach, the estimation of liver uptake function and its diagnostic information can be improved compared to current methods.

  • 2.
    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).
    Non-Invasive Characterization of Liver Disease: By Multimodal Quantitative Magnetic Resonance2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    There is a large and unmet need for diagnostic tool that can be used to characterize chronic liver diseases (CLD). In the earlier stages of CLD, much of the diagnostics involves performing biopsies, which are evaluated by a histopathologist for the presence of e.g. fat, iron, inflammation, and fibrosis. Performing biopsies, however, have two downsides: i) biopsies are invasive and carries a small but non-negligible risk for serious complications, ii) biopsies only represents a tiny portion of the liver and are thus prone to sampling error. Moreover, in the later stages of CLD, when the disease has progressed far enough, the ability of the liver to perform its basic function will be compromised. In this stage, there is a need for better methods for accurately measuring liver function. Additionally, measures of liver function can also be used when developing new drugs, as biomarkers for drug-induced liver injury (DILI), which is a serious drug-safety issue.

    Magnetic resonance imaging (MRI) is a non-invasive medical imaging modality, which have shown much promise with regards to characterizing liver disease in all of the abovementioned aspects. The aim of this PhD project was to develop and validate MR-based methods that can be used to non-invasively characterize liver disease.

    Paper I investigated if R2* mapping, a MR-method for measuring liver iron content, can be confounded by liver fat. The results show fat does affect R2*. The conclusion was therefore that fat must be taken into account when measuring small amounts of liver iron, as a small increase in R2* could be due to either small amounts of iron or large amounts of fat.

    Paper II examined whether T1 mapping, which is another MR-method, can be used for staging liver fibrosis. The results of previous research have been mixed; some studies have been very promising, whereas other studies have been less promising. Unfortunately, the results in Paper II belongs to the less promising studies.

    Paper III focused on measuring liver function by dynamic contrast-enhanced MRI (DCEMRI) using a liver specific contrast agent, which is taken up the hepatocytes and excreted to the bile. The purpose of the paper was to extend and validate a method for estimating uptake and efflux rates of the contrast agent. The method had previously only been applied in health volunteers. Paper II showed that the method can be applied to CLD patients and that the uptake of the contrast agent is lower in patients with advanced fibrosis.

    Paper IV also used studied liver function with DCE-MRI in patients with primary sclerosing cholangitis (PSC). PSC is a CLD where the bile ducts are attacked by the immune system. When diagnosing PSC patients, it is common to use magnetic resonance cholangiopancreatography (MRCP), which is a method for imaging the bile ducts. Paper IV examined if there was any correlation between number and severity of the morphological changes, seen on MRCP, and measures of liver function derived using DCE-MRI. However, the results showed no such correlation. The conclusion was that the results indicates that MRCP should not be used to predict parenchymal function.

    Paper V developed a method for translating DCE-MRI liver function parameters from rats to humans. This translation could be of value when developing new drugs, as a tool for predicting which drugs might cause drug-induced liver injury.

    In summary, this thesis has shown that multimodal quantitative MR has a bright future for characterizing liver disease from a range of different aspects.

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