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DeepPriorCBCT for Low Dose Lung CBCT Reconstruction

A Deep-learning System for Low-Dose CBCT Image Reconstruction: a Stepwise, Multicentre, Early-stage Clinical Validation Study

NCT07035977•Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

View on ClinicalTrials.gov

Summary

The goal of this clinical trial is to learn if the DeepPriorCBCT model ensure high-quality CBCT image while reducing CBCT radiation dose. The main questions it aims to answer are: * Can DeepPriorCBCT reduce CBCT radiation doses? * Is the image quality of DeepPriorCBCT reconstruction consistent with that of existing clinical reconstruction method? Researchers will compare CBCT images quality reconstruction with DeepPriorCBCT model to CBCT image quality reconstruction with existing clinical protocols to see if DeepPriorCBCT model can improve the CBCT image quality while reducing radiation dose. Participants will: * Receive a single low dose (1/6 of existing clinical protocols) CBCT scan * Receive a single conventional dose CBCT scan

Eligibility

Age

18 - No limit years

Sex

ALL

Healthy Volunteers

Inclusion Criteria

•1. Participants will receive percutaneous thoracic puncture under the guidance of CBCT during the study period.
•2. The participants' medical records containing basic information, medical history, etc are complete.
•3. Participants can understand the study's purpose, procedures, potential risks, and benefits, and voluntarily signs a written informed consent form.

Exclusion Criteria

•1. Participants are under the age of 18.
•2. Participants are suffering from severe mental illness or cognitive impairment, unable to understand the study and sign.
•3. Participants are pregnant or breastfeeding women.
•4. Participants are concurrent participating in other programs that affect the results of this study.

Locations (5)

The First Affiliated Hospital East Campus of Zhengzhou University

Zhengzhou, Henan, China

The First Affiliated Hospital of Zhengzhou University

Zhengzhou, Henan, China

Wuhan Union Hospital

Wuhan, Hubei, China

Wuhan Union Jinyin Lake Hospital

Wuhan, Hubei, China

Wuhan Union West Hospital

Wuhan, Hubei, China

Key Dates

Start Date

May 15, 2025

Primary Completion Date

July 15, 2025

Completion Date

July 20, 2025

Last Updated

December 18, 2025

Enrollment

138

ACTUAL participants

Conditions

Lung Nodules

Interventions

Participants will receive a 1/6 views undersampling lung CBCT scan.

RADIATION

Shape-Sensing Robotic-Assisted Bronchoscopy for Diagnosis of Peripheral Pulmonary Nodules in Korea

NCT07057648

Lung NodulesPeripheral Lung Lesions+3 more

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UNKNOWN

AI Determine Malignancy of GGO on Chest CT

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Lung Nodules, Early Lung Cancer, Artificial Intelligence, Chest CT, Minimally Invasive Surgery, Lung Image Analysis Software

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Data Source & Attribution

This clinical trial information is sourced from ClinicalTrials.gov, a service of the U.S. National Institutes of Health.

ClinicalTrials.gov last update: December 18, 2025Data synced to Clareo: March 29, 2026

Modifications: This data has been reformatted for display purposes. Eligibility criteria have been parsed into inclusion/exclusion sections. Location data has been geocoded to enable distance-based search. For the authoritative and most current information, please visit ClinicalTrials.gov.

Neither the United States Government nor Clareo Health make any warranties regarding the data. Check ClinicalTrials.gov frequently for updates.

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DeepPriorCBCT for Low Dose Lung CBCT Reconstruction

Inclusion Criteria

Exclusion Criteria

Shape-Sensing Robotic-Assisted Bronchoscopy for Diagnosis of Peripheral Pulmonary Nodules in Korea

AI Determine Malignancy of GGO on Chest CT

3-D Reconstruction of CT Scan Images in the Evaluation of Non-Specific Pulmonary Nodules