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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.
    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.

  • 3.
    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.

    List of papers
    1. Liver R2*is affected by both iron and fat: A dual biopsy-validated study of chronic liver disease
    Open this publication in new window or tab >>Liver R2*is affected by both iron and fat: A dual biopsy-validated study of chronic liver disease
    Show others...
    2019 (English)In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 50, no 1, p. 325-333Article in journal (Refereed) Published
    Abstract [en]

    Background Liver iron content (LIC) in chronic liver disease (CLD) is currently determined by performing an invasive liver biopsy. MRI using R2* relaxometry is a noninvasive alternative for estimating LIC. Fat accumulation in the liver, or proton density fat fraction (PDFF), may be a possible confounder of R2* measurements. Previous studies of the effect of PDFF on R2* have not used quantitative LIC measurement. Purpose To assess the associations between R2*, LIC, PDFF, and liver histology in patients with suspected CLD. Study Type Prospective. Population Eighty-one patients with suspected CLD. Field Strength/Sequence 1.5 T. Multiecho turbo field echo to quantify R2*. PRESS MRS to quantify PDFF. Assessment Each patient underwent an MR examination, followed by two needle biopsies immediately following the MR examination. The first biopsy was used for conventional histological assessment of LIC, whereas the second biopsy was used to quantitatively measure LIC using mass spectrometry. R2* was correlated with both LIC and PDFF. A correction for the influence of fat on R2* was calculated. Statistical Tests Pearson correlation, linear regression, and area under the receiver operating curve. Results There was a positive linear correlation between R2* and PDFF (R = 0.69), after removing data from patients with elevated iron levels, as defined by LIC. R2*, corrected for PDFF, was the best method for identifying patients with elevated iron levels, with a correlation of R = 0.87 and a sensitivity and specificity of 87.5% and 98.6%, respectively. Data Conclusion PDFF increases R2*. Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:325-333.

    Place, publisher, year, edition, pages
    WILEY, 2019
    Keywords
    liver iron content; liver fat; R2*; PDFF; iron overload
    National Category
    Radiology, Nuclear Medicine and Medical Imaging
    Identifiers
    urn:nbn:se:liu:diva-158842 (URN)10.1002/jmri.26601 (DOI)000471831600033 ()30637926 (PubMedID)
    Note

    Funding Agencies|Region Ostergotland; Medical Research council of Southeast Sweden [FORSS #12621]; Swedish Research Council [VR/NT #2014-6157, VR/MH, #2007-2884]; Forskningsradet i Sydostra Sverige; Linkoping University; Linkoping University Hospital Research Foundations; Vinnova [#2013-01314]; Vetenskapsradet

    Available from: 2019-07-15 Created: 2019-07-15 Last updated: 2019-12-12
    2. Model-inferred mechanisms of liver function from magnetic resonance imaging data: Validation and variation across a clinically relevant cohort
    Open this publication in new window or tab >>Model-inferred mechanisms of liver function from magnetic resonance imaging data: Validation and variation across a clinically relevant cohort
    Show others...
    2019 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 15, no 6, article id e1007157Article in journal (Refereed) Published
    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.

    Place, publisher, year, edition, pages
    San Francisco, CA, United States: Public Library of Science, 2019
    National Category
    Pharmaceutical Sciences
    Identifiers
    urn:nbn:se:liu:diva-159165 (URN)10.1371/journal.pcbi.1007157 (DOI)000474703000068 ()31237870 (PubMedID)2-s2.0-85069296906 (Scopus ID)
    Note

    Funding Agencies|Swedish Research Council [2014-6157, 2007-2884]; Medical Research council of Southeast Sweden [12621]; Vinnova [2013-01314]; Linkoping University, CENIIT [15.09]; Swedish fund for research without animal experiments [Nytank2015]

    Available from: 2019-07-30 Created: 2019-07-30 Last updated: 2019-12-12Bibliographically approved
  • 4.
    Karlsson, Markus
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    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.
    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).
    Forsgren, Mikael
    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).
    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.
    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).
    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).
    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).
    Liver R2*is affected by both iron and fat: A dual biopsy-validated study of chronic liver disease2019In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 50, no 1, p. 325-333Article in journal (Refereed)
    Abstract [en]

    Background Liver iron content (LIC) in chronic liver disease (CLD) is currently determined by performing an invasive liver biopsy. MRI using R2* relaxometry is a noninvasive alternative for estimating LIC. Fat accumulation in the liver, or proton density fat fraction (PDFF), may be a possible confounder of R2* measurements. Previous studies of the effect of PDFF on R2* have not used quantitative LIC measurement. Purpose To assess the associations between R2*, LIC, PDFF, and liver histology in patients with suspected CLD. Study Type Prospective. Population Eighty-one patients with suspected CLD. Field Strength/Sequence 1.5 T. Multiecho turbo field echo to quantify R2*. PRESS MRS to quantify PDFF. Assessment Each patient underwent an MR examination, followed by two needle biopsies immediately following the MR examination. The first biopsy was used for conventional histological assessment of LIC, whereas the second biopsy was used to quantitatively measure LIC using mass spectrometry. R2* was correlated with both LIC and PDFF. A correction for the influence of fat on R2* was calculated. Statistical Tests Pearson correlation, linear regression, and area under the receiver operating curve. Results There was a positive linear correlation between R2* and PDFF (R = 0.69), after removing data from patients with elevated iron levels, as defined by LIC. R2*, corrected for PDFF, was the best method for identifying patients with elevated iron levels, with a correlation of R = 0.87 and a sensitivity and specificity of 87.5% and 98.6%, respectively. Data Conclusion PDFF increases R2*. Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:325-333.

