Spot-scanning Proton Arc Research Articles

Explore the feasibility of using spot-scanning proton arc therapy for a synchrotron accelerator-based proton therapy system - A simulation study.

The aim of this study was to evaluate the feasibility and plan quality of spot-scanning proton arc therapy (SPArc) using a synchrotron-accelerator-based proton therapy system compared to intensity-modulated proton therapy (IMPT). Five representative disease sites, including head and neck, lung, liver, brain chordoma, and prostate cancers, were retrospectively selected. Both IMPT and SPArc plans are generated with the HITACHI ProBEAT PBS system's minimum MU constraints and physics beam model. The SPArc plans are generated with 2.5° sampling frequency. The static delivery time was simulated based on the previously published synchrotron delivery sequence model, and the dynamic delivery time was simulated using a proton arc gantry mechanical model integrated with the synchrotron delivery sequence. Both dosimetric plan quality and delivery efficiency are evaluated. A superior plan quality is reached compared with the IMPT plans generated for the same disease site. However, a relatively prolonged static and dynamic delivery time post new challenge, as static time increased by 49.22% and dynamic time 59.10% on average. This study presents the first simulation results of delivering the SPArc plans using a synchrotron-accelerated proton therapy system. The result shows its feasibility and limitations, which could guide future development.

Rotationally Intensified Proton Lattice: A Novel Lattice Technique Using Spot-Scanning Proton Arc Therapy

Rotationally Intensified Proton Lattice: A Novel Lattice Technique Using Spot-Scanning Proton Arc Therapy

Impact of Relative Biologic Effectiveness for Proton Therapy for Head and Neck and Skull-Base Tumors: A Technical and Clinical Review.

Proton therapy has emerged as a crucial tool in the treatment of head and neck and skull-base cancers, offering advantages over photon therapy in terms of decreasing integral dose and reducing acute and late toxicities, such as dysgeusia, feeding tube dependence, xerostomia, secondary malignancies, and neurocognitive dysfunction. Despite its benefits in dose distribution and biological effectiveness, the application of proton therapy is challenged by uncertainties in its relative biological effectiveness (RBE). Overcoming the challenges related to RBE is key to fully realizing proton therapy's potential, which extends beyond its physical dosimetric properties when compared with photon-based therapies. In this paper, we discuss the clinical significance of RBE within treatment volumes and adjacent serial organs at risk in the management of head and neck and skull-base tumors. We review proton RBE uncertainties and its modeling and explore clinical outcomes. Additionally, we highlight technological advancements and innovations in plan optimization and treatment delivery, including linear energy transfer/RBE optimizations and the development of spot-scanning proton arc therapy. These advancements show promise in harnessing the full capabilities of proton therapy from an academic standpoint, further technological innovations and clinical outcome studies, however, are needed for their integration into routine clinical practice.

A machine learning-based approach to predict energy layer for each field in spot-scanning proton arc therapy for lung cancer: A feasibility study.

Determining the optimal energy layer (EL) for each field, under considering both dose constraints and delivery efficiency, is crucial to promoting the development of proton arc therapy (PAT) technology. This study aimed to explore the feasibility and potential clinical benefits of utilizing machine learning (ML) technique to automatically select EL for each field in PAT plans of lung cancer. Proton Bragg peak position (BPP) was employed to characterize EL. The ground truth BPPs for each field were determined using the modified ELO-SPAT framework. Features in geometric, water-equivalent thicknesses (WET) and beamlet were defined and extracted. By analyzing the relationship between the extracted features and ground truth, a polynomial regression model with L2-norm regularization (Ridge regression) was constructed and trained. The performance of the regression model was reported as an error between the predictions and the ground truth. Besides, the predictions were used to make PAT plans (PAT_PRED). These plans were compared with those using the ground truth BPPs (PAT_TRUTH) and the mid-WET of the target volumes (PAT_MID) in terms of relative biological effectiveness-weighted dose (RWD) distributions. One hundred ten patients with lung cancer, a total of 7920 samples, were enrolled retrospectively, with 5940 cases randomly selected as the training set and the remaining 1980 cases as the testing set. Nine patients (648 samples) were collected additionally to evaluate the regression model in terms of plan quality and robustness. With regard to the prediction errors, the root mean squared errors and mean absolute errors between the ML-predicted and ground truth BPPs for the testing set were 9.165 and 6.572mm, respectively, indicating differences of approximately two to three ELs. As for plan quality, the PAT_TRUTH and PAT_PRED plans performed similarly in terms of plan robustness, target coverage and organs at risk (OARs) protection, with differences smaller than 0.5Gy(RBE). This trend was also observed for dose conformity and uniformity. The PAT_MID plans produced the lowest robustness index and lowest doses to OARs, along with the highest heterogeneity index, indicating better protection for OARs, improved plan robustness, but compromised dose homogeneity. Additionally, for relatively small tumor sizes, the PAT_MID plan demonstrated a notably poor dose conformity index. Within this cohort under investigation, our study demonstrated the feasibility of using ML technique to predict ELs for each field, offering a fast (within 2 s) and memory-efficient reduced way to select ELs for PAT plan.

