Fast on-site Monte Carlo tool for dose calculations in CT applications.

PURPOSE: Monte Carlo (MC) simulation is an established technique for dose calculation in diagnostic radiology. The major drawback is its high computational demand, which limits the possibility of usage in real-time applications. The aim of this study was to develop fast on-site computed tomography (CT) specific MC dose calculations by using a graphics processing unit (GPU) cluster.
METHODS: GPUs are powerful systems which are especially suited to problems that can be expressed as data-parallel computations. In MC simulations, each photon track is independent of the others; each launched photon can be mapped to one thread on the GPU, thousands of threads are executed in parallel in order to achieve high performance. For further acceleration, the authors considered multiple GPUs. The total computation was divided into different parts which can be calculated in parallel on multiple devices. The GPU cluster is an MC calculation server which is connected to the CT scanner and computes 3D dose distributions on-site immediately after image reconstruction. To estimate the performance gain, the authors benchmarked dose calculation times on a 2.6 GHz Intel Xeon 5430 Quad core workstation equipped with two NVIDIA GeForce GTX 285 cards. The on-site calculation concept was demonstrated for clinical and preclinical datasets on CT scanners (multislice CT, flat-detector CT, and micro-CT) with varying geometry, spectra, and filtration. To validate the GPU-based MC algorithm, the authors measured dose values on a 64-slice CT system using calibrated ionization chambers and thermoluminesence dosimeters (TLDs) which were placed inside standard cylindrical polymethyl methacrylate (PMMA) phantoms.
RESULTS: The dose values and profiles obtained by GPU-based MC simulations were in the expected good agreement with computed tomography dose index (CTDI) measurements and reference TLD profiles with differences being less than 5%. For 10(9) photon histories simulated in a 256 × 256 × 12 voxel thorax dataset with voxel size of 1.36 × 1.36 × 3.00 mm(3), calculation times of about 70 and 24 min were necessary with single-core and multiple-core central processing unit (CPU) solutions, respectively. Using GPUs, the same MC calculations were performed in 1.27 min (single card) and 0.65 min (two cards) without a loss in quality. Simulations were thus speeded up by factors up to 55 and 36 compared to single-core and multiple-core CPU, respectively. The performance scaled nearly linearly with the number of GPUs. Tests confirmed that the proposed GPU-based MC tool can be easily adapted to different types of CT scanners and used as service providers for fast on-site dose calculations.
CONCLUSIONS: The Monte Carlo software package provides fast on-site calculation of 3D dose distributions in the CT suite which makes it a practical tool for any type of CT-specific application.