UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000036700 |
|---|---|
| Receipt number | R000041817 |
| Scientific Title | Noise reduction in magnetic resonance imaging by deep learning image reconstruction |
| Date of disclosure of the study information | 2019/08/19 |
| Last modified on | 2022/03/15 09:46:06 |
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Basic information
Public title
Noise reduction in magnetic resonance imaging by deep learning image reconstruction
Acronym
Noise reduction in magnetic resonance imaging
Scientific Title
Noise reduction in magnetic resonance imaging by deep learning image reconstruction
Scientific Title:Acronym
Noise reduction in magnetic resonance imaging
Region
| Japan |
Condition
Condition
MRI data obtained for berain, optic nearve, spine/bone/joint, breast and heart
Classification by specialty
| Radiology | Adult |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
To verify and optimize the noise reduction effect of MRI images by using deep learning image reconstruction
Basic objectives2
Efficacy
Basic objectives -Others
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
MRI images reconstructed by deep learning image reconstruction and those by conventional image reconstruction
Key secondary outcomes
Base
Study type
Observational
Study design
Basic design
Randomization
Randomization unit
Blinding
Control
Stratification
Dynamic allocation
Institution consideration
Blocking
Concealment
Intervention
No. of arms
Purpose of intervention
Type of intervention
Interventions/Control_1
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. Twenty years old or more at the time of informed consent
2. Signed informed consent is obtained from the participant or his/her representative
3. MRI of the target body area of the present clinical study is planned to be performed
Key exclusion criteria
1. When MRI data are regarded as inappropriate for evaluation by the investigators because of the image degradation by body movement during data aquisition and other reasons
2. Those who cannot understand the explanation of the research content
Target sample size
680
Research contact person
Name of lead principal investigator
| 1st name | Tsuneo |
| Middle name | |
| Last name | Saga |
Organization
Graduate School of Medicine, Kyoto University
Division name
Department of Advanced Medical Imaging Research
Zip code
606-8507
Address
54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan
TEL
075-751-3544
saga@kuhp.kyoto-u.ac.jp
Public contact
Name of contact person
| 1st name | Tsuneo |
| Middle name | |
| Last name | Saga |
Organization
Graduate School of Medicine, Kyoto University
Division name
Department of Advanced Medical Imaging Research
Zip code
606-8507
Address
54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan
TEL
075-751-3544
Homepage URL
saga@kuhp.kyoto-u.ac.jp
Sponsor or person
Institute
Kyoto University
Institute
Department
Personal name
Funding Source
Organization
Kyoto University
Organization
Division
Category of Funding Organization
Other
Nationality of Funding Organization
Other related organizations
Co-sponsor
CANON MEDICAL SYSTEMS CORPORATION
Name of secondary funder(s)
CANON MEDICAL SYSTEMS CORPORATION
IRB Contact (For public release)
Organization
Ethics Committee, Kyoto University Graduate School and Faculty of Medicine, Kyoto University Hospital
Address
Yoshidakonoe-cho, Sakyo-ku, Kyoto 606-8501, Japan
Tel
075-753-4680
ethcom@kuhp.kyoto-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
| 2019 | Year | 08 | Month | 19 | 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
| 2019 | Year | 05 | Month | 10 | Day |
Date of IRB
| 2019 | Year | 07 | Month | 31 | Day |
Anticipated trial start date
| 2019 | Year | 08 | Month | 01 | Day |
Last follow-up date
| 2022 | Year | 03 | Month | 14 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
By applying a newly developed image reconstruction method employing deep learning to MRI data obtained in a clinical MRI study, the efficacy of noise reduction is evaluated in qualitative and quantitative manner.
Management information
Registered date
| 2019 | Year | 05 | Month | 10 | Day |
Last modified on
| 2022 | Year | 03 | Month | 15 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000041817
