UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000053064 |
|---|---|
| Receipt number | R000060547 |
| Scientific Title | Combining deep learning reconstruction and compressive sensing investigation of the usefulness of high-speed coronary MRA imaging method |
| Date of disclosure of the study information | 2023/12/20 |
| Last modified on | 2025/02/04 09:42:41 |
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Basic information
Public title
Investigation of the usefulness of high-speed coronary MRA imaging with AI
Acronym
Investigation of high-speed coronary MRA imaging method
Scientific Title
Combining deep learning reconstruction and compressive sensing investigation of the usefulness of high-speed coronary MRA imaging method
Scientific Title:Acronym
A study of coronary MRA high-speed imaging method using AI and CS in combination
Region
| Japan |
Condition
Condition
myocardial infarction, angina pectoris
Classification by specialty
| Cardiology | Radiology |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
Coronary MRA using the newly introduced deep learning reconstruction method combined with compressed sensing is expected to significantly reduce examination time while maintaining good image quality due to improved image reconstruction techniques. The purpose of this study is to compare and discuss the two methods.
Basic objectives2
Efficacy
Basic objectives -Others
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
Image quality evaluation
Key secondary outcomes
Inspection time
Base
Study type
Interventional
Study design
Basic design
Single arm
Randomization
Non-randomized
Randomization unit
Blinding
Open -no one is blinded
Control
Self control
Stratification
Dynamic allocation
Institution consideration
Blocking
Concealment
Intervention
No. of arms
1
Purpose of intervention
Diagnosis
Type of intervention
| Device,equipment |
Interventions/Control_1
The CS coronary MRA data will be anonymized and transferred to a dedicated server in collaboration with GE Healthcare Japan. The imaging data will be reconstructed using deep learning and transferred again to the dedicated server. The image quality and imaging time of conventional coronary MRA and CS coronary MRA with deep learning reconstruction will be compared. The examination time is expected to be about 30 minutes.
Interventions/Control_2
Interventions/Control_3
Interventions/Control_4
Interventions/Control_5
Interventions/Control_6
Interventions/Control_7
Interventions/Control_8
Interventions/Control_9
Interventions/Control_10
Eligibility
Age-lower limit
| 20 | years-old | <= |
Age-upper limit
| Not applicable |
Gender
Male and Female
Key inclusion criteria
1) healthy volunteer cases who are 20 years of age or older and have no previous coronary artery disease
2) persons who have been informed about the research in advance and have given their consent
Key exclusion criteria
1) persons who could not give research consent
2) claustrophobic person
Target sample size
40
Research contact person
Name of lead principal investigator
| 1st name | Kohei |
| Middle name | |
| Last name | Tabo |
Organization
Ehime University Hospital
Division name
radiology
Zip code
791-0295
Address
454 Shitsukawa, Toon, Ehime, Japan
TEL
089-960-5371
tabo.kohei.xt@ehime-u.ac.jp
Public contact
Name of contact person
| 1st name | Kohei |
| Middle name | |
| Last name | Tabo |
Organization
Ehime University Hospital
Division name
radiology
Zip code
791-0295
Address
454 Shitsukawa, Toon, Ehime, Japan
TEL
089-960-5371
Homepage URL
tabo.kohei.xt@ehime-u.ac.jp
Sponsor or person
Institute
Ehime University
Institute
Department
Personal name
Funding Source
Organization
None
Organization
Division
Category of Funding Organization
Self funding
Nationality of Funding Organization
Other related organizations
Co-sponsor
Name of secondary funder(s)
IRB Contact (For public release)
Organization
Institutional Review Board,Ehime University Hospital
Address
Address 454 Shitsukawa, Toon, Ehime
Tel
089-960-5172
rinri@m.ehime-u.ac.jp
Secondary IDs
Secondary IDs
NO
Study ID_1
Org. issuing International ID_1
Study ID_2
Org. issuing International ID_2
IND to MHLW
Institutions
Institutions
Other administrative information
Date of disclosure of the study information
| 2023 | Year | 12 | Month | 20 | Day |
Related information
URL releasing protocol
Publication of results
Unpublished
Result
URL related to results and publications
Number of participants that the trial has enrolled
Results
Results date posted
Results Delayed
Results Delay Reason
Date of the first journal publication of results
Baseline Characteristics
Participant flow
Adverse events
Outcome measures
Plan to share IPD
IPD sharing Plan description
Progress
Recruitment status
Completed
Date of protocol fixation
| 2023 | Year | 12 | Month | 11 | Day |
Date of IRB
| 2023 | Year | 11 | Month | 27 | Day |
Anticipated trial start date
| 2024 | Year | 01 | Month | 05 | Day |
Last follow-up date
| 2024 | Year | 12 | Month | 31 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
Management information
Registered date
| 2023 | Year | 12 | Month | 11 | Day |
Last modified on
| 2025 | Year | 02 | Month | 04 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000060547
