3 research outputs found
Cellular EXchange Imaging (CEXI): Evaluation of a diffusion model including water exchange in cells using numerical phantoms of permeable spheres
Purpose: Biophysical 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. Methods: We 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. Results: CEXI 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. Conclusion: This 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.Comment: 7 figures, 2 tables, 21 pages, under revie
Tractography passes the test : Results from the diffusion-simulated connectivity (disco) challenge
Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.Peer reviewe