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This manuscript describes the structure, management and outcomes of a multi-institutional clinical and research medical physics residency program (Harvard Medical Physics Residency Program, or HMPRP) to provide potentially useful information to the centers considering a multi-institutional approach for their training programs.Data from the program documents and public records was used to describe HMPRP and obtain statistics about participating faculty, enrolled residents, and graduates. Challenges associated with forming and managing a multi-institutional program and developed solutions for effective coordination between several clinical centers are described.HMPRP was formed in 2009 and was accredited by the Commission on Accreditation of Medical Physics Education Programs (CAMPEP) in 2011. It is a 3-year therapy program, with a dedicated year of research and the 2 years of clinical training at three academic hospitals. A CAMPEP-accredited Certificate Program is embedded in HMPRP to allow enrolled residents to complete a formal didactic training in medical physics if necessary. The clinical training covers the material required by CAMPEP. In addition, training in protons, CyberKnife, MR-linac, and at network locations is included. The clinical training and academic record of the residents is outstanding. All graduates have found employment within clinical medical physics, mostly at large academic centers and graduates had a 100% pass rate at the oral American Board of Radiology exams. On average, three manuscripts per resident are published during residency, and multiple abstracts are presented at conferences.A multi-institutional medical physics residency program can be successfully formed and managed. With a collaborative administrative structure, the program creates an environment for high-quality clinical training of the residents and high productivity in research. The main advantage of such program is access to a wide variety of resources. The main challenge is creating a structure for efficient management of multiple resources at different locations. This report may provide valuable information to centers considering starting a multi-institutional residency program.

Objective. Monte Carlo (MC) codes are increasingly used for accurate radiotherapy dose calculation. In proton therapy, the accuracy of the dose calculation algorithm is expected to have a more significant impact than in photon therapy due to the depth-dose characteristics of proton beams. However, MC simulations come at a considerable computational cost to achieve statistically sufficient accuracy. There have been efforts to improve computational efficiency while maintaining sufficient accuracy. Among those, parallelizing particle transportation using graphic processing units (GPU) achieved significant improvements. Contrary to the central processing unit, a GPU has limited memory capacity and is not expandable. It is therefore challenging to score quantities with large dimensions requiring extensive memory. The objective of this study is to develop an open-source GPU-based MC package capable of scoring those quantities. Approach. We employed a hash-table, one of the key-value pair data structures, to efficiently utilize the limited memory of the GPU and score the quantities requiring a large amount of memory. With the hash table, only voxels interacting with particles will occupy memory, and we can search the data efficiently to determine their address. The hash-table was integrated with a novel GPU-based MC code, moqui. Main results. The developed code was validated against an MC code widely used in proton therapy, TOPAS, with homogeneous and heterogeneous phantoms. We also compared the dose calculation results of clinical treatment plans. The developed code agreed with TOPAS within 2%, except for the fall-off and regions, and the gamma pass rates of the results were >99% for all cases with a 2 mm/2% criteria. Significance. We can score dose-influence matrix and dose-rate on a GPU for a 3-field H&N case with 10 GB of memory using moqui, which would require more than 100 GB of memory with the conventionally used array data structure.

Pencil beam scanning (PBS) monitoring chambers use an ionization control signal, monitor units (MUs), or gigaprotons (Gp) to irradiate a pencil beam and normalize dose calculations. The nozzle deflects the beam from the nozzle axis by an angle subtended at the source-to-axis distance (τ) from the isocenter. If the angle is not correctly considered in calibrations or calculations, it can lead to systematic errors.Aspects to consider for machines of various τs are fourfold. First, for the machine, there is a pathlength change of proton tracks in the monitor chamber. Second, for measurements, a uniform-square irradiation over a plane, with constant Gp per spot, does not deliver uniform dose in a measurement plane. Third, for Monte Carlo (MC) simulations, Gp (and not MU) is proportional to simulating a number of protons. Fourth, for pencil beam algorithms (PBA), MU or Gp may be used for pencil beam weight, but usage needs to be consistent. Another consideration is the beam shape change from circular to oval in the projection onto voxels.Coordinate systems for PBS delivery are described.Users of intermediate-τ machines, corresponding to the onset of 1% pathlength corrections within the scanned field size, must not assume that MUs are proportional to the number of particles in MC simulations, and the PBA may need pathlength corrections. For a field size of 24 × 24 cmIdentifying corrections due to the pencil beam angle and their onset are important for reducing the outer diameter of proton therapy gantries. The use of Gp (or the number of protons) meterset standardizes data interchange and helps to reduce systematic errors due to the angle of the beam.

Simple Summary The fast and accurate definition of structures is a main limiting factor in online adaptive proton therapy. In this study, different methods to include structure propagation uncertainties in the optimization were compared with adaptation using physician-drawn structures, uncorrected propagated structures, and no adaptation. While adaptation with physician-drawn structures resulted in the best adaptive plan quality and no adaptation in the worst, manual structure correction could be replaced by a fast 'plausibility check', and plans could be adapted with correction-free adaptation strategies. Currently, adaptive strategies require time- and resource-intensive manual structure corrections. This study compares different strategies: optimization without manual structure correction, adaptation with physician-drawn structures, and no adaptation. Strategies were compared for 16 patients with pancreas, liver, and head and neck (HN) cancer with 1-5 repeated images during treatment: 'reference adaptation', with structures drawn by a physician; 'single-DIR adaptation', using a single set of deformably propagated structures; 'multi-DIR adaptation', using robust planning with multiple deformed structure sets; 'conservative adaptation', using the intersection and union of all deformed structures; 'probabilistic adaptation', using the probability of a voxel belonging to the structure in the optimization weight; and 'no adaptation'. Plans were evaluated using reference structures and compared using a scoring system. The reference adaptation with physician-drawn structures performed best, and no adaptation performed the worst. For pancreas and liver patients, adaptation with a single DIR improved the plan quality over no adaptation. For HN patients, integrating structure uncertainties brought an additional benefit. If resources for manual structure corrections would prevent online adaptation, manual correction could be replaced by a fast 'plausibility check', and plans could be adapted with correction-free adaptation strategies. Including structure uncertainties in the optimization has the potential to make online adaptation more automatable., Cancers, 14 (16), ISSN:2072-6694

Radiation therapy treatments are typically planned based on a single image set, assuming that the patient’s anatomy and its position relative to the delivery system remains constant during the course of treatment. Similarly, the prescription dose assumes constant biological dose-response over the treatment course. However, variations can and do occur on multiple time scales. For treatment sites with significant intra-fractional motion, geometric changes happen over seconds or minutes, while biological considerations change over days or weeks. At an intermediate timescale, geometric changes occur between daily treatment fractions. Adaptive radiation therapy is applied to consider changes in patient anatomy during the course of fractionated treatment delivery. While traditionally adaptation has been done off-line with replanning based on new CT images, online treatment adaptation based on on-board imaging has gained momentum in recent years due to advanced imaging techniques combined with treatment delivery systems. Adaptation is particularly important in proton therapy where small changes in patient anatomy can lead to significant dose perturbations due to the dose conformality and finite range of proton beams. This review summarizes the current state-of-the-art of on-line adaptive proton therapy and identifies areas requiring further research.