arXiv subjects

Jeffrey F. Williamson

Publications and source records attributed to Jeffrey F. Williamson.

5 recordsLinked to original sources

arXiv2026

Comparison of accuracy and efficiency of three-dimensional discrete ordinates and voxel-scoring Monte Carlo dose calculations for an $I^{125}$ seed

This study compares the efficiency of three-dimensional deterministic discrete-ordinates calculations and voxel-based Monte Carlo simulations of the dose distribution around a model 6702 125I seed in a homogeneous water phantom. The computational efficiency of the PARTISN multigroup discrete ordinates neutral particle transport code is compared to the most efficient Monte Carlo voxel-scoring computer code available to us at the time, which was developed specifically for brachytherapy. The difficulties of such comparisons from the fact that the Monte Carlo and discrete ordinates method exhibit stochastic and systematic errors of different origins. To address this problem, we propose a parametric model for separating the systematic and stochastic error components of each method. Based on this model, a procedure for assessing efficiency of the two methods is proposed consisting of the following steps: (i)-apply the developed error model to evaluate the accuracy of each method relative to unbiased and accurate Monte Carlo point estimator calculations in the same phantom geometry; (ii)-develop systematic and stochastic error criteria which specifies equivalent accuracy of the two methods; and (iii) determine the relative efficiency of two methods by taking the ratio of CPU times required to satisfy these accuracy criteria. This process demostrates that three-dimensional PARTISN discrete ordinate simulations is a factor-of-two more efficient than our voxel-based Monte Carlo code in the 125I energy range. This suggests that discrete ordinates simulations can support efficiency competitive with that of Monte Carlo in three-dimensional geometries and can serve as an efficient and accurate dose-calculation engine in treatment planning for brachytherapy and other treatment modalities utilizing low energy photon fields.

physics.med-ph↗

arXiv2025

Derivation of Tissue Properties from Basis-Vector Model Weights for Dual-Energy CT-Based Monte Carlo Proton Beam Dose Calculations

We propose a novel method, basis vector model material indexing (BVM-MI), for predicting atomic composition and mass density from two independent basis vector model weights derived from dual-energy CT (DECT) for Monte Carlo (MC) dose planning. BVM-MI employs multiple linear regression on BVM weights and their quotient to predict elemental composition and mass density for 70 representative tissues. Predicted values were imported into the TOPAS MC code to simulate proton dose deposition to a uniform cylinder phantom composed of each tissue type. The performance of BVM-MI was compared to the conventional Hounsfield Unit material indexing method (HU-MI), which estimates elemental composition and density based on CT numbers (HU). Evaluation metrics included absolute errors in predicted elemental compositions and relative percent errors in calculated mass density and mean excitation energy. Dose distributions were assessed by quantifying absolute error in the depth of 80% maximum scored dose (R80) and relative percent errors in stopping power (SP) between MC simulations using HU-MI, BVM-MI, and benchmark compositions. Lateral dose profiles were analyzed at R80 and Bragg Peak (RBP) depths for three tissues showing the largest discrepancies in R80 depth. BVM-MI outperformed HU-MI in elemental composition predictions, with mean RMSEs of 1.30% (soft tissue) and 0.1% (bony tissue), compared to 4.20% and 1.9% for HU-MI. R80 depth RMSEs were 0.2 mm (soft) and 0.1 mm (bony) for BVM-MI, vs. 1.8 mm and 0.7 mm for HU-MI. Lateral dose profile analysis showed overall smaller dose errors for BVM-MI across core, halo, and proximal aura regions. In conclusion, fully utilizing the two-parameter BVM space for material indexing led to significantly improved TOPAS MC dose calculations over the conventional HU-MI method, demonstrating BVM-MI's potential to enhance proton therapy planning.

physics.med-ph↗

arXiv2023

MB-DECTNet: A Model-Based Unrolled Network for Accurate 3D DECT Reconstruction

Numerous dual-energy CT (DECT) techniques have been developed in the past few decades. Dual-energy CT (DECT) statistical iterative reconstruction (SIR) has demonstrated its potential for reducing noise and increasing accuracy. Our lab proposed a joint statistical DECT algorithm for stopping power estimation and showed that it outperforms competing image-based material-decomposition methods. However, due to its slow convergence and the high computational cost of projections, the elapsed time of 3D DECT SIR is often not clinically acceptable. Therefore, to improve its convergence, we have embedded DECT SIR into a deep learning model-based unrolled network for 3D DECT reconstruction (MB-DECTNet) that can be trained in an end-to-end fashion. This deep learning-based method is trained to learn the shortcuts between the initial conditions and the stationary points of iterative algorithms while preserving the unbiased estimation property of model-based algorithms. MB-DECTNet is formed by stacking multiple update blocks, each of which consists of a data consistency layer (DC) and a spatial mixer layer, where the spatial mixer layer is the shrunken U-Net, and the DC layer is a one-step update of an arbitrary traditional iterative method. Although the proposed network can be combined with numerous iterative DECT algorithms, we demonstrate its performance with the dual-energy alternating minimization (DEAM). The qualitative result shows that MB-DECTNet with DEAM significantly reduces noise while increasing the resolution of the test image. The quantitative result shows that MB-DECTNet has the potential to estimate attenuation coefficients accurately as traditional statistical algorithms but with a much lower computational cost.

eess.IV↗

arXiv2022

A Metal Artifact Reduction Scheme For Accurate Iterative Dual-Energy CT Algorithms

CT images have been used to generate radiation therapy treatment plans for more than two decades. Dual-energy CT (DECT) has shown high accuracy in estimating electronic density or proton stopping-power maps used in treatment planning. However, the presence of metal implants introduces severe streaking artifacts in the reconstructed images, affecting the diagnostic accuracy and treatment performance. In order to reduce the metal artifacts in DECT, we introduce a metal-artifact reduction scheme for iterative DECT algorithms. An estimate is substituted for the corrupt data in each iteration. We utilize normalized metal-artifact reduction (NMAR) composed with image-domain decomposition to initialize the algorithm and speed up the convergence. A fully 3D joint statistical DECT algorithm, dual-energy alternating minimization (DEAM), with the proposed scheme is tested on experimental and clinical helical data acquired on a Philips Brilliance Big Bore scanner. We compared DEAM with the proposed method to the original DEAM and vendor reconstructions with and without metal-artifact reduction for orthopedic implants (O-MAR). The visualization and quantitative analysis show that DEAM with the proposed method has the best performance in reducing streaking artifacts caused by metallic objects.

eess.IV↗

arXiv2021

A Machine-learning Based Initialization for Joint Statistical Iterative Dual-energy CT with Application to Proton Therapy

Dual-energy CT (DECT) has been widely investigated to generate more informative and more accurate images in the past decades. For example, Dual-Energy Alternating Minimization (DEAM) algorithm achieves sub-percentage uncertainty in estimating proton stopping-power mappings from experimental 3-mm collimated phantom data. However, elapsed time of iterative DECT algorithms is not clinically acceptable, due to their low convergence rate and the tremendous geometry of modern helical CT scanners. A CNN-based initialization method is introduced to reduce the computational time of iterative DECT algorithms. DEAM is used as an example of iterative DECT algorithms in this work. The simulation results show that our method generates denoised images with greatly improved estimation accuracy for adipose, tonsils, and muscle tissue. Also, it reduces elapsed time by approximately 5-fold for DEAM to reach the same objective function value for both simulated and real data.

eess.IV↗