First direct machine-specific parameters incorporated in Spot-scanning Proton Arc (SPArc) optimization algorithm.

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. 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. 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. 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. 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.

A novel planning framework for efficient spot-scanning proton arc therapy via particle swarm optimization (SPArc-particle swarm)

Objective. Delivery efficiency is the bottleneck of spot-scanning proton arc therapy (SPArc) because of the numerous energy layers (ELs) ascending switches. This study aims to develop a new algorithm to mitigate the need for EL ascending via water equivalent thickness (WET) sector selection followed by particle swarm optimization (SPArc-particle swarm). Approach. SPArc-particle swarm divided the full arc trajectory into the optimal sectors based on K-means clustering analysis of the relative mean WET. Within the sector, particle swarm optimization was used to minimize the total energy switch time, optimizing the energy selection integrated with the EL delivery sequence and relationship. This novel planning framework was implemented on the open-source platform matRad (Department of Medical Physics in Radiation Oncology, German Cancer Research Center-DKFZ). Three representative cases (brain, liver, and prostate cancer) were selected for testing purposes. Two kinds of plans were generated: SPArc_seq and SPArc-particle swarm. The plan quality and delivery efficiency were evaluated. Main results. With a similar plan quality, the delivery efficiency was significantly improved using SPArc-particle swarm compared to SPArc_seq. More specifically, it reduces the number of ELs ascending switching compared to the SPArc_seq (from 21 to 7 in the brain, from 21 to 5 in the prostate, from 21 to 6 in the liver), leading to a 16%–26% reduction of the beam delivery time (BDT) in the SPArc treatment. Significance. A novel planning framework, SPArc-particle swarm, could significantly improve the delivery efficiency, which paves the roadmap towards routine clinical implementation.

Development of a standalone delivery sequence model for proton arc therapy.

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. 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. 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. 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. 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.

The first investigation of spot-scanning proton arc (SPArc) delivery time and accuracy with different delivery tolerance window settings

Objective. To investigate the impact of various delivery tolerance window settings on the treatment delivery time and dosimetric accuracy of spot-scanning proton arc (SPArc) therapy. Approach. SPArc plans were generated for three representative disease sites (brain, lung, and liver cancer) with an angle sampling frequency of 2.5°. An in-house dynamic arc controller was used to simulate the arc treatment delivery with various tolerance windows (±0.25, ±0.5, ±1, and ±1.25°). The controller generates virtual logfiles during the arc delivery simulation, such as gantry speed, acceleration and deceleration, spot position, and delivery sequence, similar to machine logfiles. The virtual logfile was then imported to the treatment planning system to reconstruct the delivered dose distribution and compare it to the initial SPArc nominal plan. A three-dimensional gamma index was used to quantitatively assess delivery accuracy. Total treatment delivery time and relative lost time (dynamic arc delivery time—fix beam delivery time)/fix beam delivery time) were reported. Main Results. The 3D gamma passing rate (GPR) was greater than 99% for all cases when using 3%/3 mm and 2%/2 mm criteria and the GPR (1%/1 mm criteria) degraded as the tolerance window opens. The total delivery time for dynamic arc delivery increased with the decreasing delivery tolerance window length. The average delivery time and the relative lost time (%) were 630 ± 212 s (253% ± 68%), 322 ± 101 s (81% ± 31%), 225 ± 60 s (27% ± 16%), 196 ± 41 s (11% ± 6%), 187 ± 29 s (6% ± 1%) for tolerance windows ±0.25, ±0.5, ±1, and ±1.25° respectively. Significance. The study quantitatively analyzed the dynamic SPArc delivery time and accuracy with different delivery tolerance window settings, which offer a critical reference in the future SPArc plan optimization and delivery controller design.

