Enhancing MR vascular Fingerprinting through realistic microvascular geometries

TL;DR

This paper improves MR vascular Fingerprinting by incorporating realistic 3D microvascular geometries into simulations, leading to more accurate mapping of microvascular parameters and better correlation with in vivo measurements.

Contribution

It introduces the use of 3D realistic vascular geometries in simulations for MR Fingerprinting, enhancing the accuracy of microvascular parameter estimation.

Findings

Realistic vascular models improve microvascular estimates.

Tumoral blood oxygenation estimates correlate with in vivo measurements.

Method outperforms simple 2D or cylindrical models.

Abstract

MR vascular Fingerprinting proposes to use the MR Fingerprinting framework to quantitatively and simultaneously map several microvascular characteristics at a sub-voxel scale. The initial implementation assessed the local blood oxygenation saturation (SO 2), blood volume fraction (BVf) and vessel averaged radius (R) in humans and rodent brains using simple 2D representations of the vascular network during dictionary generation. In order to improve the results and possibly extend the approach to pathological environments and other biomarkers, we propose in this study to use 3D realistic vascular geometries in the numerical simulations. 28,000 different synthetic voxels containing vascular networks segmented from whole brain healthy mice microscopy images were created. A Bayesian-based regression model was used for map reconstruction. We show on 8 healthy and 9 tumor bearing rats that realistic vascular representations yield microvascular estimates in better agreement with the literature than 2D or 3D cylindrical models. Furthermore, tumoral blood oxygenation estimates obtained with the proposed approach are the only ones correlating with in vivo optic-fiber measurements performed in the same animals.