医学
磁共振成像
核医学
轮廓
分割
自动化方法
放射科
人工智能
计算机科学
计算机图形学(图像)
作者
Daniel Stocker,Mustafa R. Bashir,Stephan Kannengießer,Cäcilia S. Reiner
出处
期刊:Journal of Computer Assisted Tomography
[Ovid Technologies (Wolters Kluwer)]
日期:2018-06-13
卷期号:42 (5): 697-706
被引量:11
标识
DOI:10.1097/rct.0000000000000759
摘要
This study aimed to evaluate the performance of an automated workflow of volumetric liver proton density fat fraction (PDFFvol) and R2* quantification with automated inline liver volume (LV) segmentation.Dual-echo and multiecho Dixon magnetic resonance images were evaluated in 74 consecutive patients (group A, PDFF < 10%; B, PDFF ≥ 10%; C, R2* ≥ 100 s; D, post-hemihepatectomy). The values of PDFFvol and R2*vol measurements across the LV were generated on multiecho images in an automated fashion based on inline liver segmentation on dual-echo images. Similar measurements were performed manually.Using the inline algorithm, the mis-segmented LV was highest in group D (80%). There were no significant differences between automated and manual measurements of PDFFvol. Automated R2*vol was significantly lower than manual R2*vol in group A (P = 0.004).Inline LV segmentation performed well in patients without and with hepatic steatosis. In cases with iron overload and post-hemihepatectomy, extrahepatic areas were erroneously included to a greater extent, with a tendency toward overestimation of PDFFvol.
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