Varian Medical Systems is an American radiation oncology treatments and software maker based in Palo Alto, California. Their medical devices include linear accelerators (LINACs) and software for treating cancer and other medical conditions with radiotherapy, radiosurgery, proton therapy, and brachytherapy. The company supplies software for managing cancer clinics, radiotherapy centers, and medical oncology practices. Varian Medical Systems employs more than 7,100 people at manufacturing sites in North America, Europe, and China and approximately 70 sites globally.In August 2020, Siemens Healthineers announced plans to acquire Varian for $16.4 billion. The deal was completed on April 15, 2021. After the merger it will continue to operate independently and will retain its headquarters along with its 10,000 employees.4 billion.
Spatially fractionated radiation therapy (SFRT) planning requires three coordinated tasks: generation of high-dose sphere structures, position-aware optimization, and peak-valley dose ratio evaluation. We present MAAS-SFRThelper, a shared-source Eclipse Scripting Application Programming Interface (ESAPI) plugin that integrates structure generation, geometric-aware optimization, and peak-valley dose ratio evaluation for SFRT into a single workflow inside Varian's Eclipse treatment planning system. The plugin exposes five task-oriented tabs sharing common services for sphere extraction and objective creation. The SphereLattice tab generates sphere lattices using five placement patterns. The Optimization tab searches over candidate lattice positions using a four-metric geometric surrogate score and triggers VMAT optimization and dose calculation. The Evaluation tab implements four analysis modes; its three-dimensional peak-valley classification recovers sphere centers from the lattice structure through a geometric extraction pipeline rather than relying on dose thresholds. We validated all functionality on digital phantoms against analytic ground truth. The plugin is distributed as source code under the Varian Limited Use Software License Agreement. Source code and documentation are publicly available on GitHub.
We present a genetic programming approach to automatically discover convergence acceleration methods for discrete ordinates solutions of neutron transport problems in slab geometry. Classical acceleration methods such as Aitken's delta-squared and Wynn epsilon assume specific convergence patterns and do not generalize well to the broad set of transport problems encountered in practice. We evolved mathematical formulas specifically tailored to SN convergence characteristics in this work. The discovered accelerator, featuring second differences and cross-product terms, achieved an approximately 78% success rate in improving convergence compared to raw sequences, almost double that observed for classical techniques for the problem set considered. This work demonstrates the potential for discovering novel numerical methods in computational physics via genetic programming and attempts to honor Prof. Ganapol's legacy of advancing experimental mathematics applied to neutron transport.
Purpose Spatially fractionated radiation therapy (SFRT) planning requires three coordinated tasks: generation of high-dose sphere structures, position-aware optimization, and peak-valley dose ratio evaluation. In practice, clinicians and researchers address these tasks with a combination of closed-source scripts, research codes, and manual calculation. The absence of a unified, commercially deployable toolkit remains a barrier to multi-institutional SFRT trials. We present MAAS-SFRThelper, a shared-source plugin that integrates structure generation, geometric-aware optimization, and peak-valley dose ratio evaluation for SFRT into a single workflow inside Varian's Eclipse treatment planning system. Acquisition and validation methods MAAS-SFRThelper is a Windows Presentation Foundation (WPF) application built on the Model-View-ViewModel (MVVM) pattern, implemented in C# against ESAPI for Eclipse 15.6 and later. The plugin currently contains five task-oriented tabs that share common services for sphere extraction and objective creation. The SphereLattice tab generates sphere lattices using five placement patterns: hexagonal close-packed, simple cubic, alternating cubic, centroidal Voronoi tessellation, and a constraint-based Monte Carlo method. The SCART tab creates contracted spindle-like boost volumes for stereotactic central ablative radiation therapy. The Optimization tab auto-populates structure list, searches over candidate lattice positions using a four-metric geometric surrogate score, and triggers Eclipse VMAT optimization along with dose calculation on the best candidate. The Evaluation tab implements four analysis modes including 1D, 2D, and 3D PVDR analysis. We validated all functionality on digital phantoms against analytic ground truth. In phantom testing, the optimized lattice achieved 22.5% higher PVDR. Data format and usage The plugin is distributed as source code that compiles to a single dynamic-link library (DLL); all third-party dependencies are bundled at build time. At first launch, users review the Varian Limited Use Software License Agreement (LUSLA) and enter an access code that is emailed upon request. The plugin accepts the patient structure set and, where applicable, a plan with calculated dose; outputs include new structures written directly to the structure set through ESAPI and summary statistics exported as comma-separated value (CSV) files. Source code and documentation are publicly available on GitHub under the LUSLA. Potential applications MAAS-SFRThelper supports clinical lattice SFRT planning with consistent workflows across institutions and standardized peak-valley dose ratio reporting for multi-institutional trials. The shared-services architecture enables community contributions of new placement patterns, evaluation metrics, and validation datasets. These features represent a practical step toward the dosimetric consistency called for in the 2024 NRG Oncology/AAPM consensus on SFRT. The plugin also serves as a platform for research extensions to GRID therapy, minibeam radiation therapy, and other heterogeneous dose-delivery modalities.
