Automated nasopharyngeal carcinoma detection with dynamic gadolinium-enhanced MR imaging

Objectives. The purpose of this research is to develop an automatic medical diagnosis for segmenting nasopharyngeal carcinoma (NPC) with dynamic gadol inium-enhanced MR imaging. Methods: This system is a multistage process, involving motion correction, head mask generation, dynamic MR data quantitative evaluation, rough segmen tation, and rough segmentation refinement. Two approaches, a relative signa l increase method and a slope method, are proposed for the quantitative eva luation 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 increa se method. Conclusions. The proposed methods can identify the NPC regions quickly and effectively. This system can enhance the performance of clinical diagnosis.