Transpire (United States)

Purpose: The TG‐186 report provides guidance to early adopters of model‐based dose calculation algorithms (MBDCAs) for brachytherapy. A charge of the AAPM‐ESTRO Working Group on MBDCA is to develop well‐defined test case plans, available as references for the software commissioning process to be performed by end‐users. The aim of this work is to develop test case plans for a generic HDR 192Ir source alone and in combination with a vaginal cylinder applicator with 180° tungsten‐alloy shielding in a DICOM‐based water phantom. Methods: A DICOM CT dataset was created with a 30 cm diameter water sphere surrounded by air. The voxel size was 1.33×1.33×1.33 mm3 for evaluating absorbed dose rate (cGy.h−1.U−1). Dose distributions were obtained in the planes x=0, y=0 and z=0. Three geometries were considered with the HDR source (a) at the center of sphere, (b) displaced 12.75 cm laterally, and (c) at the center of the applicator. Monte Carlo (MC) codes (Geant4, Penelope2009, MCNP5, and EGSnrc‐based BrachyDose) and Acuros were used. MC simulations were performed by multiple users to validate consistency of the DICOM reading, calculation set‐up, and dose reporting. Results: For all three geometries, MC results were generally within 2% agreement for r < 14 cm except at the sphere boundary where volume averaging occurred in voxels comprising two materials. There were no trends for r > 14 cm except that dosimetric agreement amongst the codes was limited by computational statistics. Results from Acuros and Penelope2009 were compared using a 2%/1.33 mm λ‐index criteria for r < 7 cm. Voxel pass rates were 100%, 100%, and 94% for geometries (a), (b), and (c), respectively. Conclusions: Dose rate distributions were consistent within statistical uncertainties among MC codes and for different users. Acuros reproduces MC data for the three geometries. These geometries may serve as test case plans for MBDCAs. T. Wareing is employee of Transpire Inc. that developed Acuros.

Herein we present a comparison of Monte Carlo, radiative transport (RT), and diffusion based forward models for implementation into small animal optical tomography algorithm. The simulations show that the unique RT solver can accurately predict the time-dependent propagation of excitation and emission photons in diffuse as well as transport limited systems. Furthermore, the need for the accurate modeling of incident excitation illumination and the acceptance angle of the detector is demonstrated

Small animal optical tomography has significant, but potential application for streamlining drug discovery and pre-clinical investigation of drug candidates. However, accurate modeling of photon propagation in small animal volumes is critical to quantitatively obtain accurate tomographic images. Herein we present solutions from a robust fluorescence-enhanced, frequency domain radiative transport equation (RTE) solver with unique attributes that facilitate its deployment within tomographic algorithms. Specifically, the coupled equations describing time-dependent excitation and emission light transport are solved using discrete ordinates (SN) angular differencing along with linear discontinuous finite-element spatial differencing on unstructured tetrahedral grids. Source iteration in conjunction with diffusion synthetic acceleration is used to iteratively solve the resulting system of equations. This RTE solver can accurately and efficiently predict ballistic as well as diffusion limited transport regimes which could simultaneously exist in small animals. Furthermore, the solver provides accurate solutions on unstructured, tetrahedral grids with relatively large element sizes as compared to commonly employed solvers that use step differencing. The predictions of the solver are validated by a series of frequency-domain, phantom measurements with optical properties ranging from diffusion limited to transport limited propagation. Our results demonstrate that the RTE solution consistently matches measurements made under both diffusion and transport-limited conditions. This work demonstrates the use of an appropriate RTE solver for deployment in small animal optical tomography.