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Clinical Application of a Low-Dose CBCT AI Model

Clinical Application of a Low-Dose CBCT AI Model

Recruiting
18 years and older
All
Phase N/A

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Overview

Cone Beam CT (CBCT) is an imaging modality used in interventional digital subtraction angiography (DSA). It produces three-dimensional images via cone-beam X-ray scanning and computer reconstruction. Clinically, CBCT guides puncture for pulmonary and hepatic lesions and evaluates post-intervention outcomes in liver cancer and cerebrovascular diseases. However, CBCT-guided interventions carry high patient radiation exposure; dose reduction often degrades image quality and impairs procedural results. Studies report that each 100 mGy radiation increment elevates cancer risk by 1.96-fold.

Artificial intelligence enables low-dose CBCT. Our prior DeepPriorCBCT model embedded anatomical priors using neural discrete representation learning, reducing thoracic CBCT dose to one-sixth of routine protocols while preserving image quality. We further developed DeepPriorCBCT-V2 using 55 000 pre-reconstruction CBCT datasets covering brain, thorax and abdomen. This multi-organ model maintains image quality at one-sixth standard radiation dose. Nevertheless, its real-world clinical performance remains unvalidated. We therefore designed this prospective multicenter randomized controlled trial to evaluate the clinical applicability of DeepPriorCBCT-V2.

Eligibility

Inclusion Criteria:

  • Age ≥18 years.
    • Requires CBCT-guided interventional treatment or surgery (e.g., cerebral angiography, lung puncture biopsy, hepatic artery chemoembolization, or percutaneous liver biopsy) and meets operational indications.
      • Can understand the study's purpose, procedures, potential risks, and benefits, and voluntarily signs a written informed consent form.

Exclusion Criteria:

  • History of high-dose radiation exams or treatments.
    • Known allergies or severe adverse reactions to iodine contrast agents or other relevant medications.
      • Pregnant or breastfeeding women.
        • Severe comorbidities or chronic diseases (e.g., severe diabetes, renal insufficiency).
          • Severe mental illness or cognitive impairment preventing understanding of the study procedures or providing informed consent.

            ⑥ Participants are not suitable for the trial per investigator's opinion.

Study details
    Cerebrovascular Diseases
    Lung
    Liver

NCT07845929

Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

20 October 2026

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FAQs

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