Deep learning imaging enhancement
Diagnostic TestConventional imaging or down-sampling imaging from CT or MR are enhanced by approved deep learning method.
NCT Number: NCT06589843
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.
Trial opening soon.
Get Notified18 year–100 year
All sexes
Observational
A non-enhanced plain scan CT image simulates or reconstructs an image effect similar to that of CTPA through the following technical solutions:
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Conventional imaging or down-sampling imaging from CT or MR are enhanced by approved deep learning method.
Time frame: 2 year
The performance of deep enhanced imaging in lesion detection and diagnosis, including imaging quality, accuracy, sensitivity and specificity in lesion detection and imaging diagnosis.
Contact information is provided by the study sponsor or research team.
Xin Lou
Other
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