    The full text will be freely available from 2020-09-13 14:26
  • 5.
    Karlsson, Markus
    et al.
    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. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Forsgren, Mikael
    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.
    Dahlström, Nils
    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. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Leinhard Dahlqvist, Olof
    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.
    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.
    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, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Diffuse Liver Disease: Measurements of Liver Trace Metal Concentrations and R2* Relaxation Rates2016Conference paper (Refereed)
    Abstract [en]

    Introduction

    Over the past decade, several methods for measuring of liver iron content (LIC) non-invasively with MRI have been developed and verified. The most promising methods uses relaxometry, measuring either R2- or R2* relaxation rate in the liver1,2. For instance, several studies have shown that there seems to be a linear relationship between R2* and LIC1. However, few of these studies have measured the liver content of other metals, which could also affect the relaxation rates. The goal of this study was to investigate if any trace metals, other than iron could affect the R2* relaxation rate in liver tissue in a patients with diffuse liver disease.

    Subjects and methods

    75 patients with suspected diffuse liver disease underwent an MRI examination followed by a liver biopsy the same day. The R2* relaxation rate of the water protons in the liver was measured using an axial 3D multi-slice fat-saturated multi-echo turbo field echo sequence (TE=4.60/9.20/13.80/18.40/23.00ms). Regions of interest (ROI) were drawn and R2* was estimated by fitting the mean signal intensity from the ROIs to a mono-exponential decay model. The biopsies were freeze dried and the concentrations of iron, manganese, copper, cobalt and gadolinium were measured using Inductively Coupled Plasma Sector Field Mass Spectrometry (ICP-SFMS). A multiple linear regression analysis was applied to determine which of the measured metals significantly affected the relaxation rate.

    Results

    A linear regression with the LIC and R2* showed a reasonable fit (Figure 1). The multiple linear regression analysis (Table 1) showed that iron as well as manganese had a significant affect on R2*. Unlike iron however, the regression coefficient of manganese was negative, meaning that an increasing manganese concentration gave a shorter R2* relaxation rate. The same trend can be seen when plotting the manganese concentration against R2* (Figure 2).

  • 6.
    Lundberg, Peter
    et al.
    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. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Karlsson, Markus
    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.
    Forsgren, Mikael
    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.
    Dahlström, Nils
    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. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Leinhard Dahlqvist, Olof
    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. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    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.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    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.
    Mechanistic modeling of qDCE-MRI data reveals increased bile excretion of Gd-EOB-DTPA in diffuse liver disease patients with severe fibrosis2016Conference paper (Refereed)
    Abstract [en]

    Introduction

    Over the past decades, several different non-invasive methods for staging hepatic fibrosis have been proposed. One such method is dynamic contrast enhanced MRI (DCE-MRI) using the contrast agent (CA) Gd-EOB-DTPA. Gd-EOB-DTPA is liver specific, which means that it is taken up specifically by the hepatocytes via the OATP3B1/B3 transporters and excreted into the bile via the MRP2 transporter. Several studies have shown that DCE-MRI and Gd-EOBDTPA can separate patients with advanced (F3-F4) from mild (F0-F2) hepatic fibrosis by measuring the signal intensity, where patients with advanced fibrosis have a lower signal intensity than the mild fibrosis cases.1 However, none of the studies up to date have been able to differentiate if the reduced signal intensity in the liver is because of an decreased uptake of CA or an increased excretion. Analyzing the DCE-MRI data with mechanistic mathematical modelling has the possibility of investigating such a differentiation.

    Subjects and methods

    88 patients with diffuse liver disease were examined using DCE-MRI (1.5 T Philips Achieva, two-point Dixon, TR=6.5 ms, TE=2.3/4.6 ms, FA=13) after a bolus injection of Gd-EOB-DTPA, followed by a liver biopsy. Regions of interest were placed within the liver, spleen and veins and a whole-body mechanistic pharmacokinetic model2 was fitted to the data. The fitted parameters in the model correspond to the rate of CA transport between different compartments, e.g. hepatocytes, blood plasma, and bile (Fig. 1).

    Results

    As can be seen in Fig. 2, the parameter corresponding to the transport of CA from the blood plasma to the hepatocytes, kph, is lower for patients with advanced fibrosis (p=0.01). Fig. 3 shows that the parameter corresponding to the CA excretion into the bile, khb, is higher for patients with advanced fibrosis (p<0.01).

    Discussion/Conclusion

    This work shows that the decreased signal intensity in DCE-MRI images in patients with advanced fibrosis depends on both a decreased uptake of CA in the hepatocytes and an increased excretion into the bile. Similar results have also been observed in a rat study3. In that study, rats with induced cirrhosis had a higher MRP2-activity than the healthy control rats.

    References

    1Norén et al: Eur. Radiol, 23(1), 174-181, 2013.

    2Forsgren et al: PloS One, 9(4): e95700, 2014.

    3Tsuda & Matsui: Radiol, 256(3): 767-773, 2010.

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