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

NCT ID: NCT06769867

Last Updated: 2025-03-18

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Clinical Phase

NA

Total Enrollment

134 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-04-15

Study Completion Date

2025-05-31

Brief Summary

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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

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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.

Conditions

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Cerebrovascular Disease

Study Design

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Allocation Method

NON_RANDOMIZED

Intervention Model

CROSSOVER

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

TRIPLE

Participants Investigators Outcome Assessors

Study Groups

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PS-3D-DSA

undergo a PS-3D-DSA scan

Group Type EXPERIMENTAL

PS-3D-DSA

Intervention Type RADIATION

undergo a PS-3D-DSA scan

classic 3D-DSA

undergo a classic 3D-DSA scan

Group Type SHAM_COMPARATOR

classic 3D-DSA

Intervention Type RADIATION

undergo a classic 3D-DSA scan

Interventions

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PS-3D-DSA

undergo a PS-3D-DSA scan

Intervention Type RADIATION

classic 3D-DSA

undergo a classic 3D-DSA scan

Intervention Type RADIATION

Eligibility Criteria

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Inclusion Criteria

1. Age ≥18 years.
2. Requires 3D-DSA-guided interventional diagnosis or treatment (e.g., cerebral angiography, cerebral artery chemoembolization) and meets operational indications.
3. Can understand the study's purpose, procedures, potential risks, and benefits, and voluntarily signs a written informed consent form.

Exclusion Criteria

1. Severe heart or lung disease, such as heart failure or chronic obstructive pulmonary disease (COPD).
2. History of high-dose radiation exams or treatments.
3. Known allergies or severe adverse reactions to iodine contrast agents or other relevant medications.
4. Pregnant or breastfeeding women.
5. Severe comorbidities or chronic diseases (e.g., severe diabetes, renal insufficiency).
6. Severe mental illness or cognitive impairment preventing understanding of the study procedures or providing informed consent.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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

OTHER

Sponsor Role lead

Responsible Party

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Yaowei Bai

MD

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Wuhan Union Hospital

Wuhan, Hubei, China

Site Status

Countries

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China

Central Contacts

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Huangxuan Zhao, PhD

Role: CONTACT

+86 18627162379

References

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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 subtraction angiography for detection of macrovascular causes of intracerebral haemorrhage: prospective, multicentre cohort study. BMJ. 2015 Nov 9;351:h5762. doi: 10.1136/bmj.h5762.

Reference Type BACKGROUND
PMID: 26553142 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 37556901 (View on PubMed)

Other Identifiers

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Patient-Specific Generative AI

Identifier Type: -

Identifier Source: org_study_id

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