Objective: To investigate the usefulness of intravoxel incoherent motion (IVIM) in determining the severity of hepatic fibrosis, steatosis, and inflammation in patients with chronic liver disease.
Methods: Forty-nine patients who had liver MRI with IVIM sequence and liver biopsy within three months of MRI were enrolled. A reviewer, blinded to histology, placed regions of interest of 1-2 cm in the right liver lobe. In addition, the first twenty patients were assessed with a second reviewer. Perfusion fraction (f), pseudodiffusion coefficient (D ), true diffusion coefficient (D ), and apparent diffusion coefficient (ADC) were calculated from normalized signal intensities that were fitted into a biexponential model. Errors in the model were minimized with global stochastic optimization using Simulated Annealing. ANOVA with post hoc Tukey-Kramer test and multivariate generalized linear model analysis were performed, using histological findings as the gold standard.
Results: The most common etiologies for liver disease were hepatitis C and alcohol, accounting together for 76% (37/49) of patients. Low-grade fibrosis (F0, F1), hepatic steatosis, and inflammation were seen in 24% (12/49), 31% (15/49), and 29% (14/49) of patients, respectively. The interobserver correlation was poor for D and D (0.105, 0.173) and moderate for f and ADC (0.461, 0.418). ANOVA showed a strong inverse association between D and liver fibrosis grade (p = 0.001). A weak inverse association was seen between ADC and hepatic steatosis (p = 0.059). Multivariate general linear model revealed that the only significant association between IVIM parameters and pathological features was between D and fibrosis. On ROC curve analysis, D < 23.4 × 10 mm/s had a sensitivity of 82.8% and a specificity of 64.3% in predicting high-grade fibrosis.
Conclusion: D has the strongest association with hepatic fibrosis but has weak interobserver correlation. IVIM parameters were not significantly associated with hepatic inflammation or steatosis.
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http://dx.doi.org/10.1007/s00261-017-1263-8 | DOI Listing |
AJNR Am J Neuroradiol
January 2025
From the Department of Radiology, Medical Physics (MML, TJC), Department of Interventional Radiology (NS, GAC), Department of Surgery and Large Animal Studies (MAN), and the Department of Statistics (MG), University of Chicago, Chicago, IL, USA; Department of Anesthesiology (SPR), University of Illinois, Chicago, IL, USA; Department of Radiology (MSS), University of Massachusetts Chan Medical School, Worcester, MA, USA; Department of Radiology, Biomedical Engineering and Imaging Institute (Current affiliation MML), Icahn School of Medicine at Mount Sinai, New York, NY, USA; Mount Carmel Health Systems (Current affiliation GAC), Columbus, OH, USA.
Background And Purpose: In acute ischemic stroke, the amount of "local" CBF distal to the occlusion, i.e. all blood flow within a region whether supplied antegrade or delayed and dispersed through the collateral network, may contain valuable information regarding infarct growth rate and treatment response.
View Article and Find Full Text PDFRofo
January 2025
Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany.
Multiparametric MRI is a promising technique for noninvasive structural and functional imaging of the kidneys that is gaining increasing importance in clinical research. Still, there are no standardized recommendations for analyzing the acquired images and there is a need to further evaluate the accuracy and repeatability of currently recommended MRI parameters. The aim of the study was to evaluate the test-retest repeatability of functional renal MRI parameters using different image analysis strategies.
View Article and Find Full Text PDFMed Image Anal
December 2024
Faculty of Biomedical Engineering, Technion, Haifa, Israel. Electronic address:
Quantitative analysis of pseudo-diffusion in diffusion-weighted magnetic resonance imaging (DWI) data shows potential for assessing fetal lung maturation and generating valuable imaging biomarkers. Yet, the clinical utility of DWI data is hindered by unavoidable fetal motion during acquisition. We present IVIM-morph, a self-supervised deep neural network model for motion-corrected quantitative analysis of DWI data using the Intra-voxel Incoherent Motion (IVIM) model.
View Article and Find Full Text PDFMed Image Anal
November 2024
Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway; Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.
In medical image analysis, the utilization of biophysical models for signal analysis offers valuable insights into the underlying tissue types and microstructural processes. In diffusion-weighted magnetic resonance imaging (DWI), a major challenge lies in accurately estimating model parameters from the acquired data due to the inherently low signal-to-noise ratio (SNR) of the signal measurements and the complexity of solving the ill-posed inverse problem. Conventional model fitting approaches treat individual voxels as independent.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
This study aimed to establish and validate a multiparameter prediction model for Ki67 expression in hepatocellular carcinoma (HCC) patients while also exploring its potential to predict the one-year recurrence risk. The clinical, pathological, and imaging data of 83 patients with HCC confirmed by postoperative pathology were analyzed, and the patients were randomly divided into a training set (n = 58) and a validation set (n = 25) at a ratio of 7:3. All patients underwent a magnetic resonance imaging (MRI) scan that included multi-b value diffusion-weighted scanning before surgery, and quantitative parameters were obtained via intravoxel incoherent motion (IVIM) and diffusion kurtosis (DKI) models.
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