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Background: Spot‐scanning Proton Arc (SPArc) has been of significant interest in recent years because of its superior plan quality. Currently, the primary focus of research and development is on deliverability and treatment efficiency. Purpose: To address the challenges in generating a deliverable and efficient SPArc plan for a proton therapy system with a massive gantry, we developed a novel SPArc optimization algorithm (SPArcDMPO) by directly incorporating the machine‐specific parameters such as gantry mechanical constraints and proton delivery sequence. Methods: SPArc delivery sequence model (DSMarc) was built based on the machine‐specific parameters of the prototype arc delivery system, IBA ProteusONE®, including mechanical constraint (maximum gantry speed, acceleration, and deceleration) and proton delivery sequence (energy and spot delivery sequence, and irradiation time). SPArcDMPO resamples and adjusts each control point's delivery speed based on the DSMarc calculation through the iterative approach. In SPArcDMPO, users could set a reasonable arc delivery time during the plan optimization, which aims to minimize the gantry momentum changes and improve the delivery efficiency. Ten cases were selected to test SPArcDMPO. Two kinds of SPArc plans were generated using the same planning objective functions: (1) original SPArc plan (SPArcoriginal); (2) SPArcDMPO plan with a user‐pre‐defined delivery time. Additionally, arc delivery sequence was simulated based on the DSMarc and was compared. Treatment delivery time was compared between SPArcoriginal and SPArcDMPO. Dynamic arc delivery time, the static irradiation time, and its corresponding time differential (time differential = dynamic arc delivery time—static irradiation time) were analyzed, respectively. The total gantry velocity change was accumulated throughout the treatment delivery. Results: With a similar plan quality, objective value, number of energy layers, and spots, both SPArcoriginal and SPArcDMPO plans could be delivered continuously within the ± 1 degree tolerance window. However, compared to the SPArcoriginal, the strategy of SPArcDMPO is able to reduce the time differential from 30.55 ± 11.42%(90 ± 32 s) to 14.67 ± 6.97%(42 ± 20 s), p < 0.01. Furthermore, the corresponding total variations of gantry velocity during dynamic arc delivery are mitigated (SPArcoriginal vs. SPArcDMPO) from 14.73 ± 9.14 degree/s to 4.28 ± 2.42 degree/s, p < 0.01. Consequently, the SPArcDMPO plans could minimize the gantry momentum change based on the clinical user's input compared to the SPArcoriginal plans, which could help relieve the mechanical challenge of accelerating or decelerating the massive proton gantry. Conclusions: For the first time, clinical users not only could generate a SPArc plan meeting the mechanical constraint of their proton system but also directly control the arc treatment speed and momentum changes of the gantry during the plan optimization process. This work paved the way for the routine clinical implementation of proton arc therapy in the treatment planning system. [ABSTRACT FROM AUTHOR]

