Advanced Diffusion Modeling for Microstructure Mapping
Traditional Poster
Diffusion
Tuesday, 12 May 2026
This session highlights recent advances in diffusion MRI modeling for tissue microstructure estimation. Presentations cover biophysical and data-driven signal models, protocol and encoding optimization, and the influence of hardware and gradient nonlinearities on parameter estimation. Applications range from neural to cardiac tissue, with a particular emphasis on interpreting diffusion-derived metrics in terms of underlying microstructural features.
Skill Level: Intermediate,Advanced
|
470-05-106.
Intra- and extra-axonal structural disorder in a rat model of mild traumatic brain injury using time-dependent Standard Model
Impact: Morphological alterations in intra- and extra-axonal spaces, including axonal beadings, occur in neurological disorders. Integrating diffusion time-dependence into the Standard Model reveals compartment-specific sensitivity of diffusion MRI to micrometer-scale structurally-disordered tissue morphology in rats with mild traumatic brain injury (mTBI).
|
||
|
470-05-107.
A Model For The Diffusion Signal of Cardiac Muscle Tissue
Impact: We present and characterize the accuracy of a model for the diffusion signal in the myocardium and white matter. We anticipate that this model may be useful for inverse problems related to cardiomyocyte and axon sizing using diffusion MRI.
|
||
|
470-05-108.
Protocol Optimization of the IMPULSED Model for Cell Size Imaging on Clinical MRI Scanners
Impact: This
study introduces an information-theory–based optimization framework for
IMPULSED acquisition protocol design, providing a theoretical tool to improve
the data quality of diffusion
time-dependent imaging, and thus enables more reliable estimation of
microstructural parameters in clinical MRI.
|
||
|
470-05-109.
Whole-brain mapping of diffusion fibre response functions using hybrid MRI–microscopy modelling
Impact: By integrating MRI and microscopy, we achieve voxelwise estimation of the diffusion response function across the whole-brain, revealing microstructure-dependent spatial variation. These findings challenge the assumption of a global FRF and support development of microstructure-informed, spatially adaptive deconvolution models.
|
||
|
470-05-110.
Linear and B-tensor Encoding Comparison for Diffusion Microstructure Parameter Estimation on Ultra High Performance Gradient
Impact:
This work enables researchers and clinicians to optimize diffusion MRI protocols by selecting encoding schemes based on desired trade-offs between sensitivity and robustness. It opens pathways for refined modeling approaches and better assessment of neurological disorders using advanced diffusion encoding. |
||
|
470-05-111.
Maximum‑Entropy Diffusion Tensor Distribution for Fiber Orientation and Microstructure Estimation
Impact: This MaxEnt-DTD framework offers improved angular resolution for fiber orientation and microstructure estimation, potentially enabling more accurate diagnosis and understanding of brain diseases for clinicians and patients. It opens new avenues for investigating complex tissue properties.
|
||
|
470-05-112.
Effect of gradient nonlinearities on advanced diffusion models (DTI, DKI, NODDI) in ultra-high gradient MRI
Impact: As new MRI systems are developed to enhance microstructural characterization using ultra strong-gradients, robust corrections become essential to ensure the validity of diffusion metrics. This is relevant to characterize fine brain structure, such as axon diameter, astrocyte and microglia morphology.
|
||
|
470-05-113.
Relating Diffusion MRI-Derived Axon Diameters to the Underlying Diameter Distribution
Impact: Our simulations reveal that violations of NP/WP limits and unmodelled kurtosis in dMRI axon diameter models cause systematic underestimation, contributing to inconsistency between diffusion-derived and histological measures. Recognizing these biases is essential for validation and interpretation of dMRI axon diameters.
|
||
|
470-05-114.
Why q-space metrics from Microscopic Propagator Imaging actually matter
Impact: Q-space imaging (e.g. MAP-MRI) characterizes the brain nervous tissue beyond the Gaussianity assumptions of DTI. Like the latter, however, q-space metrics are a mixture of microscopic and mesoscopic properties of the tissue. Microscopic Propagator Imaging (MPI) isolates the microscopic ones.
|