Automated Nasopharyngeal Carcinoma Detection with Dynamic Gadolinium-Enhanced MR Imaging

Abstract

Summary Objectives: The purpose of this research is to develop an automatic medical diagnosis for segmenting nasopharyngeal carcinoma (NPC) with dynamic gadolinium-enhanced MR imaging. Methods: This system is a multistage process, involving motion correction, head mask generation, dynamic MR data quantitative evaluation, rough segmentation, and rough segmentation refinement. Two approaches, a relative signal increase method and a slope method, are proposed for the quantitative evaluation of dynamic MR data. Results: The NPC detection results obtained using the proposed methods had a rating of 85% in match percent compared with these lesions identified by an experienced radiologist. The match percent for the two proposed methods did not have significant differences. However, the computation cost for the slope method was about twelve times faster than the relative signal increase method. Conclusions: The proposed methods can identify the NPC regions quickly and effectively. This system can enhance the performance of clinical diagnosis.