Songeon Julien, Lazeyras François, Agius Thomas, Dabrowski Oscar, Ruttimann Raphael, Toso Christian, Longchamp Alban, Klauser Antoine, Courvoisier Sebastien
Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
CIBM Center for Biomedical Imaging, University Hospital of Geneva, Bd de la Tour 8, 1205, Geneva, Switzerland.
MAGMA. 2025 Apr;38(2):161-173. doi: 10.1007/s10334-024-01218-y. Epub 2024 Dec 27.
Phosphorus-31 magnetic resonance spectroscopic imaging (P-MRSI) is a non-invasive tool for assessing cellular high-energy metabolism in-vivo. However, its acquisition suffers from a low sensitivity, which necessitates large voxel sizes or multiple averages to achieve an acceptable signal-to-noise ratio (SNR), resulting in long scan times.
To overcome these limitations, we propose an acquisition and reconstruction scheme for FID-MRSI sequences. Specifically, we employed Compressed Sensing (CS) and Low-Rank (LR) with Total Generalized Variation (TGV) regularization in a combined CS-LR framework. Additionally, we used a novel approach to k-space undersampling that utilizes distinct pseudo-random patterns for each average. To evaluate the proposed method's performance, we performed a retrospective analysis on healthy volunteers' brains and ex-vivo perfused kidneys.
The presented method effectively improves the SNR two-to-threefold while preserving spectral and spatial quality even with threefold acceleration. We were able to recover signal attenuation of anatomical information, and the SNR improvement was obtained while maintaining the metabolites peaks linewidth.
We presented a novel combined CS-LR acceleration and reconstruction method for FID-MRSI sequences, utilizing a unique approach to k-space undersampling. Our proposed method has demonstrated promising results in enhancing the SNR making it applicable for reducing acquisition time.
磷-31磁共振波谱成像(P-MRSI)是一种用于在体内评估细胞高能代谢的非侵入性工具。然而,其采集存在灵敏度低的问题,这需要大的体素尺寸或多次平均才能获得可接受的信噪比(SNR),从而导致扫描时间长。
为克服这些限制,我们提出了一种用于FID-MRSI序列的采集和重建方案。具体而言,我们在组合的CS-LR框架中采用了压缩感知(CS)和具有全广义变分(TGV)正则化的低秩(LR)方法。此外,我们使用了一种新颖的k空间欠采样方法,该方法在每次平均时利用不同的伪随机模式。为评估所提出方法的性能,我们对健康志愿者的大脑和离体灌注肾脏进行了回顾性分析。
所提出的方法有效地将SNR提高了两到三倍,同时即使在加速三倍的情况下仍能保持光谱和空间质量。我们能够恢复解剖学信息的信号衰减,并且在保持代谢物峰线宽的同时获得了SNR的提高。
我们提出了一种用于FID-MRSI序列的新颖的组合CS-LR加速和重建方法,利用独特的k空间欠采样方法。我们提出的方法在提高SNR方面已显示出有前景的结果,使其适用于减少采集时间。