arXiv subjects

C. M. W. Tax

Publications and source records attributed to C. M. W. Tax.

2 recordsLinked to original sources

arXiv2026

Direct correlation of intravoxel incoherent motion imaging based on implicit neural representations and histopathology in pediatric Wilms tumors: can it differentiate between subtypes?

Wilms tumor (WT) is a heterogeneous pediatric renal malignancy. Invasive procedures to establish histology prior to pre operative treatment are not recommended by the International Society of Pediatric (SIOP) Oncology Renal Tumor Study Group, yet different tissue components are associated with different responses to therapy (SIOP Umbrella protocol). Intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) may improve subtype differentiation before surgery as a noninvasive radiographic biomarker by capturing both diffusion and perfusion characteristics, as opposed to the conventionally used apparent diffusion coefficient (ADC). The objective of this study is to evaluate the discriminative performance of IVIM MRI in differentiating WT subtypes. IVIM provides quantitative parameters that disentangle the diffusion coefficient (D), incoherent blood flow (pseudodiffusion Dstar) and perfusion fraction (f). This retrospective study included thirthy children with WT. Standardized 1.5T MRI with 10 b value diffusion weighted imaging (DWI) was performed. IVIM parameters were estimated using a selfsupervised implicit neural representations network and were correlated with histopathology via a patient specific 3D printed cutting guide. Statistical analyses included mixed models, receiver operating curve analysis, and logistic regression. Thirty lesions were MRI histopathology correlated on slice level. Significant subtype differences were observed for Dstar stromal versus epithelial (p = 0.024), regressive tumors (p = 0.031) and mixed WT tumors (p = 0.043). ADC showed no significant subtype differences. Direct MRI histopathology correlation enabled differentiation between stromal subtype and epithelial, mixed and regressive subtype on macroscopic slice level using Dstar.

physics.med-ph↗

arXiv2020

Resolving orientation-specific diffusion-relaxation features via Monte-Carlo density-peak clustering in heterogeneous brain tissue

Characterizing the properties and orientations of sub-voxel fiber populations, although essential to study white-matter architecture, microstructure and connectivity, remains one of the main challenges faced by the MRI microstructure community. While some progress has been made in overcoming this challenge using models, signal representations and tractography algorithms, these approaches are ultimately limited by their key assumptions or by the lack of specificity of the diffusion signal alone. In order to alleviate these limitations, we combine diffusion-relaxation MR acquisitions incorporating tensor-valued diffusion encoding, Monte-Carlo signal inversions that extract non-parametric intra-voxel distributions of diffusion tensors and relaxation rates, and density-based clustering techniques. This new approach, called "Monte-Carlo density-peak clustering" (MC-DPC), first delineates clusters in the diffusion-orientation subspace of the fiber-like diffusion-relaxation components output by Monte-Carlo signal inversions and then draws from the statistical aspect of these inversion algorithms to compute the median and interquartile range of orientation-resolved means of diffusivities and relaxation rates. Evaluating MC-DPC on tensor-valued diffusion-encoded and T2-weighted correlated datasets in silico and in vivo, we demonstrate its ability to simultaneously capture sub-voxel fiber orientations and cones of uncertainty, and measure fiber-specific diffusion-relaxation properties that are consistent with the known anatomy and existing literature. Straightforwardly translatable to other diffusion-relaxation correlation experiments probing $T_1$ and $T_2^*$, MC-DPC shows potential in tracking bundle-specific patient-control group differences and longitudinal microstructural changes, enabling new tools for microstructure-informed tractography, and mapping tract-specific myelination states.

physics.med-ph↗