UMIN-CTR Clinical Trial

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
Email	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
Email	saga@kuhp.kyoto-u.ac.jp

Sponsor
Institute	Kyoto University
Institute
Department

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
Email	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
URL(English)	https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000041817