2023 Fiscal Year Final Research Report

Elucidating pathophysiology of normal pressure hydrocephalus using a novel fluid dynamic analysis of cerebrospinal fluid

Research Project

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Project/Area Number	21K09098
Research Category	Grant-in-Aid for Scientific Research (C)
Allocation Type	Multi-year Fund
Section	一般
Review Section	Basic Section 56010:Neurosurgery-related
Research Institution	Nagoya City University (2022-2023) Shiga University of Medical Science (2021)
Principal Investigator	Yamada Shigeki 名古屋市立大学, 医薬学総合研究院(医学), 講師 (40422969)
Co-Investigator(Kenkyū-buntansha)	渡邉嘉之滋賀医科大学, 医学部, 教授 (20362733) 大島まり東京大学, 大学院情報学環・学際情報学府, 教授 (40242127) 野崎和彦滋賀医科大学, 医学部, 客員教授 (90252452)
Project Period (FY)	2021-04-01 – 2024-03-31
Keywords	4D Flow MRI / IVIM MRI / 脳脊髄液 / 脳老化シミュレーション / 脳体積 / 流体力学 / AI / 画像解析
Outline of Final Research Achievements	We proposed oscillatory shear stress (OSS) for 4D Flow MRI. Additionally, the f-value (%) calcutated by IVIM MRI serves as a quantitative measure of the subtle pulsatile motion of CSF. Furthermore, we found that the subarachnoid space gradually enlarges with age in normal volunteers, while ventricular size remains stable until age 60. However, after the age of 60, ventricles enlarge along with the enlargement of the foramina of Magendi and Luschka. Moreover, we developed the first AI model capable of automatically extracting four regions from 3D MRI scans: the CSF space, ventricles, high parietal convexity subarachnoid space, and the Sylvian fissure and basal cisterns. Furthermore, based on these extracted subregions, we developed the second AI model that identifies Disproportionately Enlarged Subarachnoid-space Hydrocephalus (DESH), ventricular enlargement, tightened sulci in the high convexity region, and dilatation of the Sylvian fissure.
Free Research Field	脳神経外科学関連
Academic Significance and Societal Importance of the Research Achievements	本研究の成果により、ハキム病（iNPH）に特徴的なDisproportionately Enlarged Subarachnoid-space Hydrocephalus (DESH)と脳室拡大をきたす原因に関連するCSF動態の加齢性変化を明らかにすることができた。また、脳MRI画像からDESHの判定に重要な領域を自動で抽出し、DESHの自動判定に加えて、DESHの程度や特徴を数値化した。これらのAIモデルを社会実装し、診断・治療の地域偏在を減らし（AIによる医療の均てん化）、高齢者の生活自立の向上や健康寿命の延伸に貢献する。