Deep Learning-Based Dose Prediction Model for Automated Spot-Scanning Proton Arc Planning

Spot-scanning proton arc (SPArc) is a novel technique that employs a planning optimization algorithm to select the energies and positions of spots along a dynamic rotational arc trajectory. The SPArc technique has the potential to achieve superior dose conformality and treatment delivery efficiency over intensity-modulated proton therapy. However, creating such a SPArc plan using existing approaches is time-consuming and computationally extensively. This study investigated the feasibility of using the deep learning (DL) technique to predict the 3D dose distribution of the SPArc treatment plan, leveraging the prior knowledge acquired from conventional intensity-modulated radiation therapy (IMRT) plans. A DL model, 3D-Unet with residual connections and attention gates, was trained using an open-source database of CT images, critical structures, and IMRT plans from 340 head and neck cancer patients (HNC) as the base model. Transfer learning technique was applied to fine-tune the model parameters using the SPArc treatment plans created on the HNC patients from an in-house dataset, where the SPArc treatment plans (including control point sampling, energy layer distribution, arc trajectory, etc.,) were optimized using a previously developed iterative approach. The performance of the DL model was evaluated by comparing predicted and planned doses over 17 SPArc treatment plans by using 4-fold cross-validation. The SPArc planning time per patient was 8∼12 hours, while the dose prediction time was reduced to 2∼3 minutes using the proposed DL model. The deviation of D95 in the target was (-1.8±1.6) %. The deviation of the mean dose in the parotids, cord, mandible, and brainstem were (2.5±6.5) %, (-0.5±4.3) %, (1.4±3.9) %, and (3.4±8) % of the prescription, respectively. The dice similarity coefficients of the 80%, 70%, and 60% isodose lines were (0.9±0.09), (0.93±0.01), and (0.94±0.01), respectively. Our results demonstrate that a DL-based dose prediction model can be created with a limited number of SPArc treatment plans through transfer learning. The DL model can directly predict the 3D dose distribution in minutes for automated planning. This study paves the roadmap to develop a quick clinical decision platform for the optimal selection among the multi-treatment modalities.

A Novel Planning and Delivery Technology: Dose, Dose Rate and Linear Energy Transfer (LET) Optimization Based on Spot-Scanning Proton Arc Therapy FLASH (SPLASHLET)

To achieve a high conformal dose with Linear Energy Transfer (LET) optimized FLASH proton therapy, we introduced a new planning and delivery technique concept, the voxel-wised optimization of LET distribution and dose rate based on scanning arc therapy (SPLASHLET) MATERIALS/METHODS: The algorithm optimizes (1) the clinical dose-volume constraint based on dose distribution and (2) the clinical LET-volume constraint based on LET distribution using Alternating Direction Method of Multipliers (ADMM) with Limited-memory BFGS solver by minimizing the monitor unit (MU) constraint on spot weight and (3) the effective dose-average dose rate by minimizing the accelerator's beam current sequentially. Such optimization framework enables the high dose conformal dynamic arc therapy with the capability of LET painting with voxel-based FLASH dose rate in an open-source proton planning platform (MatRad, Department of Medical Physics in Radiation Oncology, German Cancer Research Center-DKFZ). It aiming to minimize the overall cost function value combined with plan quality and voxel-based LET and dose rate constraints. Three representative cases (brain, liver and prostate cancer) were used for testing purposes. Dose-volume histogram (DVH), LET volume histogram (LVH) dose rate volume histogram (DRVH) and dose rate map were assessed compared to the original SPArc plan (SPArcoriginal). SPLASHLET plan could offer comparable plan quality compared to SPArcoriginal plan. The DRVH results indicated that SPArcoriginal could not achieve FLASH using the clinic beam current configuration, while SPLASHLET could significantly not only improve V40Gy/s in target and region of interest (ROI) but also improve the mean LET in the target and reduce the high LET in organ at risk (OAR) in comparison with SPArcoriginal (Table 1). SPLASHLET offers the first LET painting with voxel-based ultra-dose-rate and high-dose conformity treatment using proton beam therapy. Such technique has the potential to take full vantage of LET painting, FLASH and SPArc.

