Radiology

The goal of this observational, retrospective and prospective study is to develop a noninvasive disease assessment system by leveraging artificial intelligence (AI) to comprehensively analyze multi-modal imaging features, including magnetic resonance enterography (MRE) and computed tomography enterography (CTE), for the diagnosis and prognostication of digestive diseases. To this end, the investigators retrospectively enrolled imaging, endoscopic, and clinical data from 21 centers across China to construct and iteratively optimize the AI model. The model's performance will be prospectively validated in two centers, and its accuracy in lesion localization will be verified through real-world deployment in endoscopy suites.

National multicentric registry of a cohort of patient with suffering parietal endometriosis, carried out by a multidisciplinary radiosurgery team

This research takes patients with cerebrovascular diseases as the research subjects and employs the experimental design of a prospective cohort study, aiming to innovatively develop and validate a "one-stop" multimodal CT examination scheme. The 301-CTAP multimodal CT technique implies that three modalities are acquired through a single collection, including head and neck CTA (head and neck CTA), multiphase CTA imaging of the brain (multiphase CTA), and CT perfusion imaging of the brain (CT perfusion).

In this study, patients with chest pain, lung cancer, pulmonary embolism, and routine inpatient physical examination were selected as the research objects, and the experimental design of retrospective cohort study was adopted to carry out artificial intelligence analysis related to pulmonary vascular diseases in patients with multi-dimensional big data. The multi-modal CT acquisition process included plain scan CT(NCCT) and CT pulmonary angiography (CTPA). Ctpa-like image effects can be simulated or reconstructed by non-enhanced plain scan CT images, so that CTPA-like image quality can be obtained without injecting contrast agent. The synthetic CTPA images were further analyzed by artificial intelligence to assist doctors in the intelligent diagnosis of pulmonary vascular diseases.

To investigate the performance of enhanced computed tomography (CT) or magnetic resonance (MR) imaging by deep learning relative to conventional CT or MR imaging in brain stroke and vascular neurology. We expect that the deep enhanced imaging method can shorten the time stay in the imaging session of stroke patients, optimize the overall imaging quality and improve the patients' care in imaging session.

In 2014 radiologic lesions were detected at one or more semicircular canals using CT and MR imaging of temporal bone in subjects carrying the p.P51S mutation in COCH. These lesions are believed to present at more advanced stages of the hearing and vestibular deterioration. Since then, other authors have described similar lesions in advanced non-genetic hearing and vestibular impairment as well. The purpose of this study is therefore to assess the radiologic investigation using CT and MR imaging of temporal bone to all subjects presenting with bilateral vestibulopathy, using the Barany criteria, compared to the p.P51S population.