Deep learning how to fit an intravoxel incoherent motion model to diffusion-weighted MRI.

Journal: Magnetic resonance in medicine

Published Date: Jan 1, 2020

Abstract

PURPOSE: This prospective clinical study assesses the feasibility of training a deep neural network (DNN) for intravoxel incoherent motion (IVIM) model fitting to diffusion-weighted MRI (DW-MRI) data and evaluates its performance.

Authors

Sebastiano Barbieri

Centre for Big Data Research in Health, UNSW, Sydney, Australia.
Oliver J Gurney-Champion

Joint Department of Physics, The Institute of Cancer Research, London, United Kingdom.
Remy Klaassen

Cancer Center Amsterdam, Department of Medical Oncology and LEXOR (Laboratory for Experimental Oncology and Radiobiology), Academic Medical Center, Amsterdam, The Netherlands.
Harriet C Thoeny

Department of Radiology, HFR Fribourg-Hôpital Cantonal, Fribourg, Switzerland.

Keywords

Adult Algorithms Bayes Theorem Computer Simulation Deep Learning Diffusion Magnetic Resonance Imaging Humans Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Kidney Least-Squares Analysis Liver Male Middle Aged Motion Pancreas Prospective Studies Reproducibility of Results Spleen

External Resources

View on PubMed Access via DOI PubMed (31389081)

Deep learning how to fit an intravoxel incoherent motion model to diffusion-weighted MRI.

Abstract

Authors

Keywords

External Resources

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