The University of Tokyo · 의학
Koichiro Yasaka 교수의 연구실은 의료 영상 진단의 정밀도를 향상시키기 위해 딥 러닝 기반의 복합 신경망(CNN) 기술을 응용한 영상 분석 연구를 주요 과제로 삼고 있습니다. 특히 간 종양의 정밀 구분, 간섬유화 단계 예측, 그리고 MRI 및 CT 영상에서의 질병 특징 자동 진단에 초점을 맞추고 있으며, 영상 품질 향상 기술과도 연계하여 임상적 활용가능성을 높이고자 합니다. 연구는 영상의학, 인공지능, 방사선학의 융합을 기반으로 한 정밀의료 기반 기술 개발을 추구합니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Purpose To investigate diagnostic performance by using a deep learning method with a convolutional neural network (CNN) for the differentiation of liver masses at dynamic contrast agent-enhanced computed tomography (CT). Materials and Methods This clinical retrospective study used CT image sets of liver masses over three phases (noncontrast-agent enhanced, arterial, and delayed). Masses were diagnosed according to five categories (category A, classic hepatocellular carcinomas [HCCs]; category B,
Radiological imaging diagnosis plays important roles in clinical patient management. Deep learning with convolutional neural networks (CNNs) is recently gaining wide attention for its high performance in recognizing images. If CNNs realize their promise in the context of radiology, they are anticipated to help radiologists achieve diagnostic excellence and to enhance patient healthcare. Here, we discuss very recent developments in the field, including studies published in the current PLOS Medici
Purpose To investigate the performance of a deep convolutional neural network (DCNN) model in the staging of liver fibrosis using gadoxetic acid-enhanced hepatobiliary phase magnetic resonance (MR) imaging. Materials and Methods This retrospective study included patients for whom input data (hepatobiliary phase MR images, static magnetic field of the imaging unit, and hepatitis B and C virus testing results available, either positive or negative) and reference standard data (liver fibrosis stage
MBIR significantly improves image noise and streak artifacts compared to ASIR, and can achieve radiation dose reduction without severely compromising image quality.
Quantitative computed tomography (CT) texture analyses for images with and without filtration are gaining attention to capture the heterogeneity of tumors. The aim of this study was to investigate how quantitative texture parameters using image filtering vary among different computed tomography (CT) scanners using a phantom developed for radiomics studies.A phantom, consisting of 10 different cartridges with various textures, was scanned under 6 different scanning protocols using four CT scanner
DLR enables the production of higher quality 1.5 T intracranial MRA images with improved visualization of arteries.
Patients with PD can be differentiated from healthy controls by applying the deep learning technique to the parameter-weighted connectome matrices, and neural circuit disorders including those between the basal ganglia on one side and the cerebellum on the contralateral side were visualized.
SEMAR-A could provide images with lesser metal artefact and better depiction of structures than AIDR 3D and FIRST.
Background Abdominal computed tomography (CT) without arm elevation is associated with degraded image quality due to streak artifacts. Purpose To compare the degree of streak artifacts in abdominal CT images without arm elevation between full iterative reconstruction (IR), hybrid IR, and filtered back projection (FBP) using two commercially available scanners. Material and Methods First, a phantom study simulating CT examination without arm elevation was performed. Second, unenhanced axial image
