Zhong Jingyu, Xing Yue, Hu Yangfan, Liu Xianwei, Dai Shun, Ding Defang, Lu Junjie, Yang Jiarui, Song Yang, Lu Minda, Nickel Dominik, Lu Wenjie, Zhang Huan, Yao Weiwu
Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
Shanghai Key Laboratory of Flexible Medical Robotics, Tongren Hospital, Institute of Medical Robotics, Shanghai Jiao Tong University, 200336, Shanghai, China.
J Imaging Inform Med. 2025 Jun 25. doi: 10.1007/s10278-025-01503-9.
The objective of this study is to investigate the impact of deep learning reconstruction and accelerated acquisition on reproducibility and variability of radiomic features in abdominal MRI. Seventeen volunteers were prospectively included to undergo abdominal MRI on a 3-T scanner for axial T2-weighted, axial T2-weighted fat-suppressed, and coronal T2-weighted sequences. Each sequence was scanned for four times using clinical reference acquisition with standard reconstruction, clinical reference acquisition with deep learning reconstruction, accelerated acquisition with standard reconstruction, and accelerated acquisition with deep learning reconstruction, respectively. The regions of interest were drawn for ten anatomical sites with rigid registrations. Ninety-three radiomic features were extracted via PyRadiomics after z-score normalization. The reproducibility was evaluated using clinical reference acquisition with standard reconstruction as reference by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). The variability among four scans was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). Our study found that the median (first and third quartile) of overall ICC and CCC values were 0.451 (0.305, 0.583) and 0.450 (0.304, 0.582). The overall percentage of radiomic features with ICC > 0.90 and CCC > 0.90 was 8.1% and 8.1%, and was considered acceptable. The median (first and third quartile) of overall CV and QCD values was 9.4% (4.9%, 17.2%) and 4.9% (2.5%, 9.7%). The overall percentage of radiomic features with CV < 10% and QCD < 10% was 51.9% and 75.0%, and was considered acceptable. Without respect to clinical significance, deep learning reconstruction and accelerated acquisition led to a poor reproducibility of radiomic features, but more than a half of the radiomic features varied within an acceptable range.
本研究的目的是探讨深度学习重建和加速采集对腹部MRI中影像组学特征的可重复性和变异性的影响。前瞻性纳入17名志愿者，在3-T扫描仪上进行腹部MRI检查，扫描轴向T2加权、轴向T2加权脂肪抑制和冠状T2加权序列。每个序列分别使用标准重建的临床参考采集、深度学习重建的临床参考采集、标准重建的加速采集和深度学习重建的加速采集进行4次扫描。通过刚性配准在10个解剖部位绘制感兴趣区域。经z分数标准化后，通过PyRadiomics提取93个影像组学特征。以标准重建的临床参考采集为参照，采用组内相关系数（ICC）和一致性相关系数（CCC）评估可重复性。通过变异系数（CV）和四分位数离散系数（QCD）评估4次扫描之间的变异性。我们的研究发现，总体ICC和CCC值的中位数（第一和第三四分位数）分别为0.451（0.305，0.583）和0.450（0.304，0.582）。ICC>0.90和CCC>0.90的影像组学特征的总体百分比为8.1%和8.1%，被认为是可接受的。总体CV和QCD值的中位数（第一和第三四分位数）为9.4%（4.9%，17.2%）和4.9%（2.5%，9.7%）。CV<10%和QCD<10%的影像组学特征的总体百分比为51.9%和75.0%，被认为是可接受的。不考虑临床意义，深度学习重建和加速采集导致影像组学特征的可重复性较差，但超过一半的影像组学特征在可接受范围内变化。
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