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BACKGROUND: Simulations of physical processes and behavior can provide unique insights and understanding of real-world problems. Magnetic Resonance Imaging (MRI) is an imaging technique with several components of complexity. Several of these components have been characterized and simulated in the past. However, several computational challenges prevent simulations from being simultaneously fast, flexible, and accurate. PURPOSE: The simulation of MRI experiments is underutilized by medical physicists and researchers using currently available simulators due to reasons including speed, accuracy, and extensibility constraints. This paper introduces an innovative MRI simulation engine and framework that aims to overcome these issues making available realistic and fast MRI simulation. METHODS: Using the CUDA C/C++ programing language, an MRI simulation engine (PhoenixMR), incorporating a Turing-complete virtual machine (VM) to simulate abstract spatiotemporal complexities, was developed. This engine solves a set of time-discrete Bloch equations using the symmetric operator splitting technique. An extensible front-end framework package (written in Python) aids the use of PhoenixMR to simplify simulation development. RESULTS: The PhoenixMR library and front-end codes have been developed and tested. A set of example simulations were performed to demonstrate the ease of use and flexibility of simulation components such as geometrical setup, pulse sequence design, phantom design, and so forth. Initial validation of PhoenixMR is performed by comparing its accuracy and performance against a widely used MRI simulator using identical simulation parameters. Validation results show PhoenixMR simulations are three orders of magnitude faster. There is also strong agreement between models. CONCLUSIONS: A novel MRI simulation platform called PhoenixMR has been introduced. This research tool is designed to be usable by physicists and engineers interested in performing MRI simulations. Examples are shown demonstrating the accuracy, flexibility, and usability of PhoenixMR in several key areas of MRI simulation.
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PURPOSE: There is little evidence in the literature which quantifies the accuracy of Treatment Planning Systems (TPSs) using large fields at extended SSD (eSSD). This paper introduces the approach taken at Christchurch Hospital, New Zealand to validate the use of the Monaco TPS for Total Body Irradiation (TBI) treatments. METHODS: A purpose-built device for allowing precise movements of block-like phantoms called a Phantom Mobility Device (PMD) was used for collecting measurements at eSSD. These measurements were used for determining the ability of the Monaco TPS (originally validated for SSDs between 80 and 110 cm) to accurately model dose distributions for TBI treatments at Christchurch Hospital on either treatment machine one (T1) or two (T2) with SSD values of 341 and 432.6 and clinically useful field sizes of 120 and 170 cm, respectively. RESULTS: We found that within the limits of measurement uncertainty the PMD contributed no determinable scatter to the measurements and proved a reliable approach for eSSD dose measurements. Additionally, by applying depth and off-axis distance constraints of use for TPS information it is possible to use the existing Monaco CCC model at eSSD for block phantom geometries. Dose Difference (DD) analysis showed a clinically acceptable agreement between the CCC model and measured data over a range of depths and off-axis distances. CONCLUSIONS: The PMD was determined to be a useful tool for accurate measurement of extended SSD treatment fields. Monaco TPS CCC model agreed well for block phantoms so future comparisons to anthropomorphic phantoms or patient data are feasible.