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논문 기본 정보

자료유형
학술저널
저자정보
Jing Cheng (Chinese Academy of Sciences) Yuanyuan Liu (Chinese Academy of Sciences) Yanjie Zhu (Chinese Academy of Sciences) Dong Liang (Chinese Academy of Sciences)
저널정보
대한자기공명의과학회 Investigative Magnetic Resonance Imaging Investigative Magnetic Resonance Imaging 제25권 제4호
발행연도
2021.12
수록면
300 - 312 (13page)

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초록· 키워드

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Compressed sensing (CS) has been investigated in magnetic resonance (MR) parametric mapping to reduce scan time. However, the relatively long reconstruction time restricts its widespread applications in the clinic. Recently, deep learning based methods have shown great potential in accelerating reconstruction time and improving imaging quality in fast MR imaging, although their adaptation to parametric mapping is still in an early stage. In this paper, we proposed a novel deep learning based framework DEMO for fast and robust MR parametric mapping. Different from current deep learning-based methods, DEMO trains the network in an unsupervised way, which is more practical given that it is difficult to acquire large fully sampled training data of parametric-weighted images. Specifically, a CS-based loss function is used in DEMO to avoid the necessity of using fully sampled k-space data as the label, thus making it an unsupervised learning approach. DEMO reconstructs parametric weighted images and generates a parametric map simultaneously by unrolling an interaction approach in conventional fast MR parametric mapping, which enables multi-tasking learning. Experimental results showed promising performance of the proposed DEMO framework in quantitative MR T1ρ mapping.

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