Computational approaches to detect small lesions in (18) F-FDG PET/CT scans

利用计算方法检测(18)F-FDG PET/CT扫描中的微小病灶

阅读:1

Abstract

PURPOSE: When physicians interpret (18) F-FDG PET/CT scans, they rely on their subjective visual impression of the presence of small lesions, the criteria for which may vary among readers. Our investigation used physical phantom scans to evaluate whether image texture analysis metrics reliably correspond to visual criteria used to identify lesions and accurately differentiate background regions from sub-centimeter simulated lesions. METHODS: Routinely collected quality assurance test data were processed retrospectively for 65 different (18) F-FDG PET scans performed of standardized phantoms on eight different PET/CT systems. Phantoms included 8-, 12-, 16-, and 25-mm diameter cylinders embedded in a cylindrical water bath, prepared with 2.5:1 activity-to-background ratio emulating typical whole-body PET protocols. Voxel values in cylinder regions and background regions were sampled to compute several classes of image metrics. Two experienced physicists, blinded to quantified image metrics and to each other's readings, independently graded cylinder visibility on a 5-level scale (0 = definitely not visible to 4 = definitely visible). RESULTS: The three largest cylinders were visible in 100% of cases with a mean visibility score of 3.3 ± 1.2, while the smallest 8-mm cylinder was visible in 58% of cases with a significantly lower mean visibility score of 1.5±1.1 (P < 0.0001). By ROC analysis, the polynomial-fit signal-to-noise ratio was the most accurate at discriminating 8-mm cylinders from the background, with accuracy greater than visual detection (93% ± 2% versus 76% ± 4%, P = 0.0001), and better sensitivity (94% versus 58%, P < 0.0001). CONCLUSION: Image texture analysis metrics are more sensitive than visual impressions for detecting sub-centimeter simulated lesions. Therefore, image texture analysis metrics are potentially clinically useful for (18) F-FDG PET/CT studies.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。