CNN 彩色编码差异图能准确显示肝脏磁共振成像-质子密度脂肪分数（MRI-PDFF）的纵向变化。
CNN color-coded difference maps accurately display longitudinal changes in liver MRI-PDFF.
作者信息
Hasenstab Kyle, Cunha Guilherme Moura, Ichikawa Shintaro, Dehkordy Soudabeh Fazeli, Lee Min Hee, Kim Soo Jin, Schlein Alexandra, Covarrubias Yesenia, Sirlin Claude B, Fowler Kathryn J
机构信息
Liver Imaging Group, Department of Radiology, University of California, San Diego, La Jolla, CA, USA.
Department of Mathematics and Statistics, San Diego State University, San Diego, CA, USA.
出版信息
Eur Radiol. 2021 Jul;31(7):5041-5049. doi: 10.1007/s00330-020-07649-0. Epub 2021 Jan 15.
OBJECTIVES
To assess the feasibility of a CNN-based liver registration algorithm to generate difference maps for visual display of spatiotemporal changes in liver PDFF, without needing manual annotations.
METHODS
This retrospective exploratory study included 25 patients with suspected or confirmed NAFLD, who underwent PDFF-MRI at two time points at our institution. PDFF difference maps were generated by applying a CNN-based liver registration algorithm, then subtracting follow-up from baseline PDFF maps. The difference maps were post-processed by smoothing (5 cm round kernel) and applying a categorical color scale. Two fellowship-trained abdominal radiologists and one radiology resident independently reviewed difference maps to visually determine segmental PDFF change. Their visual assessment was compared with manual ROI-based measurements of each Couinaud segment and whole liver PDFF using intraclass correlation (ICC) and Bland-Altman analysis. Inter-reader agreement for visual assessment was calculated (ICC).
RESULTS
The mean patient age was 49 years (12 males). Baseline and follow-up PDFF ranged from 2.0 to 35.3% and 3.5 to 32.0%, respectively. PDFF changes ranged from - 20.4 to 14.1%. ICCs against the manual reference exceeded 0.95 for each reader, except for segment 2 (2 readers ICC = 0.86-0.91) and segment 4a (reader 3 ICC = 0.94). Bland-Altman limits of agreement were within 5% across all three readers. Inter-reader agreement for visually assessed PDFF change (whole liver and segmental) was excellent (ICCs > 0.96), except for segment 2 (ICC = 0.93).
CONCLUSIONS
Visual assessment of liver segmental PDFF changes using a CNN-generated difference map strongly agreed with manual estimates performed by an expert reader and yielded high inter-reader agreement.
KEY POINTS
• Visual assessment of longitudinal changes in quantitative liver MRI can be performed using a CNN-generated difference map and yields strong agreement with manual estimates performed by expert readers.
目的
评估基于卷积神经网络（CNN）的肝脏配准算法生成差异图以直观显示肝脏质子密度脂肪分数（PDFF）时空变化的可行性，且无需手动标注。
方法
这项回顾性探索性研究纳入了25例疑似或确诊非酒精性脂肪性肝病（NAFLD）的患者，他们在我们机构的两个时间点接受了PDFF磁共振成像（MRI）检查。通过应用基于CNN的肝脏配准算法生成PDFF差异图，然后从基线PDFF图中减去随访时的图。差异图通过平滑处理（5厘米圆形内核）并应用分类颜色标度进行后处理。两名经过专科培训的腹部放射科医生和一名放射科住院医师独立审查差异图，以直观确定节段性PDFF变化。他们的视觉评估与使用组内相关系数（ICC）和布兰德-奥特曼分析对每个Couinaud肝段和全肝PDFF进行的基于手动感兴趣区（ROI）的测量进行比较。计算视觉评估的阅片者间一致性（ICC）。
结果
患者平均年龄为49岁（12名男性）。基线和随访时的PDFF分别为2.0%至35.3%和3.5%至32.0%。PDFF变化范围为-20.4%至14.1%。除第2段（2名阅片者ICC = 0.86 - 0.91）和第4a段（第3名阅片者ICC = 0.94）外，每名阅片者与手动参考值的ICC均超过0.95。所有三名阅片者的布兰德-奥特曼一致性界限均在5%以内。除第2段（ICC = 0.93）外，视觉评估的肝脏节段性（全肝和节段）PDFF变化的阅片者间一致性极佳（ICC > 0.96）。
结论
使用CNN生成的差异图对肝脏节段性PDFF变化进行视觉评估与专家阅片者的手动估计高度一致，且阅片者间一致性高。
关键点
• 使用CNN生成的差异图可对定量肝脏MRI的纵向变化进行视觉评估，且与专家阅片者的手动估计高度一致。
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