arXiv subjects

Jason G Parker

Publications and source records attributed to Jason G Parker.

3 recordsLinked to original sources

arXiv2026

PsSource: an installable Geant4 extension for transport-coupled positronium annihilation with validated Ore-Powell three-photon physics

PsSource is an installable Geant4 extension for configurable positronium-aware terminal positron annihilation. Geant4 transports the positron normally, after which PsSource replaces only terminal at-rest annihilation, preserves the terminal position and global time, resolves a fixed or host-defined environment, samples the annihilation class and delay, and returns ordinary two- or three-photon secondaries. Supported outcomes are direct two-photon, para-positronium two-photon, ortho-positronium two-photon, and ortho-positronium three-photon annihilation. Fixed and exponential delays and approximate phase-space, Geant4 Ore-Powell, and polarized Geant4 Ore-Powell backends are available. Regression tests verified terminal-state preservation, timing decomposition, all four classes, photon multiplicity and parentage, equivalent environment pathways, and unchanged photon physics when PsSource-specific truth recording was disabled. The Ore-Powell backend was evaluated using 100,000 three-photon events. Comparison with an analytic joint-energy distribution yielded chi-square = 1236.91 for 1274 degrees of freedom, a standardized score of -0.735, and a maximum marginal empirical CDF difference of 0.00208. Ordered photon-energy spectra agreed with native Geant4 and GATE references, with no pairwise empirical CDF difference greater than 0.00452. Photon directions and event-plane normals were isotropic with no detectable Cartesian-axis bias. The polarized backend satisfied normalization and transversality to within 1.5 x 10^-12 and preserved the ordinary Ore-Powell energy spectrum. PsSource provides a validated integration layer for positronium-aware terminal annihilation in Geant4.

physics.med-ph↗

arXiv2019

Predicted disease compositions of human gliomas estimated from multiparametric MRI can predict endothelial proliferation, tumor grade, and overall survival

Background and Purpose: Biopsy is the main determinants of glioma clinical management, but require invasive sampling that fail to detect relevant features because of tumor heterogeneity. The purpose of this study was to evaluate the accuracy of a voxel-wise, multiparametric MRI radiomic method to predict features and develop a minimally invasive method to objectively assess neoplasms. Methods: Multiparametric MRI were registered to T1-weighted gadolinium contrast-enhanced data using a 12 degree-of-freedom affine model. The retrospectively collected MRI data included T1-weighted, T1-weighted gadolinium contrast-enhanced, T2-weighted, fluid attenuated inversion recovery, and multi-b-value diffusion-weighted acquired at 1.5T or 3.0T. Clinical experts provided voxel-wise annotations for five disease states on a subset of patients to establish a training feature vector of 611,930 observations. Then, a k-nearest-neighbor (k-NN) classifier was trained using a 25% hold-out design. The trained k-NN model was applied to 13,018,171 observations from seventeen histologically confirmed glioma patients. Linear regression tested overall survival (OS) relationship to predicted disease compositions (PDC) and diagnostic age (alpha = 0.05). Canonical discriminant analysis tested if PDC and diagnostic age could differentiate clinical, genetic, and microscopic factors (alpha = 0.05). Results: The model predicted voxel annotation class with a Dice similarity coefficient of 94.34% +/- 2.98. Linear combinations of PDCs and diagnostic age predicted OS (p = 0.008), grade (p = 0.014), and endothelia proliferation (p = 0.003); but fell short predicting gene mutations for TP53BP1 and IDH1. Conclusions: This voxel-wise, multi-parametric MRI radiomic strategy holds potential as a non-invasive decision-making aid for clinicians managing patients with glioma.

q-bio.QM↗

arXiv2019

Statistical multiscale mapping of IDH1, MGMT, and microvascular proliferation in human brain tumors from multiparametric MR and spatially-registered core biopsy

We propose a statistical multiscale mapping approach to identify microscopic and molecular heterogeneity across a tumor microenvironment using multiparametric MR (mp-MR). Twenty-nine patients underwent pre-surgical mp-MR followed by MR-guided stereotactic core biopsy. The locations of the biopsy cores were identified in the pre-surgical images using stereotactic bitmaps acquired during surgery. Feature matrices mapped the multiparametric voxel values in the vicinity of the biopsy cores to the pathologic outcome variables for each patient and logistic regression tested the individual and collective predictive power of the MR contrasts. A non-parametric weighted k-nearest neighbor classifier evaluated the feature matrices in a leave-one-out cross validation design across patients. Resulting class membership probabilities were converted to chi-square statistics to develop full-brain parametric maps, implementing Gaussian random field theory to estimate inter-voxel dependencies. Corrections for family-wise error rates were performed using Benjamini-Hochberg and random field theory, and the resulting accuracies were compared. The combination of all five image contrasts correlated with outcome (P<.001) for all four microscopic variables. The probabilistic mapping method using Benjamini-Hochberg generated statistically significant results (P<.05) for three of the four dependent variables: 1) IDH1, 2) MGMT, and 3) microvascular proliferation, with an average classification accuracy of 0.984 +/- 0.02 and an average classification sensitivity of 1.567% +/- 0.967. The images corrected by random field theory demonstrated improved classification accuracy (0.989 +/- 0.008) and classification sensitivity (5.967% +/- 2.857) compared with Benjamini-Hochberg. Microscopic and molecular tumor properties can be assessed with statistical confidence across the brain from minimally-invasive, mp-MR.

physics.med-ph↗