Microstructure estimation from diffusion-MRI: Compartmentalized models in permeable cellular tissue
R\'emy Gardier (1), Juan Luis Villarreal Haro (1), Erick J, Canales-Rodr{\i}guez (1), Ileana O. Jelescu (2, 3), Gabriel Girard (1 and, 2, 4), Jonathan Rafael-Patino (2, 1), Jean-Philippe Thiran (1, 2 and, 4) ((1) Signal Processing Laboratory (LTS5), \'Ecole Polytechnique

TL;DR
This study demonstrates that incorporating membrane permeability into diffusion-MRI models significantly improves the accuracy of microstructure estimates in permeable tissues like gray matter and tumors.
Contribution
The paper introduces CEXI, a new diffusion-MRI model that accounts for membrane exchange, outperforming existing models that assume negligible water exchange.
Findings
Model with exchange provides more stable estimates of cell size and volume fractions.
Model with exchange accurately estimates membrane permeability and exchange time.
Neglecting exchange leads to inaccurate microstructure characterization in permeable tissues.
Abstract
Diffusion-weighted magnetic resonance imaging (DW-MRI) is used to characterize brain tissue microstructure employing tissue-specific biophysical models. A current limitation, however, is that most of the proposed models are based on the assumption of negligible water exchange between the intra- and extracellular compartments, which might not be valid in various brain tissues, including unmyelinated axons, gray matter, and tumors. The purpose of this work is to quantify the effect of membrane permeability on the estimates of two popular models neglecting exchange, and compare their performance with a model including exchange. To this aim, DW-MRI experiments were performed in controlled environments with Monte-Carlo simulations. The DW-MRI signals were generated in numerical substrates mimicking biological tissue made of spherical cells with permeable membranes like cancerous tissue or…
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Taxonomy
TopicsAdvanced Neuroimaging Techniques and Applications · MRI in cancer diagnosis · Advanced MRI Techniques and Applications
MethodsDiffusion
