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Blood Flow

Blood Flow

Blood is pumped by the heart into the aorta, the largest artery in the body, and then into increasingly smaller arteries, arterioles, and capillaries. The velocity of blood flow decreases with increased cross-sectional blood vessel area. As blood returns to the heart through venules and veins, its velocity increases. The movement of blood is encouraged by smooth muscle in the vessel walls, the movement of skeletal muscle surrounding the vessels, and one-way valves that prevent backflow.

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Automated Measurement of Microcirculatory Blood Flow Velocity in Pulmonary Metastases of Rats

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Machine Learning for Aiding Blood Flow Velocity Estimation Based on Angiography.

Swati Padhee¹, Mark Johnson², Hang Yi²

¹Department of Computer Science and Engineering, Wright State University, Dayton, OH 45435, USA.

Bioengineering (Basel, Switzerland)

|November 10, 2022

Summary

This summary is machine-generated.

This study introduces a machine learning (ML) framework to estimate blood flow (hemodynamics) from angiography images, overcoming computational fluid dynamics (CFD) complexity. The ML approach significantly improves accuracy for clinical applications.

Keywords:

angiography cardiovascular computational fluid dynamics (CFD)convolutional neural networks (CNN)dye perfusion hemodynamics least absolute shrinkage and selection operator (LASSO)machine learning (ML)optical flow method (OFM)particle image velocimetry (PIV)

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Area of Science:

Biomedical Engineering
Medical Imaging
Computational Science

Background:

Computational fluid dynamics (CFD) is complex for clinical use.
Hemodynamic analysis is crucial for diagnosing vascular diseases.
Angiography provides visual data of blood vessels.

Purpose of the Study:

To develop an efficient and accurate machine learning (ML) framework for estimating hemodynamics from angiography images.
To provide a clinically applicable alternative to complex CFD simulations.
To validate the ML framework's performance against CFD data.

Main Methods:

Mimicked blood flow and X-ray imaging using dye diffusion in CFD simulations.
Estimated initial velocity fields using the optical flow method (OFM).
Trained ML models (LASSO, CNN) using CFD velocity data as ground truth and OFM as input.

Main Results:

ML models achieved high accuracy, surpassing predefined error criteria (MAE < 3 × 10^-3 m/s, MSE < 5 × 10^-7 m/s).
The ML framework reduced v-velocity estimation error from 53.5% to 2.5% compared to CFD.
Validated models demonstrated significant improvements in predicting hemodynamic information.

Conclusions:

The proposed ML framework offers an efficient and accurate method for hemodynamic estimation from angiography.
This approach can support clinical diagnosis by providing reliable hemodynamic insights.
The ML framework presents a viable alternative to complex CFD for clinical settings.