Affiliation:
1. Signal Processing Laboratory (LTS5) École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland
2. Radiology Department Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL) (CHUV‐UNIL) Lausanne Switzerland
3. School of Biology and Medicine University of Lausanne (UNIL) Lausanne Switzerland
4. Department of Computer Science Université de Sherbrooke Sherbrooke Canada
5. CIBM Center for Biomedical Imaging Lausanne Switzerland
Abstract
PurposeBiophysical models of diffusion MRI have been developed to characterize microstructure in various tissues, but existing models are not suitable for tissue composed of permeable spherical cells. In this study we introduce Cellular Exchange Imaging (CEXI), a model tailored for permeable spherical cells, and compares its performance to a related Ball & Sphere (BS) model that neglects permeability.MethodsWe generated DW‐MRI signals using Monte‐Carlo simulations with a PGSE sequence in numerical substrates made of spherical cells and their extracellular space for a range of membrane permeability. From these signals, the properties of the substrates were inferred using both BS and CEXI models.ResultsCEXI outperformed the impermeable model by providing more stable estimates cell size and intracellular volume fraction that were diffusion time‐independent. Notably, CEXI accurately estimated the exchange time for low to moderate permeability levels previously reported in other studies (). However, in highly permeable substrates (), the estimated parameters were less stable, particularly the diffusion coefficients.ConclusionThis study highlights the importance of modeling the exchange time to accurately quantify microstructure properties in permeable cellular substrates. Future studies should evaluate CEXI in clinical applications such as lymph nodes, investigate exchange time as a potential biomarker of tumor severity, and develop more appropriate tissue models that account for anisotropic diffusion and highly permeable membranes.
Funder
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Subject
Radiology, Nuclear Medicine and imaging
Cited by
9 articles.
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