DE LA MODÉLISATION À LA QUANTIFICATION PAR ULTRASONS DE L’AGRÉGATION ÉRYTHROCYTAIRE (original) (raw)
Many studies have reported that an enhanced level of red blood cell aggregation is associated with the presence of hemorheological disorders. Pathological aggregation has been characterized by quantitative ultrasound based on the backscattering coefficient. In order to describe the interaction between the incident ultrasound and the interrogated biological tissues, mathematical models are used. Mathematical modeling is known to be the optimal way to describe the interaction occurring between ultrasound and tissues at the cellular level. The structure factor model (SFM), considered as the exact scattering model has been developed to predict the backscattering coefficient from blood. However, the numerical SFM cannot be applied in real time for practical measurements and does not provide aggregate size to assess the level of aggregation. Therefore, we come up with a new model based on the effective medium theory in order to tackle this difficulty. The effective medium theory combined with the structure factor model (EMTSFM) can be applied in real time and contrary to the SFM provides two indices of the aggregate state in vivo: aggregate size and compactness. Based on a 3D simulation study, the backscattering coefficients (BSCs) predicted by the effective medium theory combined with the Structure Factor Model (EMTSFM) are compared to the BSCs computed with SFM. Our aim here is to assess the accuracy of the EMTSFM against the SFM by comparing their BSC in the framework of a forward problem, i.e., the calculation of the BSC from the known acoustic and structure aggregate parameters. This was done in order to validate the proposed model. To simulate aggregates, RBCs are stacked following a hexagonal close packing scheme. The influences of the aggregate radius and compactness on the BSC are studied as well. The results showed good agreement between the SFM and the EMTSFM based on our simulated microstructure of RBC aggregates. Our work provides thus the theoretical background to assess locally the aggregation level for diagnosis purposes.
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