A Novel Ultrahigh-Dose-Rate Proton Therapy Technology: Spot-Scanning Proton Arc Therapy + FLASH (SPLASH)

To take full advantage of FLASH dose rate (40 Gy/s) and high-dose conformity, we introduce a novel optimization and delivery technique, the spot-scanning proton arc therapy (SPArc)+FLASH (SPLASH). SPLASH framework was implemented in an open-source proton planning platform (MatRad, Department of Medical Physics in Radiation Oncology, German Cancer Research Center). It optimizes with the clinical dose-volume constraint based on dose distribution and the dose-average dose rate by minimizing the monitor unit constraint on spot weight and accelerator beam current sequentially, enabling the first dynamic arc therapy with voxel-based FLASH dose rate. This new optimization framework minimizes the overall cost function value combined with plan quality and voxel-based dose-rate constraints. Three representative cases (brain, liver, and prostate cancer) were used for testing purposes. Dose-volume histogram, dose-rate-volume histogram, and dose-rate map were compared among intensity modulated proton radiation therapy (IMPT), SPArc, and SPLASH. SPLASH/SPArc could offer superior plan quality over IMPT in terms of dose conformity. The dose-rate-volume histogram results indicated SPLASH could significantly improve V40 Gy/s in the target and region of interest for all tested cases compared with SPArc and IMPT. The optimal beam current per spot is simultaneously generated, which is within the existing proton machine specifications in the research version (<200 nA). SPLASH offers the first voxel-based ultradose-rate and high-dose conformity treatment using proton beam therapy. Such a technique has the potential to fit the needs of a broad range of disease sites and simplify clinical workflow without applying a patient-specific ridge filter, which has never before been demonstrated.

Introduce a rotational robust optimization framework for spot-scanning proton arc (SPArc) therapy

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.

A Novel Ultra-High Dose Rate Proton Therapy Technology: Spot-Scanning Proton Arc Therapy FLASH (SPLASH)

<h3>Purpose/Objective(s)</h3> The flash dose rate on the order of 40 Gy/s has the potential to spare normal tissue with iso-effective tumor growth delay. Meanwhile, the spot-scanning proton arc therapy (SPArc), demonstrated its superior dosimetric conformity and plan robustness compared to the conventional intensity modulated proton therapy (IMPT). To take the full advantage of flash dose rate and the high dose conformity, we introduce a novel optimization and delivery technique, SPArc FLASH (SPLASH). <h3>Materials/Methods</h3> SPLASH framework was implemented in an open-source proton planning platform (matRad). It optimizes the spot monitor unit (MU) weighting and cyclotron beam current per spot simultaneously. More specifically, it simultaneously optimized with the clinical dose-volume constraint based on dose distribution and optimized the dose-average dose rate by minimizing the monitor unit constraint on spot weight and maximum cyclotron beam current to minimize the cost function value combined by dose and dose rate. An intracranial target (volume = 20.8cc) case was used for testing purpose. Prescription is 50 Gy in 25 fractions. Dose-volume histogram, dose averaged dose rate histogram and dose rate map per spot were compared between SPArc and SPLASH. <h3>Results</h3> The dose-averaged dose rate histogram indicated that flash dose rate (40Gy/s) could not be achieved using the original SPArc with the current clinical beam current configuration. With a same plan quality, SPLASH improved dose-averaged dose rate of V<i>r</i> > 40 Gy/s (Volume of dose rate larger than 40Gy/s) in PTV, optic chiasm, optic nerve and brainstem from 0%, 0%, 0%, 0% in SPArc plan to 100%, 100%, 100%, 75%, respectively. The optimal cyclotron beam current per spot is simultaneously generated. <h3>Conclusion</h3> The SPLASH offers simultaneously optimizing dose distribution and dose rate. It could generate a treatment plan with a superior conformal dose distribution and ultra-high dose rate, which has never been demonstrated before.

Redefine the Role of Spot-Scanning Proton Beam Therapy for the Single Brain Metastasis Stereotactic Radiosurgery.

