The University of Tokyo · Medicine
Professor Koichiro Yasaka's research lab specializes in medical image analysis and artificial intelligence, focusing on advancing diagnostic accuracy in abdominal and neurological radiology using deep learning and quantitative imaging techniques. The lab investigates the application of convolutional neural networks (CNNs) for liver mass characterization, fibrosis staging, and improved image reconstruction, particularly in CT and MR imaging. Key research directions include radiomics, image noise reduction, and the development of AI tools to support radiologists in clinical decision-making. The lab also explores the impact of imaging protocols and reconstruction methods on texture analysis and diagnostic performance.
Figures are computed from collected data and may differ slightly.
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
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