The University of Tokyo · 의학
Ryusuke Irie 교수의 연구실은 뇌 영상 진단의 정밀도를 높이기 위해 고해상도 MRI 기반의 양적 영상 분석과 인공지능 기술을 융합한 연구를 주도하고 있습니다. 특히, 무소음 MRA, 싱커틱 MRI, 딥러닝 기반 혈관 영상 생성 및 신경조직 미세구조 평가 기술 개발에 초점이 맞춰져 있으며, 뇌질환의 조기 진단 및 정밀 분류에 기여하고자 합니다. 다양한 연령대의 뇌 조직 특성 변화를 기반으로 한 기준값 설정도 연구의 핵심 요소입니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Silent MRA was able to visualize flow in an intracranial stent more effectively than TOF MRA. Silent MRA might be useful for follow-up imaging after stent-assisted coil embolization, though these study results may be only preliminary due to some limitations.
Our study showed changes according to age in tissue characteristic values and brain volumes using quantitative synthetic MRI. The reference values for age demonstrated in this study may be useful to discriminate brain disorders from healthy brains.
Magnetic resonance angiography generated by deep learning from 3D synthetic MRI data visualized major intracranial arteries as effectively as TOF-MRA, with inherently aligned quantitative maps and multiple contrast-weighted images. Our proposed algorithm may be useful as a screening tool for intracranial aneurysms without requiring additional scanning time.
Residual extraction approach in a deep learning method achieved a high accuracy for the differential diagnosis of iNPH, AD, and healthy controls trained with a small number of cases.
Objective The aim of this study was to evaluate the reproducibility of computed tomography (CT)–derived fractional flow reserve (FFR) determined on site by inexperienced observers using a postprocessing software based on structural and fluid analysis. Methods Using 21 coronary vessels in 7 patients who underwent 320-row coronary CT angiography and catheter-FFR, 2 independent inexperienced observers (A: a student radiation technologist; B: a nonmedical staff) determined the CT-FFR using a postpro
DKI accurately reflected neurite density in the area with crossing fibers, potentially allowing evaluation of complex microstructures.
This pilot study tests the feasibility of rapid carotid MR angiography using the liver acquisition with volume acceleration-flex technique (LAVA MRA). Seven healthy volunteers and 21 consecutive patients suspected of carotid stenosis underwent LAVA and conventional time-of-flight (cTOF) MRAs. Artery-to-fat and artery-to-muscle signal intensity ratios were manually measured. LAVA MRA exhibited a significantly larger artery-to-fat signal intensity ratio compared with cTOF MRA in all slices (P < 0.
Liver acquisition with volume acceleration-flex (LAVA-Flex) acquires out-of-phase and in-phase echo images and automatically generates water-only and fat-only images from one single acquisition. The scan time of carotid MR angiography (MRA) using LAVA-Flex (LAVA MRA) is about one-fifth that of conventional time-of-flight MRA (cTOF MRA). We aimed to investigate whether LAVA MRA could provide useful information for the diagnosis of carotid plaque by utilizing the ability to acquire multiple sequen
Purpose: The Prostate Imaging Reporting and Data System (PI-RADS) was introduced to standardize prostate cancer diagnosis by MRI. However, the inter-reader agreement by PI-RADS scoring is not always high. The purpose of this study was to validate a deep-learning-based diagnostic algorithm of PI-RADS. Methods: We applied a Siemens Healthineers Prostate Artificial Intelligence (AI) prototype (work in progress) for fully automated prostate lesion detection, classification and reporting. More than 2