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We describe the installation of the Gordon Browne Proton Therapy Center within the Lunder Building at Massachusetts General Hospital. Advances in compact proton units with modular design brought to life the vision of allocating a space in a tight corner of a fully functioning high-rise building and installing equipment via pathways typical in a hospital setting. While the assembled equipment is larger than a conventional photon therapy linear accelerator, LINAC, the proton therapy equipment was designed to be modular to make installation easier. This allowed the largest components to be lowered down an elevator shaft and transported past areas used for patient care. Issues that complicated the installation process include the sharing of the loading dock with other activities in the hospital, elevator inspections, after-hours deliveries, infection control, lay-down space, and shielding requirements. This work is important in the identification of future sites for proton therapy.

Gain calibration for X-ray imaging systems with a movable flat panel detector and an intrinsic crosshair is a challenge due to the geometry-dependent heel effect and crosshair artifact. This study aims to develop a gain correction method for such systems by implementing the Multi-Acquisition Gain Image Correction technique. Flood field images containing crosshair and heel effect were acquired in 4 different flat panel detector positions at fixed exposure parameters. The crosshair region was automatically detected using common image processing algorithms and removed by a simple interpolation procedure, resulting in a crosshair-removed image. A large kernel-based correction was then used to remove the heel effect. Mask filters corresponding to each crosshair region were applied to the resultant heel effect-removed images to invalidate the pixels of the original crosshair region. Finally, a seamless gain map was composed with corresponding valid pixels from the processed images either by the sequential replacement or by the selective averaging techniques developed in this study. Quantitative evaluation was performed based on normalized noise power spectrum and detective quantum efficiency improvement factor for the flood field images corrected by the Multi-Acquisition Gain Image Correction-based gain maps. For comparison purposes, a single crosshair-removed gain map was also tested. As a result, it was demonstrated that the Multi-Acquisition Gain Image Correction technique achieved better image quality than the crosshair-removed technique, showing lower normalized noise power spectrum values over most of spatial frequencies. The improvement was more obvious at the priori-crosshair region of the gain map. The mean detective quantum efficiency improvement factor was 1.09 ± 0.06, 2.46 ± 0.32, and 3.34 ± 0.36 in the priori-crosshair region and 2.35 ± 0.31, 2.33 ± 0.31, and 3.09 ± 0.34 in the normal region, for crosshair-removed, Multi-Acquisition Gain Image Correction-sequential replacement, and Multi-Acquisition Gain Image Correction-selective averaging techniques, respectively. Therefore, this study indicates that the introduced Multi-Acquisition Gain Image Correction technique is an appropriate method for gain calibration of an imaging system associated with a moving flat panel detector and an intrinsic crosshair.

AbstractThe superior dose conformation from protons is attributed to the Bragg peak near the end of the proton range. One challenge in proton cancer treatment is to assess the proton range fluctuations due to organ motion such as respiration. A time-resolved proton range telescope that measures coordinates, direction cosines, and the residual range of each proton can be useful in detecting and quantifying variations in radiological path length during the course of proton radiotherapy. In this paper, the Monte Carlo N-Particle eXtended (MCNPX) code was used to simulate the range telescope and study the image quality. To validate the MCNPX simulations, a simulated proton radiograph was compared with an experimentally acquired film for the same phantom. In addition, four quality assurance phantoms were simulated to investigate the quality of simulated proton radiography. Finally, the methods were applied to one phase of a patient four-dimensional computed tomography (4DCT) data set for proton radiography simulations. The results indicate that Monte Carlo simulations offer data that are useful in analyzing image spatial and temporal resolutions. Simulations show that it is useful to quantify the tumor position changes due to respiration by using a proton telescope.

Image guidance in radiotherapy and extracranial radiosurgery offers the potential for precise radiation dose delivery to a moving tumour. Recent work has demonstrated how to locate and track the position of a tumour in real-time using diagnostic x-ray imaging to find implanted radio-opaque markers. However, the delivery of a treatment plan through gating or beam tracking requires adequate consideration of treatment system latencies, including image acquisition, image processing, communication delays, control system processing, inductance within the motor, mechanical damping, etc. Furthermore, the imaging dose given over long radiosurgery procedures or multiple radiotherapy fractions may not be insignificant, which means that we must reduce the sampling rate of the imaging system. This study evaluates various predictive models for reducing tumour localization errors when a real-time tumour-tracking system targets a moving tumour at a slow imaging rate and with large system latencies. We consider 14 lung tumour cases where the peak-to-peak motion is greater than 8 mm, and compare the localization error using linear prediction, neural network prediction and Kalman filtering, against a system which uses no prediction. To evaluate prediction accuracy for use in beam tracking, we compute the root mean squared error between predicted and actual 3D motion. We found that by using prediction, root mean squared error is improved for all latencies and all imaging rates evaluated. To evaluate prediction accuracy for use in gated treatment, we present a new metric that compares a gating control signal based on predicted motion against the best possible gating control signal. We found that using prediction improves gated treatment accuracy for systems that have latencies of 200 ms or greater, and for systems that have imaging rates of 10 Hz or slower.

The design of an integrated radiotherapy imaging system (IRIS), consisting of gantry mounted diagnostic (kV) x-ray tubes and fast read-out flat-panel amorphous-silicon detectors, has been studied. The system is meant to be capable of three main functions: radiographs for three-dimensional (3D) patient set-up, cone-beam CT and real-time tumour/marker tracking. The goal of the current study is to determine whether one source/panel pair is sufficient for real-time tumour/marker tracking and, if two are needed, the optimal position of each relative to other components and the isocentre. A single gantry-mounted source/imager pair is certainly capable of the first two of the three functions listed above and may also be useful for the third, if combined with prior knowledge of the target's trajectory. This would be necessary because only motion in two dimensions is visible with a single imager/source system. However, with previously collected information about the trajectory, the third coordinate may be derived from the other two with sufficient accuracy to facilitate tracking. This deduction of the third coordinate can only be made if the 3D tumour/marker trajectory is consistent from fraction to fraction. The feasibility of tumour tracking with one source/imager pair has been theoretically examined here using measured lung marker trajectory data for seven patients from multiple treatment fractions. The patients' selection criteria include minimum mean amplitudes of the tumour motions greater than 1 cm peak-to-peak. The marker trajectory for each patient was modelled using the first fraction data. Then for the rest of the data, marker positions were derived from the imager projections at various gantry angles and compared with the measured tumour positions. Our results show that, due to the three dimensionality and irregular trajectory characteristics of tumour motion, on a fraction-to-fraction basis, a ‘monoscopic’ system (single source/imager) is inadequate for consistent real-time tumour tracking, even with prior knowledge. We found that, among the seven patients studied with peak-to-peak marker motion greater than 1 cm, five cases have mean localization errors greater than 2 mm and two have mean errors greater than 3 mm. Because of this uncertainty associated with a monoscopic system, two source/imager pairs are necessary for robust 3D target localization. Dual orthogonal x-ray source/imager pairs mounted on the linac gantry are chosen for the IRIS. We further studied the placement of the x-ray sources/panel based on the geometric specifications of the Varian 21EX Clinac. The best configuration minimizes the localization error while maintaining a large field of view and avoiding collisions with the floor/ceiling or couch.