MoG-QSM: Model-based Generative Adversarial Deep Learning Network for Quantitative Susceptibility Mapping

by   Ruimin Feng, et al.

Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from the MRI gradient-echo phase signal and has demonstrated great potential in quantifying tissue susceptibility in various brain diseases. However, the intrinsic ill-posed inverse problem relating the tissue phase to the underlying susceptibility distribution affects the accuracy for quantifying tissue susceptibility. The resulting susceptibility map is known to suffer from noise amplification and streaking artifacts. To address these challenges, we propose a model-based framework that permeates benefits from generative adversarial networks to train a regularization term that contains prior information to constrain the solution of the inverse problem, referred to as MoG-QSM. A residual network leveraging a mixture of least-squares (LS) GAN and the L1 cost was trained as the generator to learn the prior information in susceptibility maps. A multilayer convolutional neural network was jointly trained to discriminate the quality of output images. MoG-QSM generates highly accurate susceptibility maps from single orientation phase maps. Quantitative evaluation parameters were compared with recently developed deep learning QSM methods and the results showed MoG-QSM achieves the best performance. Furthermore, a higher intraclass correlation coefficient (ICC) was obtained from MoG-QSM maps of the traveling subjects, demonstrating its potential for future applications, such as large cohorts of multi-center studies. MoG-QSM is also helpful for reliable longitudinal measurement of susceptibility time courses, enabling more precise monitoring for metal ion accumulation in neurodegenerative disorders.


page 15

page 17

page 18

page 19

page 20

page 21

page 22

page 23


QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with Increased Receptive Field

Quantitative susceptibility mapping (QSM) is a powerful MRI technique th...

Learning-based Single-step Quantitative Susceptibility Mapping Reconstruction Without Brain Extraction

Quantitative susceptibility mapping (QSM) estimates the underlying tissu...

Deep Generative Adversarial Networks for Compressed Sensing Automates MRI

Magnetic resonance image (MRI) reconstruction is a severely ill-posed li...

Weakly-supervised Learning for Single-step Quantitative Susceptibility Mapping

Quantitative susceptibility mapping (QSM) utilizes MRI phase information...

Rapid tissue oxygenation mapping from snapshot structured-light images with adversarial deep learning

Spatial frequency domain imaging (SFDI) is a powerful technique for mapp...

Learned Proximal Networks for Quantitative Susceptibility Mapping

Quantitative Susceptibility Mapping (QSM) estimates tissue magnetic susc...

ScarGAN: Chained Generative Adversarial Networks to Simulate Pathological Tissue on Cardiovascular MR Scans

Medical images with specific pathologies are scarce, but a large amount ...