Paper Info
Title
MOdel-Based SyntheTic Data-Driven Learning (MOST-DL): Application in Single-Shot T<sub>2</sub> Mapping With Severe Head Motion Using Overlapping-Echo Acquisition
Abstract
Use of synthetic data has provided a potential solution for addressing unavailable or insufficient training samples in deep learning-based magnetic resonance imaging (MRI). However, the challenge brought by domain gap between synthetic and real data is usually encountered, especially under complex experimental conditions. In this study, by combining Bloch simulation and general MRI models, we propose a framework for addressing the lack of training data in supervised learning scenarios, termed MOST-DL. A challenging application is demonstrated to verify the proposed framework and achieve motion-robust <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{T}_{{2}}$ </tex-math></inline-formula> mapping using single-shot overlapping-echo acquisition. We decompose the process into two main steps: (1) calibrationless parallel reconstruction for ultra-fast pulse sequence and (2) intra-shot motion correction for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{T}_{{2}}$ </tex-math></inline-formula> mapping. To bridge the domain gap, realistic textures from a public database and various imperfection simulations were explored. The neural network was first trained with pure synthetic data and then evaluated with in vivo human brain. Both simulation and in vivo experiments show that the MOST-DL method significantly reduces ghosting and motion artifacts in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{T}_{{2}}$ </tex-math></inline-formula> maps in the presence of unpredictable subject movement and has the potential to be applied to motion-prone patients in the clinic. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/qinqinyang/MOST-DL</uri> .
Year
DOI
Venue
2022
10.1109/TMI.2022.3179981
IEEE Transactions on Medical Imaging
Keywords
DocType
Volume
Humans,Algorithms,Motion,Artifacts,Magnetic Resonance Imaging,Brain,Image Processing, Computer-Assisted
Journal
41
Issue
ISSN
Citations 
11
0278-0062
0
PageRank 
References 
Authors
0.34
24
15
Name
Order
Citations
PageRank
Qinqin Yang100.34
Yanhong Lin200.34
Jiechao Wang300.34
Jianfeng Bao400.34
Xiaoyin Wang500.34
Lingceng Ma600.34
Zihan Zhou700.34
Qizhi Yang800.34
Shuhui Cai9146.10
Hongjian He1000.68
Congbo Cai11105.90
Jiyang Dong12234.44
Jingliang Cheng1300.34
Zhong Chen1422521.56
Jianhui Zhong1500.34