Artificial Intelligent Image Processing and Diagnosis of Pulmonary Vessels in CT
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Deep learning imaging enhancement.
- Who it may be relevant to
- Registry conditions: Radiology, Vascular Diseases. Basic parameters: 18 years — 100 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
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.
Detailed description
A non-enhanced plain scan CT image simulates or reconstructs an image effect similar to that of CTPA through the following technical solutions:
1. Data acquisition: Obtain plain scan CT image data of the examined person, including multiple layers of image slices. 2. Image preprocessing: Preprocessing of plain scan CT images, including denoising, enhancing contrast and other steps, to improve image quality and lay the foundation for subsequent processing. 3. Vascular segmentation: Advanced image segmentation algorithms, such as the deep learning-based segmentation method, are used to segment the vascular structure from the preprocessed plain scan CT images. The key to this step is to accurately identify and extract vascular areas while reducing interference from non-vascular tissue. 4. Blood vessel enhancement: For the segmented blood vessel structure, a specific image enhancement algorithm is used to enhance blood vessels to make them clearer and more continuous. 5. Image synthesis: The enhanced vascular image is fused with the original plain scan CT image to generate the final CTPA image. During the synthesis process, the contrast between blood vessels and surrounding tissues can be adjusted as needed to optimize the display effect. 6. Post-processing and evaluation: Post-processing of synthesized CTPA images, such as smoothing, artifact removal, etc., and quality assessment to ensure that the images meet the needs of clinical diagnosis.
Interventions
- Diagnostic test Deep learning imaging enhancement
Conventional imaging or down-sampling imaging from CT or MR are enhanced by approved deep learning method.
Primary outcome measures
- The performance of deep enhanced imaging in lesion detection and diagnosis [Time frame: 2 year]
Eligibility criteria
Inclusion criteria
- Age ≥18≤100 years old Scan the pulmonary artery and its major branches Patients with suspected pulmonary embolism who received CTPA had a set of CTPA and CT scans The image quality meets the requirements of diagnosis and post-processing Patients who completed the examination in accordance with the data collection criteria Clinical data and follow-up were complete
Exclusion criteria
- Age \<18 years or age \>100 years The image is incomplete or incorrect Pulmonary artery absent or underenhanced Severe motion artifacts or image noise affect evaluation of pulmonary embolism History of aortic reconstruction, replacement, or stent implantation Congenital variations in the whole or important branches of the aorta in adults (e.g. bovine aortic arch, abnormal right subclavian artery) Severe hypovolemia and hemodynamic instability Severe heart failure with low ejection fraction Dialysis patient
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Case-only
Study locations
Center list to be confirmed — check the primary protocol.
Identifiers
NCT: NCT06589843 · NCCT-CTPA AI