PurposeTo explore the role of using Pencil Beam Scanning (PBS) proton beam therapy in single lesion brain stereotactic radiosurgery (SRS), we developed and validated a dosimetric in silico model to assist in the selection of an optimal treatment approach among the conventional Volumetric Modulated Arc Therapy (VMAT), Intensity Modulated Proton Therapy (IMPT) and Spot-scanning Proton Arc (SPArc).Material and MethodsA patient’s head CT data set was used as an in silico model. A series of targets (volume range from 0.3 cc to 33.03 cc) were inserted in the deep central and peripheral region, simulating targets with different sizes and locations. Three planning groups: IMPT, VMAT, and SPArc were created for dosimetric comparison purposes and a decision tree was built based on this in silico model. Nine patients with single brain metastases were retrospectively selected for validation. Multiple dosimetric metrics were analyzed to assess the plan quality, such as dose Conformity Index (CI) (ratio of the target volume to 100% prescription isodose volume); R50 (ratio of 50% prescription isodose volume to the target volume); V12Gy (volume of brain tissue minus GTV receiving 12 Gy), and mean dose of the normal brain. Normal tissue complication probability (NTCP) of brain radionecrosis (RN) was calculated using the Lyman-Kutcher-Burman (LKB) model and total treatment delivery time was calculated. Six physicians from different institutions participated in the blind survey to evaluate the plan quality and rank their choices.ResultsThe study showed that SPArc has a dosimetric advantage in the V12Gy and R50 with target volumes > 9.00 cc compared to VMAT and IMPT. A significant clinical benefit can be found in deep centrally located lesions larger than 20.00 cc using SPArc because of the superior dose conformity and mean dose reduction in healthy brain tissue. Nine retrospective clinical cases and the blind survey showed good agreement with the in silico dosimetric model and decision tree. Additionally, SPArc significantly reduced the treatment delivery time compared to VMAT (SPArc 184.46 ± 59.51s vs. VMAT: 1574.78 ± 213.65s).ConclusionThe study demonstrated the feasibility of using Proton beam therapy for single brain metastasis patients utilizing the SPArc technique. At the current stage of technological development, VMAT remains the current standard modality of choice for single lesion brain SRS. The in silico dosimetric model and decision tree presented here could be used as a practical clinical decision tool to assist the selection of the optimal treatment modality among VMAT, IMPT, and SPArc in centers that have both photon and proton capabilities.

Treatment planning in arc proton therapy: Comparison of several optimization problem statements and their corresponding solvers

Arc proton therapy (ArcPT) is an emerging modality in cancer treatments. It delivers the proton beams following a sequence of irradiation angles while the gantry is continuously rotating around the patient. Compared to conventional proton treatments (intensity modulated proton therapy, IMPT), the number of beams is significantly increased bringing new degrees of freedom that leads to potentially better cancer care. However, the optimization of such treatment plans becomes more complex and several alternative statements of the problem can be considered and compared in order to solve the ArcPT problem. Three such problem statements, distinct in their mathematical formulation and properties, are investigated and applied to solving the ArcPT optimization problem. They make use of (i) fast iterative shrinkage-thresholding algorithm (FISTA), (ii) local search (LS) and (iii) mixed-integer programming (MIP). The treatment plans obtained with those methods are compared among them, but also with IMPT and an existing state-of-the-art method: Spot-Scanning Proton Arc (SPArc). MIP stands out at low scale problems both in terms of dose quality and time delivery efficiency. FISTA shows high dose quality but experiences difficulty to optimize the energy sequence while LS is mostly the antagonist. This detailed study describes independent approaches to solve the ArcPT problem and depending on the clinical case, one should be cautiously picked rather than the other. This paper gives the first formal definition of the problem at stake, as well as a first reference benchmark. Finally, empirical conclusions are drawn, based on realistic assumptions.

The First Modeling of the Spot-Scanning Proton Arc (SPArc) Delivery Sequence and Investigating its Efficiency Improvement in the Clinical Proton Treatment Workflow