Purpose/Objective(s) Volumetric-modulated arc therapy (VMAT)–based Total Body Irradiation and Total Marrow and Lymphoid Irradiation (TMLI) are increasingly implemented on both C-arm and ring-gantry linacs. However, no consensus exists regarding the optimal superior–inferior overlap between adjacent arc sets. This study systematically evaluates the trade-offs between minimal arc overlap—expected to reduce treatment time but potentially compromise dosimetry, robustness, and peripheral target coverage—and maximal overlap, which could improve robustness at the cost of longer delivery. We aimed to quantify how isocenter spacing influences dose quality, setup robustness, and delivery efficiency. Materials/Methods A publicly available knowledge-based planning (KBP) model was used to generate TMLI VMAT plans for the upper body portion of a 50-cm-wide male patient on a ring-gantry linac with a 28 × 28 cm² MLC aperture. A reference plan was created with full 14-cm superior–inferior field overlap to ensure each arc fully overlapped half of its adjacent arcs. Additional plans were generated with progressively reduced overlaps of 12, 10, 8, 6, and 4 cm.Robustness was evaluated by simulating systematic superior–inferior isocenter misalignments. For each plan, alternating isocenter pairs were shifted 1 mm closer and 1 mm farther (net 2-mm add/gap). This process was repeated for misalignment magnitudes of 2, 4, 6, 8, and 10 mm (corresponding to 2–10 mm add/gap), yielding 36 total plans spanning all overlap conditions and misalignment scenarios. Each plan underwent scorecard-based quality analysis for a composite dose quality score as well as: Dmax, Mean lung dose (MLD), 12-Gy coverage of PTV in the arms (PTVarms), estimated beam-on time, and required number of isocenters. Results Increasing isocenter spacing did not reduce treatment time in proportion to the reduced overlap due to increased modulation required to maintain coverage. A meaningful decrease in beam-on time occurred only when spacing increased enough to eliminate an isocenter group (20-cm spacing in this patient), resulting in a 46-second reduction (674 s → 628 s). However, this came with notable dosimetric degradation:• Composite score: 79.7% → 76.4%• Dmax: 133% → 164%• 12-Gy PTVarms coverage: 85% → 81%• MLD remained essentially unchanged (6.5 Gy → 6.4 Gy)Importantly, the severity of misalignment-induced dosimetric degradation was comparable across all isocenter spacing conditions, indicating that reduced overlap does not meaningfully exacerbate head-to-foot misalignment sensitivity. Conclusion Maximal isocenter overlap provides the highest overall dosimetric quality for TMLI on a ring-gantry linac. While increasing isocenter spacing can reduce treatment time when it eliminates an entire isocenter group, this benefit is small and accompanied by clinically meaningful reductions in target coverage and dose quality. The authors therefore recommend maintaining maximal feasible overlap to ensure optimal dosimetric results.
Background:Clinical linear accelerators are an accessible platform for preclinical research on the biological effects of ultra-rapid electron irradiation (FLASH). However, they are not inherently designed for the accurate pulse control required for experiments using a small number of relatively high-dose pulses, and available methods for beam control such as respiratory gating can be error-prone owing to system latency. Purpose:Here we experimentally characterize the temporal latency of the respiratory gating system for controlling beam-on and beam-off at the individual linac pulse level. We use this information to develop accurate pulse delivery methods for preclinical FLASH research. Methods and Materials:We used programmable controller boards and a relay circuit to monitor and control delivery of specific numbers of pulses through the built-in monitor chamber and respiratory gating system of a Varian Trilogy linac. We modeled system response latency as a normally distributed random variable and experimentally recorded the probability of successful pulse delivery and inhibition relative to the time of beam-on and beam-off request signals to derive the mean and standard deviation of latency times at different pulse repetition frequencies. We implemented two methods - an adaptive method using only the delivered-pulse signal, and a synchronization method additionally using the linac's internal pulse-timing signal - and characterized their performance for standard and customized pulse sequences. Results:The mean and standard deviation values of the respiratory gating latency at 60, 90 and 180 Hz pulse repetition frequency were respectively 2.0±0.8 ms, 2.1±2.8 ms, and 2.4±1.9 ms for beam-on and 1.3±0.9 ms, 1.9±2.9 ms, and 1.8±2.1 ms for beam-off. Beam-on and beam-off latencies were similar to each other, and similar across pulse repetition frequencies. Characterizing the latency parameters permitted choosing optimal timing parameters that maximized the rate of successfully delivering the desired number of pulses using both adaptive and synchronization methods, exceeding 99% at 90 Hz for both methods, and reaching 95% (adaptive) and 80% (synchronization) at 180 Hz. This also enabled successful implementation of custom pulse sequences not natively available on the system. Conclusions:We demonstrated that accounting for latency and/or using the ability to read the prior information on expected pulse timing can provide high accuracy in delivering specified numbers of pulses. This reliability is critical for accurate dose delivery in preclinical FLASH research of single fraction and especially fractionated dosing regimens. The ability to generate custom pulse sequences enables more detailed exploration of the temporal dependence of biological FLASH effects.
