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NCT Number: NCT06589843

Artificial Intelligent Image Processing and Diagnosis of Pulmonary Vessels in CT

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.

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Key information

Age range

18 year–100 year

Sex eligibility

All sexes

Study type

Observational

About this study

A non-enhanced plain scan CT image simulates or reconstructs an image effect similar to that of CTPA through the following technical solutions:

  • Data acquisition: Obtain plain scan CT image data of the examined person, including multiple layers of image slices.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Deep learning imaging enhancement

Diagnostic Test

Conventional imaging or down-sampling imaging from CT or MR are enhanced by approved deep learning method.

Primary outcomes

  1. The performance of deep enhanced imaging in lesion detection and diagnosis

    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.

Study contacts

Contact information is provided by the study sponsor or research team.

Sponsors and collaborators

Lead sponsor

Xin Lou

Other

Registry information

Important dates

Study start
2024
Primary completion
2029
Study completion
2029
First posted
Sep 19, 2024
Registry last updated
Sep 19, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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