No abstract

The deep learning-based automatic recognition of the scanning or exposing region in medical imaging automation is a promising new technique, which can decrease the heavy workload of the radiographers, optimize imaging workflow and improve image quality. However, there is little related research and practice in X-ray imaging. In this paper, we focus on two key problems in X-ray imaging automation: automatic recognition of the exposure moment and the exposure region. Consequently, we propose an automatic video analysis framework based on the hybrid model, approaching real-time performance. The framework consists of three interdependent components: Body Structure Detection, Motion State Tracing, and Body Modeling. Body Structure Detection disassembles the patient to obtain the corresponding body keypoints and body Bboxes. Combining and analyzing the two different types of body structure representations is to obtain rich spatial location information about the patient body structure. Motion State Tracing focuses on the motion state analysis of the exposure region to recognize the appropriate exposure moment. The exposure region is calculated by Body Mod-

MRI is a primary imaging method for the evaluation of pituitary adenomas (PAs). However, it is challenging to identify the postoperative residual tumors using traditional techniques. A recent approach Golden-angle Radial Sparse Parallel (GRASP)-DCE, and the novel simultaneous multi-slice (SMS) readout-segmented EPI technique (RESOLVE) procedures were performed to assess the residual tumors of postoperative PAs. And the residual tumors were observed as slow enhancement in GRASP images while manifesting as hyperintense in SMS-RESOLVE images. Thus, both of them are promising techniques for the evaluation of postoperative residual PAs.