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Anzai Lab
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A lab that analyzes blood flow with computation and machine learning to help diagnose and predict disease.

AboutAbout the lab
Visualizing blood flow computationally to advance diagnosis and treatment
The Anzai Lab studies blood flow with computer-based analysis, with the aim of helping to diagnose and predict disease.
In computational fluid dynamics (CFD) analysis, the lab calculates flow velocity and pressure to visualize how blood flows.
In machine learning, the lab predicts blood flow instantly from images and builds standard vessel geometries that show diseases quantitatively and objectively.
The lab develops technology jointly with clinical sites and medical device manufacturers, and also accepts international students and exchanges with overseas universities.
Key terms
- CFD analysis
- A method that calculates flow velocity and pressure through computer simulation to study how a fluid flows
- Machine learning
- Technology in which computers learn from large amounts of data to classify things or predict numerical values. Deep learning is one type
- Shunt vessel
- A vessel created in the forearm by connecting a vein and an artery to secure the blood volume needed for hemodialysis
ResearchResearch themes
By solving blood flow computationally, the lab works on predicting diseases related to blood flow and developing treatments. It also aims to support the diagnosis and prediction of disease by combining medical images with machine learning.
*Illustrative imageImproving the field of view in angioscopy
In angioscopy, a transparent fluid (flush) is released from the catheter tip to displace blood for observation, but the volume is limited and the vessel wall often cannot be seen. Using CFD analysis, the lab investigates how release conditions affect the field of view and proposes optimal conditions.
*Illustrative imageHow droplets form in the airway
In airborne and droplet transmission, viruses leave the body carried by the flow of exhaled air. The lab simulates how and in what quantity droplets form inside the body, and analyzes and visualizes their behavior.
*Illustrative imageAnalysis of dialysis shunt vessels
In hemodialysis, a shunt vessel is created in the forearm by connecting an artery and a vein, but blood flow becomes abnormal at the junction, making stenosis more likely. The lab analyzes blood flow with CFD analysis and aims to propose vessel shapes in which stenosis is less likely to occur.
*Illustrative imagePredicting blood flow with deep learning
Blood flow measurement has insufficient resolution, and CFD analysis takes a long time to compute. Using deep learning, the lab is developing technology that instantly estimates the flow field, at a resolution comparable to CFD, for vessel geometries built from medical images.
MembersFaculty
- Associate ProfessorHitomi Anzai
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Incompressible Flow in a Venturi Injector” SimScale GmbH / CC BY-SA 4.0 (cropped)
- Improving the field of view in angioscopy: “Central venous catheter set” Mikael Häggström / CC0 (cropped)
- How droplets form in the airway: “Sneeze” James Gathany / Public domain (cropped)
- Analysis of dialysis shunt vessels: “Dialysis - arm - 01” Anna Frodesiak / CC0 (cropped)
- Predicting blood flow with deep learning: “CSIRO ScienceImage 11313 The CSIRO GPU cluster at the data centre” division, CSIRO / CC BY 3.0 (cropped)
- Related lab (太田 研究室): “Cobalt Chromium Coronary Stent in Heart” ETTon / CC BY-SA 4.0 (cropped)
- Related lab (石川 研究室): “Blood Vessel (NIH BioArt 55)” Courtesy of NIAID Ryan Kissinger / Public domain (cropped)
- Related lab
(菊地 研究室): “Titan models red blood cells ( )” Oak Ridge National Laboratory / CC BY 2.0 (cropped) - Related lab (大森 研究室): “Cardiovascular system - Blood flow 1 -- Smart-Servier” Laboratoires Servier / CC BY-SA 3.0 (cropped)
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