NANADec 24, 2018

On the stable estimation of flow geometry and wall shear stress from magnetic resonance images

arXiv:1812.098485 citationsh-index: 3
Originality Synthesis-oriented
AI Analysis

For researchers in medical imaging and hemodynamics, this provides a theoretically grounded method to improve reliability of flow-derived metrics, though it is incremental over existing regularization approaches.

The paper proposes a systematic reconstruction procedure for stable estimation of flow geometry and wall shear stress from MRI measurements, with quantified error bounds under smoothness assumptions, and demonstrates viability on experimental data.

We consider the stable reconstruction of flow geometry and wall shear stress from measurements obtained by magnetic resonance imaging. As noted in a review article by Petersson, most approaches considered so far in the literature seem not be satisfactory. We therefore propose a systematic reconstruction procedure that allows to obtain stable estimates of flow geometry and wall shear stress and we are able to quantify the reconstruction errors in terms of bounds for the measurement errors under reasonable smoothness assumptions. A full analysis of the approach is given in the framework of regularization methods. In addition, we discuss the efficient implementation of our method and we demonstrate its viability, accuracy, and regularizing properties for experimental data.

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