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In the real-time tumor-tracking radiotherapy system, fluoroscopy is used to determine the real-time position of internal fiducial markers. The pattern recognition score (PRS) ranging from 0 to 100 is computed by a template pattern matching technique in order to determine the marker position on the fluoroscopic image. The PRS depends on the quality of the fluoroscopic image. However, the fluoroscopy parameters such as tube voltage, current and exposure duration are selected manually and empirically in the clinical situation. This may result in an unnecessary imaging dose from the fluoroscopy or loss of the marker because of too much or insufficient x-ray exposure. In this study, a novel optimization method is proposed in order to minimize the fluoroscopic dose while keeping the image quality usable for marker tracking. The PRS can be predicted in a region where the marker appears to move in the fluoroscopic image by the proposed method. The predicted PRS can be utilized to judge whether the marker can be tracked with accuracy. In this paper, experiments were performed to show the feasibility of the PRS prediction method under various conditions. The predicted PRS showed good agreement with the measured PRS. The root mean square error between the predicted PRS and the measured PRS was within 1.44. An experiment using a motion controller and an anthropomorphic chest phantom was also performed in order to imitate a clinical fluoroscopy situation. The result shows that the proposed prediction method is expected to be applicable in a real clinical situation.

A feasibility study of a novel PET-based molecular image guided radiation therapy (m-IGRT) system was conducted by comparing PET-based digitally reconstructed planar image (PDRI) registration with radiographic registration. We selected a pair of opposing parallel-plane PET systems for the practical implementation of this system. Planar images along the in-plane and cross-plane directions were reconstructed from the parallel-plane PET data. The in-plane and cross-plane FWHM of the profile of 2 mm diameter sources was approximately 1.8 and 8.1 mm, respectively. Therefore, only the reconstructed in-plane image from the parallel-plane PET data was used in the PDRI registration. In the image registration, five different sizes of (18)F cylindrical sources (diameter: 8, 12, 16, 24, 32 mm) were used to determine setup errors. The data acquisition times were 1, 3 and 5 min. Image registration was performed by five observers to determine the setup errors from PDRI registration and radiographic registration. The majority of the mean registration errors obtained from the PDRI registration were not significantly different from those obtained from the radiographic registration. Acquisition time did not appear to result in significant differences in the mean registration error. The mean registration error for the PDRI registration was found to be 0.93 ± 0.33 mm. This is not statistically different from the radiographic registration which had a mean registration error of 0.92 ± 0.27 mm. Our results suggest that m-IGRT image registration using PET-based reconstructed planar images along the in-plane direction is feasible for clinical use if PDRI registration is performed at two orthogonal gantry angles.

The purpose of this study was to develop a novel scintillation dosimeter for in vivo dosimetry in Ir-192 brachytherapy via the pulse-counting mode. The new dosimeter was made from a plastic scintillator shaped into a hemisphere of diameter 1 mm and connected to the tip of a plastic optical fiber. The relationship between pulse counts and absorbed dose was derived based on the assumption that scintillation photons from the incident gamma ray are proportional to the absorbed dose. An equation for the conversion of pulse counts to water-equivalent dose was deduced wherein the pulse height spectrum from scintillation photons was assumed to be exponential. To confirm its accuracy, the dose rate distribution in a water phantom was measured by the present dosimeter and this was compared with Monte Carlo simulations, resulting in a discrepancy of less than 1.97%. It was found that the dosimeter has a wide dynamic range of linearity up to an order of magnitude of almost 10(3), including corrections for loss of counts due to pile-up.

We discuss our experience improving the speed of Monte Carlo dose distribution calculations with GEANT4. First we customized the core geometry library to navigate voxelized CT volumes more efficiently. This resulted in a speed improvement of more than two orders of magnitude in our case. We then tried to run the simulation in parallel. However, we found that existing parallelizations of GEANT4 such as TOP-C (Task Oriented Parallel C/C++) did not scale well. We determined that the poor performance was due to excessive network traffic. We abandoned the master/slave model employed in TOP-C in favour of "static scheduling" parallelism where the event list is divided evenly among the processes. Our implementation used only a few low-level MPI calls. With this method, we achieved a linear speed improvement on our cluster of 23 machines. The simulation executes efficiently because there is almost no network traffic during the simulation. Using our customized GEANT4 navigation library running on our cluster with our parallelization method, we achieved a combined speed improvement factor of more than 3000, reducing our simulation time from several days to a few minutes. Although we did not consider issues such as fault tolerance or load sharing, our approach is well suited for many kinds of Monte Carlo simulations on a homogenous cluster of local PCs.