Deep Learning and Simulation for the Estimation of Red Blood Cell Flux With Optical Coherence Tomography

Ştefan, Sabina; Kim, Anna; Marchand, Paul J.; Lesage, Frédéric; Lee, Jong‐Hwan

doi:10.3389/fnins.2022.835773

Cited by 1 publication

(1 citation statement)

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“…In recent years, ML techniques have been applied to various microscale hemodynamics studies. Examples include the classification of RBC shapes ( 34 ), predicting RBC deformation and trajectory in microfluidic devices ( 35 ), estimation of cell deformability ( 36–38 ), fast processing of in vivo images ( 39 ), and estimating RBC flux in cortical capillary networks ( 40 ). ML was also used to integrate images of blood flow with underlying physical laws to infer the flow field in microaneurysm ( 41 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting capillary vessel network hemodynamics in silico by machine learning

Ebrahimi,

Bagchi

2024

PNAS Nexus

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Blood velocity and red blood cell (RBC) distribution profiles in a capillary vessel cross-section in the microcirculation are generally complex and do not follow Poiseuille’s parabolic or uniform pattern. Existing imaging techniques used to map large microvascular networks in vivo do not allow a direct measurement of full 3D velocity and RBC concentration profiles, although such information is needed for accurate evaluation of the physiological variables, such as the wall shear stress (WSS) and near-wall cell-free layer (CFL), that play critical roles in blood flow regulation, disease progression, angiogenesis, and hemostasis. Theoretical network flow models, often used for hemodynamic predictions in experimentally acquired images of the microvascular network, cannot provide the full 3D profiles either. In contrast, such information can be readily obtained from high-fidelity computational models that treat blood as a suspension of deformable RBCs. These models, however, are computationally expensive and not feasible for extension to the microvascular network at large spatial scales up to an organ level. To overcome such limitations, here we present machine learning (ML) models that bypass such expensive computations but provide highly accurate and full 3D profiles of the blood velocity, RBC concentration, WSS, and CFL in every vessel in the microvascular network. The ML models, which are based on artificial neural network and convolution-based U-net models, predict hemodynamic quantities that compare very well against the true data but reduce the prediction time by several orders. This study therefore paves the way for ML to make detailed and accurate hemodynamic predictions in spatially large microvascular networks at an organ-scale.

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