Background Purpose Methods and Materials Results Conclusion Intensity‐modulated proton therapy (IMPT) optimizes spot intensities and position, providing better conformability. However, the successful application of IMPT is dependent upon addressing the challenges posed by range and setup uncertainties. In order to address the uncertainties in IMPT, robust optimization is essential.This study aims to develop a novel fast algorithm for robust optimization of IMPT with minimum monitor unit (MU) constraint.The study formulates a robust optimization problem and proposes a novel, fast algorithm based on the alternating direction method of multipliers (ADMM) framework. This algorithm enables distributed computation and parallel processing. Ten clinical cases were used as test scenarios to evaluate the performance of the proposed approach. The robust optimization method (RBO‐NEW) was compared with plans that only consider nominal optimization using CTV (NMO‐CTV) without handling uncertainties and PTV (NMO‐PTV) to handle the uncertainties, as well as with conventional robust‐optimized plans (RBO‐CONV). Dosimetric metrics, including D95, homogeneity index, and Dmean, were used to evaluate the dose distribution quality. The area under the root‐mean‐square dose (RMSD)–volume histogram curves (AUC) and dose–volume histogram (DVH) bands were used to evaluate the robustness of the treatment plan. Optimization time cost was also assessed to measure computational efficiency.The results demonstrated that the RBO plans exhibited better plan quality and robustness than the NMO plans, with RBO‐NEW showing superior computational efficiency and plan quality compared to RBO‐CONV. Specifically, statistical analysis results indicated that RBO‐NEW was able to reduce the computational time from 389.70±207.40$389.70\pm 207.40$ to 228.60±123.67$228.60\pm 123.67$ s (p<0.01$p<0.01$) and reduce the mean organ‐at‐risk (OAR) dose from 9.38±12.80$9.38\pm 12.80$% of the prescription dose to 9.07±12.39$9.07\pm 12.39$% of the prescription dose (p<0.05$p<0.05$) compared to RBO‐CONV.This study introduces a novel fast robust optimization algorithm for IMPT treatment planning with minimum MU constraint. Such an algorithm is not only able to enhance the plan's robustness and computational efficiency without compromising OAR sparing but also able to improve treatment plan quality and reliability. [ABSTRACT FROM AUTHOR]

Background: Spot‐scanning proton arc (SPArc) has been drawing significant interests in recent years because of its capability of continuous proton irradiation during the gantry rotation. Previous studies demonstrated SPArc plans were delivered on a prototype of the DynamicARC solution, IBA ProteusONE. Purpose: We built a novel delivery sequence model through an independent experimental approach: the first SPArc delivery sequence model (DSMSPArc). Based on the model, we investigated SPArc treatment efficiency improvement in the routine proton clinical operation. Methods: SPArc test plans were generated and delivered on a prototype of the DynamicARC solution, IBA ProteusONE. An independent gantry inclinometer and the machine logfiles were used to derive the DSMSPArc. Seventeen SPArc plans were used to validate the model's accuracy independently. Two random clinical operation dates (6th January and 22nd March, 2021) from a single‐room proton therapy center (PTC) were selected to quantitatively assess the improvement of treatment efficiency compared to the IMPT. Results: The difference between the logfile and DSMSPArc is about 3.2 ± 4.8%. SPArc reduced 58.1% of the average treatment delivery time per patient compared to IMPT (p < 0.01). Daily treatment throughput could be increased by 30% using SPArc using a single‐room proton therapy system. Conclusions: The first model of dynamic arc therapy is established in this study through an independent experimental approach using logfiles and measurements which allows clinical users and investigators to simulate the dynamic treatment delivery and assess the daily treatment throughput improvement. [ABSTRACT FROM AUTHOR]

Objective. Proton dosimetric uncertainties resulting from the patient's daily setup errors in rotational directions exist even with advanced image-guided radiotherapy techniques. Thus, we developed a new rotational robust optimization SPArc algorithm (SPArcrot) to mitigate the dosimetric impact of the rotational setup error in Raystation ver. 6.02 (RaySearch Laboratory AB, Stockholm, Sweden). Approach. The initial planning CT was rotated ±5° simulating the worst-case setup error in the roll direction. The SPArcrot uses a multi-CT robust optimization framework by taking into account of such rotational setup errors. Five cases representing different disease sites were evaluated. Both SPArcoriginal and SPArcrot plans were generated using the same translational robust optimized parameters. To quantitatively investigate the mitigation effect from the rotational setup errors, all plans were recalculated using a series of pseudo-CT with rotational setup error (±1°/±2°/±3°/±5°). Dosimetric metrics such as D98% of CTV, and 3D gamma analysis were used to assess the dose distribution changes in the target and OARs. Main results. The magnitudes of dosimetric changes in the targets due to rotational setup error were significantly reduced by the SPArcrot compared to SPArc in all cases. The uncertainties of the max dose to the OARs, such as brainstem, spinal cord and esophagus were significantly reduced using SPArcrot. The uncertainties of the mean dose to the OARs such as liver and oral cavity, parotid were comparable between the two planning techniques. The gamma passing rate (3%/3 mm) was significantly improved for CTV of all tumor sites through SPArcrot. Significance. Rotational setup error is one of the major issues which could lead to significant dose perturbations. SPArcrot planning approach can consider such rotational error from patient setup or gantry rotation error by effectively mitigating the dose uncertainties to the target and in the adjunct series OARs. [ABSTRACT FROM AUTHOR]

