NCT05614193

Deep Enhanced Imaging in Stroke and Vascular Neurology

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 1,000 (estimated)
• Sponsor: Chinese PLA General Hospital · Academic / Other
• Sex: All
• Age: 18 Years – 100 Years
• Healthy volunteers: Not accepted

Summary

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.

Detailed description

Early diagnosis of cerebral infarction, detection of ischemic penumbra, evaluation of collateral circulation and identification of vascular lesions by imaging are critical for treatment decision and outcome improvement in cerebral stroke. Multimodal computed tomography (CT) and magnetic resonance (MR) imaging are most prevalent and accessible approaches in clinical scenarios. These two approaches are downgraded either by radiation exposure or long scanning time which may hinder the rapid treatment for patients. Deep learning has shown substantial achievements in medical imaging enhancement. The added value of deep learning method in stroke and vascular neurology has not been thoroughly validated. In this study, we aimed 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.

Conditions

• Radiology
• Cerebral Stroke
• Vascular Diseases

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	Deep learning imaging enhancement	Conventional imaging or down-sampling imaging from CT or MR are enhanced by approved deep learning method.

Timeline

• Start date: 2022-12-01
• Primary completion: 2027-12-31
• Completion: 2027-12-31
• First posted: 2022-11-14
• Last updated: 2023-02-08

Locations

1 site across 1 country: China