東京大学 · 医学
Kato教授の研究室は、脳の微細構造変化を高精度に可視化する多パラメトリック画像診断技術の開発を柱としています。特に、3D-QALASやNODDIを用いた脳全体の定量的画像解析、ならびに画像コントラストの最適化と画像認識技術の統合による疾患の原因解析を進めています。また、脳腫瘍や神経炎症性疾患の早期診断を支援する画像支援技術の開発も実施しており、臨床応用に向けた実用的で革新的な研究が特徴です。
Figures are computed from collected data and may differ slightly.
Multiparametric imaging of the whole brain based on 3D-QALAS can be accelerated using CS while preserving tissue quantitative values, tissue segmentation, and quality of synthetic images.
FW imaging and NODDI were useful for identifying the etiology of neurodegeneration- and neuroinflammation-related microstructural changes in RRMS and NMOSD patients.
The increased ICVF in the BMS group may represent myelination and/or astrocytic hypertrophy, and microstructural changes in the amygdala in GBSS analysis indicate the emotional-affective profile of BMS.
CAD improved BM detection sensitivity on NECT without increasing FPs or reading time among less experienced radiologists, but this was not the case among experienced radiologists.
Computer-aided detection significantly improved BM detection sensitivity without prolonging reading time while marginally increased the false positives.
Mesenteric lymph node injection improved the efficacy of TD CT lymphangiography in mice. Mesenteric injection provided significantly better TD visualization than popliteal injection. Enhanced TD visualization in mice advances preclinical research on lymphatic diseases.
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