Objective. We proposed an experimental approach to build a precise machine-specific beam delivery time (BDT) prediction and delivery sequence model for standard, volumetric, and layer repainting delivery based on a cyclotron accelerator system. Approach. Test fields and clinical treatment plans’ log files were used to experimentally derive three main beam delivery parameters that impacted BDT: energy layer switching time (ELST), spot switching time, and spot drill time. This derived machine-specific model includes standard, volumetric, and layer repainting delivery sequences. A total of 103 clinical treatment fields were used to validate the model. Main results. The study found that ELST is not stochastic in this specific machine. Instead, it is actually the data transmission time or energy selection time, whichever takes longer. The validation showed that the accuracy of each component of the BDT matches well between machine log files and the model’s prediction. The average total BDT was about (âˆ'0.74 ± 3.33)% difference compared to the actual treatment log files, which is improved from the current commercial proton therapy system’s prediction (67.22%±26.19%). Significance. An accurate BDT prediction and delivery sequence model was established for an cyclotron-based proton therapy system IBA ProteusPLUS®. Most institutions could adopt this method to build a machine-specific model for their own proton system. [ABSTRACT FROM AUTHOR]

In this paper, an effective spectral-Galerkin method was proposed for the dissipative dynamics of a Hamiltonian system. By introducing a suitable Sobolev space, we establish a weak form and corresponding discrete scheme. Compared with the existing methods, our method can achieve higher accuracy and shorter computational time. We also simulate quantum cooling in an optomechanical system as an example to show the advantage of our method. Our method provides a promising platform for studying the dissipative dynamics of nonlinear and complex Hamiltonian systems. [ABSTRACT FROM AUTHOR]

Objective . To quantitatively investigate the impact of spot position error (PE) on the dose distribution in (Spot-scanning arc therapy) SPArc plans compared to Intensity-Modulated Proton Therapy (IMPT). Approach. Twelve representative cases, including brain, lung, liver, and prostate cancers, were retrospectively selected. Spot PEs were simulated during dynamic SPArc treatment delivery. Two types of errors were generated, including random error and systematic error. Two different probability distributions of random errors were used (1) Gaussian distribution (PE ran-GS ) (2) uniform distribution (PE ran-UN ). In PE ran-UN , four sub-scenarios were considered: 25%, 50%, 75%, and 100% spots were randomly selected in various directions on the scale of 0-1 mm or 0-2 mm of PE. Additionally, systematic error was simulated by shifting all the spot uniformly by 1 or 2 mm in various directions (PE sys ). Gamma-index Passing Rate (GPR) is applied to assess the dosimetric perturbation quantitatively. Main results. For PE ran-GS in the 1 mm scenario, both SPArc and IMPT are comparable with a GPR exceeding 99%. However, for PE ran-GS in 2 mm scenario, SPArc could provide better GPR. As PE sys of 2 mm, SPArc plans have a much better GPR compared to IMPT plans: SPArc's GPR is 99.59 ± 0.47%, 93.82 ± 4.07% and 64.58 ± 15.83% for 3 mm/3%, 2 mm/2% and 1 mm/1% criteria compared to IMPT with 97.49 ± 2.44%, 84.59 ± 4.99% and 42.02 ± 6.31%. Significance. Compared to IMPT, SPArc shows better dosimetric robustness in spot PEs. This study presents the first simulation results and the methodology that serves as a reference to guide future investigations into the accuracy and quality assurance of SPArc treatment delivery., (© 2024 Institute of Physics and Engineering in Medicine.)