Lam Fan, Peng Xi, Liang Zhi-Pei
Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801 USA.
Beckman Institute for Advanced Science and Technology, Department of Electrical and Computer Engineering and Cancer Center at Illinois, University of Illinois Urbana-Champaign.
IEEE Signal Process Mag. 2023 Mar;40(2):101-115. doi: 10.1109/msp.2022.3203867. Epub 2023 Feb 27.
Magnetic resonance spectroscopic imaging (MRSI) offers a unique molecular window into the physiological and pathological processes in the human body. However, the applications of MRSI have been limited by a number of long-standing technical challenges due to high dimensionality and low signal-to-noise ratio (SNR). Recent technological developments integrating physics-based modeling and data-driven machine learning that exploit unique physical and mathematical properties of MRSI signals have demonstrated impressive performance in addressing these challenges for rapid, high-resolution, quantitative MRSI. This paper provides a systematic review of these progresses in the context of MRSI physics and offers perspectives on promising future directions.
磁共振波谱成像（MRSI）为了解人体生理和病理过程提供了一个独特的分子窗口。然而，由于高维度和低信噪比（SNR），MRSI的应用一直受到一些长期存在的技术挑战的限制。最近，将基于物理的建模和数据驱动的机器学习相结合的技术发展，利用了MRSI信号独特的物理和数学特性，在应对这些挑战以实现快速、高分辨率、定量MRSI方面展现出了令人印象深刻的性能。本文在MRSI物理的背景下对这些进展进行了系统综述，并对未来有前景的方向提出了展望。
