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化学交换饱和传递(CEST)MRI 的运动和磁场不均匀性校正技术:当代综述。

Motion and magnetic field inhomogeneity correction techniques for chemical exchange saturation transfer (CEST) MRI: A contemporary review.

机构信息

Russell H. Morgan Department of Radiology and Radiological Sciences, Division of MR Research, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA.

出版信息

NMR Biomed. 2025 Jan;38(1):e5294. doi: 10.1002/nbm.5294. Epub 2024 Nov 12.


DOI:10.1002/nbm.5294
PMID:39532518
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11606773/
Abstract

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) has emerged as a powerful imaging technique sensitive to tissue molecular composition, pH, and metabolic processes in situ. CEST MRI uniquely probes the physical exchange of protons between water and specific molecules within tissues, providing a window into physiological phenomena that remain invisible to standard MRI. However, given the very low concentration (millimolar range) of CEST compounds, the effects measured are generally only on the order of a few percent of the water signal. Consequently, a few critical challenges, including correction of motion artifacts and magnetic field (B and B ) inhomogeneities, have to be addressed in order to unlock the full potential of CEST MRI. Motion, whether from patient movement or inherent physiological pulsations, can distort the CEST signal, hindering accurate quantification. B and B inhomogeneities, arising from scanner hardware imperfections, further complicate data interpretation by introducing spurious variations in the signal intensity. Without proper correction of these confounding factors, reliable analysis and clinical translation of CEST MRI remain challenging. Motion correction methods aim to compensate for patient movement during (prospective) or after (retrospective) image acquisition, reducing artifacts and preserving data quality. Similarly, B and B inhomogeneity correction techniques enhance the spatial and spectral accuracy of CEST MRI. This paper aims to provide a comprehensive review of the current landscape of motion and magnetic field inhomogeneity correction methods in CEST MRI. The methods discussed apply to saturation transfer (ST) MRI in general, including semisolid magnetization transfer contrast (MTC) and relayed nuclear Overhauser enhancement (rNOE) studies.

摘要

化学交换饱和传递(CEST)磁共振成像(MRI)已成为一种强大的成像技术,能够敏感地探测组织的分子组成、pH 值以及原位代谢过程。CEST MRI 独特地探测了质子在水和组织内特定分子之间的物理交换,为标准 MRI 无法探测的生理现象提供了一个窗口。然而,由于 CEST 化合物的浓度非常低(毫摩尔范围),所测量的效果通常仅在水信号的几个百分点左右。因此,为了充分发挥 CEST MRI 的潜力,必须解决几个关键挑战,包括运动伪影和磁场(B 和 B )不均匀性的校正。运动,无论是来自患者的运动还是内在的生理脉动,都会扭曲 CEST 信号,阻碍准确的定量分析。B 和 B 不均匀性源于扫描仪硬件的不完善,通过在信号强度中引入虚假变化,进一步使数据解释复杂化。如果不对这些混杂因素进行适当的校正,那么 CEST MRI 的可靠分析和临床转化仍然具有挑战性。运动校正方法旨在补偿(前瞻性)或(回顾性)图像采集期间的患者运动,减少伪影并保持数据质量。同样,B 和 B 不均匀性校正技术增强了 CEST MRI 的空间和光谱准确性。本文旨在全面综述 CEST MRI 中运动和磁场不均匀性校正方法的现状。所讨论的方法适用于一般的饱和传递(ST)MRI,包括半固态磁化转移对比(MTC)和中继核奥弗豪瑟增强(rNOE)研究。

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引用本文的文献

[1]
Dynamic glucose enhanced imaging using direct water saturation.

Magn Reson Med. 2025-7

[2]
Dynamic Glucose Enhanced Imaging using Direct Water Saturation.

ArXiv. 2024-10-22

本文引用的文献

[1]
Data Consistent Deep Rigid MRI Motion Correction.

Proc Mach Learn Res. 2024

[2]
B inhomogeneity corrected CEST MRI based on direct saturation removed omega plot model at 5T.

Magn Reson Med. 2024-8

[3]
Fluid suppression in amide proton transfer-weighted (APTw) CEST imaging: New theoretical insights and clinical benefits.

Magn Reson Med. 2024-4

[4]
Improved postprocessing of dynamic glucose-enhanced CEST MRI for imaging brain metastases at 3 T.

Eur Radiol Exp. 2023-12-8

[5]
Deep learning based synthesis of MRI, CT and PET: Review and analysis.

Med Image Anal. 2024-2

[6]
Deep Learning-Based Denoising of CEST MR Data: A Feasibility Study on Applying Synthetic Phantoms in Medical Imaging.

Diagnostics (Basel). 2023-10-27

[7]
Reproducibility of APT-weighted CEST-MRI at 3T in healthy brain and tumor across sessions and scanners.

Sci Rep. 2023-10-23

[8]
Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review.

IEEE Trans Med Imaging. 2024-2

[9]
Hierarchical K-means clustering method for accelerated Lorentzian estimation (KALE) in chemical exchange saturation transfer-magnetic resonance imaging quantification.

Quant Imaging Med Surg. 2023-7-1

[10]
Learned spatiotemporal correlation priors for CEST image denoising using incorporated global-spectral convolution neural network.

Magn Reson Med. 2023-11

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