Fat–water MRI separation using deep complex convolution network

Objective Deep complex convolutional networks (DCCNs) utilize complex-valued convolutions and can process complex-valued MRI signals directly without splitting them into two real-valued magnitude and phase components. The performance of DCCN and real-valued U-Net is thoroughly investigated in the ph...

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Bibliographic Details
Published in:	Magma (New York, N.Y.) Vol. 38; no. 6; pp. 959 - 977
Main Authors:	Ganeshkumar, Moorthy, Kandasamy, Devasenathipathy, Sharma, Raju, Mehndiratta, Amit
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 01.12.2025 Springer Nature B.V
Subjects:	Abdomen - diagnostic imaging Adipose Tissue - diagnostic imaging Algorithms Basic Science - Reconstruction algorithms and artificial intelligence Biomedical Engineering and Bioengineering Body Water - diagnostic imaging Computer Appl. in Life Sciences Convex analysis Correlation coefficient Correlation coefficients Datasets Deep Learning Echoes Fatty liver Fatty Liver - diagnostic imaging Health Informatics Humans Image Processing, Computer-Assisted - methods Imaging Knee - diagnostic imaging Magnetic resonance imaging Magnetic Resonance Imaging - methods Medicine Medicine & Public Health Methods Neural Networks, Computer Optimization algorithms Phantoms, Imaging Physics Radiology Reconstruction Research Article Separation Solid State Physics Thigh - diagnostic imaging Water Water - chemistry MRI fat quantification Fat–water separation Physics-informed deep learning Complex-valued convolution Deep complex network
ISSN:	1352-8661, 0968-5243, 1352-8661
Online Access:	Get full text
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