Initiative for the new medical MR imaging with sparse modeling

2017 Fiscal Year Final Research Report

• Project Area: Initiative for High-Dimensional Data-Driven Science through Deepening of Sparse Modeling
• Project/Area Number: 25120002
• Research Category: Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)
• Allocation Type: Single-year Grants
• Review Section: Complex systems
• Research Institution: Kyoto University
• Principal Investigator: TOGASHI Kaori 京都大学, 医学研究科, 教授 (90135484)
• Co-Investigator(Kenkyū-buntansha): 岡田 知久 京都大学, 医学研究科, 特定准教授 (30321607) ; 藤本 晃司 京都大学, 医学研究科, 特定助教 (10580110) ; 山本 憲 京都大学, 医学研究科, 助教 (60525567) ; 伏見 育崇 京都大学, 医学研究科, 助教 (90639014)
• Project Period (FY): 2013-06-28 – 2018-03-31
• Keywords: Sparse modeling / Compressed sensing / MRI / Stroke / Myocardial infarction / Cancer

Outline of Final Research Achievements

Current MRI examinations require long scan time to get various types of images with high resolution. This research aims at improving magnetic resonance imaging (MRI) of the three major adult diseases by using the sparse modeling (SpM), which is a new principle of data collection and image reconstruction. As results of this research, the images with high quality equivalent to the current standard could be obtained in less than half of the conventional scan time, confirmed in patients with cerebrovascular aneurysms and stenosis (Moyamoya disease). Myocardial movement was visualized with higher temporal resolution, and the coronary artery was observed in higher resolution using super-resolution based on SpM. Visualization of tumor blood vessels and differential diagnosis of benign or malignant tumor were improved. SpM could also be used to suppress the influence of body movement. These new methods are expected to improve our healthcare in near future.

Free Research Field

画像診断学