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Not yet recruiting NCT06769867

(Withdrawal) AI-Based Low-Dose 3D-DSA Reconstruction

No phase Interventional Cerebrovascular Disease

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: PS-3D-DSA, classic 3D-DSA.
Who it may be relevant to
Registry conditions: Cerebrovascular Disease. Basic parameters: from 18 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
China
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

Unsure about the terms? Read our patient guide →

Official title

(Withdrawal) Validation of a Patient-Specific Generative AI-Based Low-Dose Cerebrovascular 3D-DSA Image Reconstruction Method: A Stepwise, Multicenter, Randomized Crossover Trial

Overview

If the participants agree to participate in this study, the participants will undergo two scans (classic 3D-DSA and PS-3D-DSA assisted scan) to compare the imaging effects of both. After the procedure, the investigators will record the radiation exposure and collect DSA images.

Detailed description

Although several previous studies have used deep learning methods to reduce 3D-DSA radiation dose, no prospective clinical trial had yet validated the practical application of these models. Herein, the investigators introduce a patient-specific generative AI-based low-dose cerebrovascular 3D-DSA image reconstruction method (PS-3D-DSA) to reconstruct 3D-DSA images from ultra-sparse 2D projection views and a prospective cohort is used to validate its efficacy in clinical practice.

Interventions

  • Radiation PS-3D-DSA
    undergo a PS-3D-DSA scan
  • Radiation classic 3D-DSA
    undergo a classic 3D-DSA scan

Primary outcome measures

  • The radiation dose received by patients during interventional procedures when using two scanning protocols (classic 3D-DSA and PS-3D-DSA) [Time frame: No more than 6 hours]
Secondary outcome measures (1)
  • The image diagnostic capabilities using two scanning protocols (classic 3D-DSA and PS-3D-DSA) [Time frame: No more than 1 month]

Eligibility criteria

Inclusion criteria

  • Age ≥18 years.
  • Requires 3D-DSA-guided interventional diagnosis or treatment (e.g., cerebral angiography, cerebral artery chemoembolization) 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

  • Severe heart or lung disease, such as heart failure or chronic obstructive pulmonary disease (COPD).
  • 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.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Allocation
Non-randomized
Model
Crossover
Masking
Triple blind
Primary purpose
Diagnostic

Study locations

China · 1 center
  • Wuhan Union Hospital — Wuhan

Publications

  • van Asch CJ, Velthuis BK, Rinkel GJ, Algra A, de Kort GA, Witkamp TD, de Ridder JC, van Nieuwenhuizen KM, de Leeuw FE, Schonewille WJ, de Kort PL, Dippel DW, Raaymakers TW, Hofmeijer J, Wermer MJ, Kerkhoff H, Jellema K, Bronner IM, Remmers MJ, Bienfait HP, Witjes RJ, Greving JP, Klijn CJ; DIAGRAM Investigators. Diagnostic yield and accuracy of CT angiography, MR angiography, and digital subtractio PMID 26553142
  • Irfan M, Malik KM, Ahmad J, Malik G. StrokeNet: An automated approach for segmentation and rupture risk prediction of intracranial aneurysm. Comput Med Imaging Graph. 2023 Sep;108:102271. doi: 10.1016/j.compmedimag.2023.102271. Epub 2023 Jul 22. PMID 37556901

Identifiers

NCT: NCT06769867 · Patient-Specific Generative AI

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