Abstract #0605

The feasibility of an optimized Faster R-CNN in detection and differentiation HT from PTMC Using high b-value DWI with RESOLVE

ChengLong Deng^1,2, BingChao Wu^1,2, QingJun Wang³, QingLei Shi⁴, Bei Guan^1,2, DaCheng Qu⁵, and YongJi Wang*^1,2,6

¹Collaborative Innovation Center, Institute of Software, Chinese Academy of Sciences, Beijing, China, ²University of Chinese Academy of Sciences, Beijing, China, ³Department of Radiology, PLA 6th medical center, Beijing, China, ⁴MR Scientific Marketing, Siemens Healthcare, Beijing, China, ⁵School of Computer Science and Technology， Beijing Institute of Technology, Beijing, China, ⁶State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China

In this study, we optimized a Faster R-CNN algorithm through constraining anchor boxes generated by Region Proposal Network (RPN) based on prior knowledge, and evaluated the feasibility of the optimized model in detecting and differentiating Hashimoto's thyroiditis (HT) from papillary thyroid microcarcinomas (PTMC) based on high b-value (2000 sec/mm²) diffusion-weighted images that acquired with readout segmentation of long variable echo-trains (RESOLVE) sequence. The study indicated that our model based on high b-value (2000 sec/mm²) DWI images demonstrated great potential as a new inspection tool in the diagnosis of benign and malignant thyroid micronodules.

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