Fat-water MRI separation using deep complex convolution network.
Authors
Affiliations (5)
Affiliations (5)
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India.
- Department of Radio Diagnosis, All India Institute of Medical Sciences, New Delhi, India.
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India. [email protected].
- Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi, India. [email protected].
- Faculty of Medicine and Health, University of New South Wales (UNSW), Sydney, Australia. [email protected].
Abstract
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 physics-informed subject-specific ad-hoc reconstruction method for fat-water separation and is compared against a widely used reference approach. A comprehensive test dataset (n = 33) was used for performance analysis. The 2012 ISMRM fat-water separation workshop dataset containing 28 batches of multi-echo MRIs with 3-15 echoes from the abdomen, thigh, knee, and phantoms, acquired with 1.5 T and 3 T scanners were used. Additionally, five MAFLD patients multi-echo MRIs acquired from our clinical radiology department were also used. The quantitative results demonstrated that DCCN produced fat-water maps with better normalized RMS error and structural similarity index with the reference approach, compared to real-valued U-Nets in the ad-hoc reconstruction method for fat-water separation. The DCCN achieved an overall average SSIM of 0.847 ± 0.069 and 0.861 ± 0.078 in generating fat and water maps, respectively, in contrast the U-Net achieved only 0.653 ± 0.166 and 0.729 ± 0.134. The average liver PDFF from DCCN achieved a correlation coefficient R of 0.847 with the reference approach.