Schmitt Joachim, Weidlich Dominik, Weiss Kilian, Stelter Jonathan, Montagnese Federica, Deschauer Marcus, Schoser Benedikt, Zimmer Claus, Karampinos Dimitrios C, Kirschke Jan S, Schlaeger Sarah
Department of Diagnostic and Interventional Neuroradiology, School of Medicine and Health, TUM Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
Department of Diagnostic and Interventional Radiology, School of Medicine and Health, TUM Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
PLoS One. 2025 Apr 16;20(4):e0318599. doi: 10.1371/journal.pone.0318599. eCollection 2025.
Quantitative muscle water T2 (T2w) mapping is regarded as a biomarker for disease activity and response to treatment in neuromuscular diseases (NMD). However, the implementation in clinical settings is limited due to long scanning times and low resolution. Using artificial intelligence (AI) to accelerate MR image acquisition offers a possible solution. Combining compressed sensing and parallel imaging with AI-based reconstruction, known as CSAI (SmartSpeed, Philips Healthcare), allows for the generation of high-quality, weighted MR images in a shorter scan time. However, CSAI has not yet been investigated for quantitative MRI. Therefore, in the present work we assessed the performance of CSAI acceleration for T2w mapping compared to standard acceleration with SENSE.
T2w mapping of the thigh muscles, based on T2-prepared 3D TSE with SPAIR fat suppression, was performed using standard SENSE (acceleration factor of 2; 04:35 min; SENSE) and CSAI (acceleration factor of 5; 01:57 min; CSAI 5x) in ten patients with facioscapulohumeral muscular dystrophy (FSHD). Subjects were scanned in two consecutive sessions (14 days in between). In each dataset, six regions of interest were placed in three thigh muscles bilaterally. SENSE and CSAI 5x acceleration were compared for i) image quality using apparent signal- and contrast-to-noise ratio (aSNR/aCNR), ii) diagnostic agreement of T2w values, and iii) intra- and inter-session reproducibility.
aSNR and aCNR of SENSE and CSAI 5x scans were not significantly different (p > 0.05). An excellent agreement of SENSE and CSAI 5x T2w values was shown (r = 0.99; ICC = 0.992). T2w mapping with both acceleration methods showed excellent, matching intra-method reproducibility.
AI-based acceleration of CS data allows for scan time reduction of more than 50% for T2w mapping in the thigh muscles of NMD patients without compromising quantitative validity.
定量肌肉水T2（T2w）成像被视为神经肌肉疾病（NMD）疾病活动和治疗反应的生物标志物。然而，由于扫描时间长和分辨率低，其在临床环境中的应用受到限制。使用人工智能（AI）加速磁共振图像采集提供了一种可能的解决方案。将压缩感知和平行成像与基于AI的重建相结合，即CSAI（SmartSpeed，飞利浦医疗保健公司），可以在更短的扫描时间内生成高质量的加权磁共振图像。然而，CSAI尚未用于定量磁共振成像研究。因此，在本研究中，我们评估了与使用SENSE的标准加速方法相比，CSAI加速在T2w成像中的性能。
对10例面肩肱型肌营养不良症（FSHD）患者的大腿肌肉进行基于T2准备的3D TSE序列并采用SPAIR脂肪抑制技术的T2w成像，分别使用标准SENSE（加速因子为2；04:35分钟；SENSE）和CSAI（加速因子为5；01:57分钟；CSAI 5x）。受试者在两个连续的时间段（间隔14天）进行扫描。在每个数据集中，在双侧三块大腿肌肉中放置六个感兴趣区域。比较SENSE和CSAI 5x加速方法在以下方面的表现：i）使用表观信号和对比度噪声比（aSNR/aCNR）评估图像质量，ii）T2w值的诊断一致性，以及iii）时间段内和时间段间的可重复性。
SENSE和CSAI 5x扫描的aSNR和aCNR无显著差异（p>0.05）。SENSE和CSAI 5x的T2w值显示出极好的一致性（r = 0.99；ICC = 0.992）。两种加速方法的T2w成像均显示出极好的、匹配的方法内可重复性。
基于AI对CS数据进行加速，可使NMD患者大腿肌肉T2w成像的扫描时间减少50%以上，且不影响定量有效性。
