Ignite Creation Date:
2025-12-24 @ 11:35 PM
Ignite Modification Date:
2025-12-25 @ 9:25 PM
Study NCT ID:
NCT06372756
Status:
RECRUITING
Last Update Posted:
2024-04-18
First Post:
2024-04-11
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Deep Learning Reconstruction Algorithms in Dual Low-dose CTA
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 1200}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2023-06-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-04', 'completionDateStruct': {'date': '2026-03', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-04-16', 'studyFirstSubmitDate': '2024-04-11', 'studyFirstSubmitQcDate': '2024-04-16', 'lastUpdatePostDateStruct': {'date': '2024-04-18', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-04-18', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-12', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'The specificity and sensitivity calculated through the optimal cutoff value of the receiver operating characteristic curve.', 'timeFrame': '2026.1', 'description': 'The specificity and sensitivity were calculated separately for the standard dose group and the double low-dose group using the optimal cutoff value from the receiver operating characteristic curve, for the purpose of comparing diagnostic accuracy between the two groups.'}], 'secondaryOutcomes': [{'measure': 'The signal-to-noise ratio calculated from image CT values and noise', 'timeFrame': '2026.1', 'description': 'The signal-to-noise ratio was calculated separately for the standard dose group and the double low-dose group using image CT values and noise, to assess the image quality between the two groups.'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Deep Learning']}, 'descriptionModule': {'briefSummary': 'The goal of this observational study is to evaluate the impact of deep learning image reconstruction on the image quality and diagnostic performance of double low-dose CTA. The main question it aims to answer is to explore the feasibility of deep learning image reconstruction in double low-dose CTA.', 'detailedDescription': '1. The raw data from patients who underwent head and neck CTA, coronary CTA, and abdominal CTA in both standard dose and double low-dose groups were included.\n2. Techniques such as filtered back projection, iterative reconstruction, and deep learning reconstruction were performed.\n3. The feasibility of deep learning reconstruction in double low-dose CTA was evaluated based on image quality and diagnostic performance.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '90 Years', 'minimumAge': '18 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Healthy or diseased adults undergoing CT vascular imaging', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Patients with head and neck CTA, coronary artery CTA, and abdominal CTA due to stroke, coronary heart disease and abdominal inflammatory disease, and abdominal tumors.\n\nExclusion Criteria:\n\n* Age \\<18 years, pregnancy, allergic reaction to iodine contrast agent, renal insufficiency, and severe hyperthyroidism.'}, 'identificationModule': {'nctId': 'NCT06372756', 'briefTitle': 'Deep Learning Reconstruction Algorithms in Dual Low-dose CTA', 'organization': {'class': 'OTHER', 'fullName': 'Tongji Hospital'}, 'officialTitle': 'Evaluation of Deep Learning Reconstruction Algorithms in Dual Low-dose CT Vascular Imaging', 'orgStudyIdInfo': {'id': '102122'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Standard dose group', 'description': 'Raw data from 400 patients with conventional dose head and neck CTA, coronary CTA, and abdominal CTA were included. Filtered back-projection, iteration, and deep learning reconstruction were performed. To evaluate the impact of deep learning reconstruction on image quality and diagnostic performance in patients with conventional dose CTA.', 'interventionNames': ['Diagnostic Test: Deep learning image reconstruction']}, {'label': 'Double low dose group', 'description': 'Raw data from 800 patients with low tube voltage and contrast medium head and neck CTA, coronary CTA, and abdominal CTA were included. Filtered back-projection, iteration, and deep learning reconstruction were performed. To evaluate the impact of deep learning reconstruction on image quality and diagnostic performance in patients with double-low-dose CTA.', 'interventionNames': ['Diagnostic Test: Deep learning image reconstruction']}], 'interventions': [{'name': 'Deep learning image reconstruction', 'type': 'DIAGNOSTIC_TEST', 'description': 'Deep learning image reconstruction (DLIR) is a newly developed artificial intelligence noise reduction algorithm in recent years. It trains massive high-quality FBP data sets to learn to distinguish noise and signal, so as to selectively reduce noise and reconstruct high-quality images with low-quality image data.', 'armGroupLabels': ['Double low dose group', 'Standard dose group']}]}, 'contactsLocationsModule': {'locations': [{'zip': '430000', 'city': 'Wuhan', 'state': 'Hubei', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Youfa M Tang', 'role': 'CONTACT', 'email': '1525573397@qq.com', 'phone': '+8613554101223'}], 'facility': 'Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology', 'geoPoint': {'lat': 30.58333, 'lon': 114.26667}}], 'centralContacts': [{'name': 'Youfa M Tang, Doctor', 'role': 'CONTACT', 'email': '1525573397@qq.com', 'phone': '8613554101223'}, {'name': 'Tan, Doctor', 'role': 'CONTACT', 'email': '1655118783@qq.com', 'phone': '86 159 2631 4149'}], 'overallOfficials': [{'name': 'Hao Tang, Doctor', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Tongji Hospital'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': "To protect the participant privacy, the relevant data is not shared until the participants' consent"}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Hao Tang', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR_INVESTIGATOR', 'investigatorTitle': 'associate chief physician', 'investigatorFullName': 'Hao Tang', 'investigatorAffiliation': 'Tongji Hospital'}}}}