Marka Alexander W, Meurer Felix, Twardy Vanessa, Graf Markus, Ebrahimi Ardjomand Saba, Weiss Kilian, Makowski Marcus R, Gersing Alexandra S, Karampinos Dimitrios C, Neumann Jan, Woertler Klaus, Banke Ingo J, Foreman Sarah C
Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, TUM Klinikum, Technical University of Munich (TUM), Ismaninger Str. 22, Munich 81675, Germany.
Musculoskeletal Radiology Section, School of Medicine and Health, TUM Klinikum, Technical University of Munich, Ismaninger Str. 22, Munich 81675, Germany.
Eur J Radiol Open. 2025 May 2;14:100656. doi: 10.1016/j.ejro.2025.100656. eCollection 2025 Jun.
To evaluate a Compressed Sense Artificial Intelligence framework (CSAI) incorporating parallel imaging, compressed sense (CS), and deep learning for high-resolution MRI of the hip, comparing it with standard-resolution CS imaging.
Thirty-two patients with femoroacetabular impingement syndrome underwent 3 T MRI scans. Coronal and sagittal intermediate-weighted TSE sequences with fat saturation were acquired using CS (0.6 ×0.8 mm resolution) and CSAI (0.3 ×0.4 mm resolution) protocols in comparable acquisition times (7:49 vs. 8:07 minutes for both planes). Two readers systematically assessed the depiction of the acetabular and femoral cartilage (in five cartilage zones), labrum, ligamentum capitis femoris, and bone using a five-point Likert scale. Diagnostic confidence and abnormality detection were recorded and analyzed using the Wilcoxon signed-rank test.
CSAI significantly improved the cartilage depiction across most cartilage zones compared to CS. Overall Likert scores were 4.0 ± 0.2 (CS) vs 4.2 ± 0.6 (CSAI) for reader 1 and 4.0 ± 0.2 (CS) vs 4.3 ± 0.6 (CSAI) for reader 2 (p ≤ 0.001). Diagnostic confidence increased from 3.5 ± 0.7 and 3.9 ± 0.6 (CS) to 4.0 ± 0.6 and 4.1 ± 0.7 (CSAI) for readers 1 and 2, respectively (p ≤ 0.001). More cartilage lesions were detected with CSAI, with significant improvements in diagnostic confidence in certain cartilage zones such as femoral zone C and D for both readers. Labrum and ligamentum capitis femoris depiction remained similar, while bone depiction was rated lower. No abnormalities detected in CS were missed in CSAI.
CSAI provides high-resolution hip MR images with enhanced cartilage depiction without extending acquisition times, potentially enabling more precise hip cartilage assessment.
评估一种结合并行成像、压缩感知（CS）和深度学习的压缩感知人工智能框架（CSAI）用于髋关节高分辨率磁共振成像（MRI），并将其与标准分辨率的CS成像进行比较。
32例股骨髋臼撞击综合征患者接受了3T MRI扫描。在可比的采集时间内（两个平面均为7分49秒对8分07秒），使用CS（分辨率0.6×0.8毫米）和CSAI（分辨率0.3×0.4毫米）协议采集了冠状位和矢状位脂肪饱和的中等加权快速自旋回波（TSE）序列。两名阅片者使用五点李克特量表系统地评估髋臼和股骨软骨（在五个软骨区域）、盂唇、股骨头韧带和骨骼的显示情况。使用Wilcoxon符号秩检验记录并分析诊断信心和异常检测情况。
与CS相比，CSAI在大多数软骨区域显著改善了软骨显示。阅片者1的总体李克特评分分别为4.0±0.2（CS）对4.2±0.6（CSAI），阅片者2为4.0±0.2（CS）对4.3±0.6（CSAI）（p≤0.001）。阅片者1和2的诊断信心分别从3.5±0.7和3.9±0.6（CS）提高到4.0±0.6和4.1±0.7（CSAI）（p≤0.001）。CSAI检测到更多的软骨病变，在某些软骨区域如两名阅片者的股骨C区和D区，诊断信心有显著提高。盂唇和股骨头韧带的显示保持相似，而骨骼显示的评分较低。CSAI未遗漏CS检测到的任何异常。
CSAI可提供高分辨率的髋关节MR图像，在不延长采集时间的情况下增强了软骨显示，有可能实现更精确的髋关节软骨评估。