To quantitatively model a precise spot-scanning proton arc (SPArc) delivery sequence and assess its efficiency improvement in the routine proton clinical operation.The SPArc delivery sequence model (DSMSPArc) includes two kinds of parameters: (1) mechanical parameters (the maximum gantry velocity, acceleration, and deceleration speed). (2) irradiation parameters (tolerance window and buffer, spot scanning speed, energy layer switching time, and burst switching time). An independent gantry inclinometer was used to measure mechanical parameters. Log files were used to derive the irradiation parameters through a series of SPArc test plans. The in-house DSMSPArc was established by fitting both mechanical and irradiation parameters. Eight SPArc plans from different disease sites (brain, HN, lung, and liver cancer) were used to validate the model's accuracy. To quantitatively assess the treatment efficiency improvement compared to the clinical IMPT, a random clinical operation date of our proton center (total 21 cases on Jan 6th 2021) was selected, and SPArc plans were generated for all the cases. The DSMSPArc was used to simulate the SPArc treatment delivery sequence and compared to the clinical IMPT treatment logfiles.The relative difference of treatment time between log files and DSMSPArc's prediction was 6.1% ± 3.9% on average, and the gantry angle vs. delivery time showed a good agreement between the DSMSPArc and log file. Additionally, the SPArc plan could effectively save two hours out of 10 hours of clinical operation by simplifying the treatment workflow for a single room proton therapy center. The average treatment delivery time (including gantry rotation and irradiation) per patient was reduced to 226 ± 149s using SPArc compared to 665 ± 407s using IMPT (P < 0.01).This is the first modeling of the SPArc delivery sequence, which paves the roadmap for implementing the delivery speed and time into the SPArc optimization algorithm. Additionally, SPArc can offer a superior delivery efficiency to improve clinical treatment throughput, compared to IMPT.

Lung Stereotactic Body Radiotherapy (SBRT) Using Spot-Scanning Proton Arc (SPArc) Therapy: A Feasibility Study

We developed a 4D interplay effect model to quantitatively evaluate breathing-induced interplay effects and assess the feasibility of utilizing spot-scanning proton arc (SPArc) therapy for hypo-fractionated lung stereotactic body radiotherapy (SBRT). The model was then validated by retrospective application to clinical cases.A digital lung 4DCT phantoms was used to mimic targets in diameter of 3cm with breathing motion amplitudes: 5, 10, 15, and 20 mm, respectively. Two planning groups based on robust optimization were generated: (1) Two-field Intensity Modulated Proton Therapy (IMPT) plans and (2) SPArc plans via a partial arc. 5000 cGy was prescribed to the internal target volume (ITV) in 5 fractions. To quantitatively assess the breathing induced interplay effect, the 4D dynamic dose was calculated by synchronizing the breathing pattern with the simulated proton machine delivery sequence, including IMPT, Volumetric repainting (IMPTvolumetric), iso-layered repainting (IMPTlayer) and SPArc. Ten lung patients' 4DCT previously treated with VMAT SBRT, were used to validate the digital lung tumor model. Normal tissue complicated probability (NTCP) of chestwall toxicity was calculated.Target dose were degraded as the tumor motion amplitude increased. The 4D interplay effect phantom model indicated that motion mitigation effectiveness using SPArc was about five times of IMPTvolumetric or IMPTlayer using maximum MU/spot as 0.5 MU at 20 mm motion amplitude. The retrospective study showed that SPArc has an advantage in normal tissue sparing. The probability of chestwall's toxicity were significantly improved from 40.2% ± 29.0% (VMAT) (P = 0.01) and 16.3% ± 12.0% (IMPT) (P = 0.01) to 10.1% ± 5.4% (SPArc). SPArc significantly mitigated the interplay effect compared to IMPT, where the average target D99 in single fraction 4D dynamic dose throughout patients was 4876 ± 107 cGy[RBE] (IMPT) vs. 4913 ± 64 cGy[RBE] (SPArc) (P < 0.01).SPArc is an efficient treatment modality capable of effectively mitigating the interplay effect in lung SBRT compared to IMPT with repainting. Such a technique has the potential to further reduce the normal tissue complexity compared to the VMAT.G. Liu: None. L. Zhao: None. A. Qin: None. R.L. Deraniyagala: None. C.W. Stevens: None. D. Yan: None. X. Li: None. X. Ding: None.

Investigate the Feasibility of Using CBCT to Assess the Dose Validation for Spot-Scanning Proton Arc (SPArc) Therapy for Advanced Staged Lung Cancer Treatment

<h3>Purpose/Objective(s)</h3> Spot-scanning proton arc therapy has been developed to improve the dose conformity and plan quality in varieties of disease indications, including advanced staged lung cancer treatment. Recently, the artificial Intelligent based synthetic CT method has been introduced in the proton clinic to assess the plan's robustness daily using CBCT. This study investigated the dose validation accuracy of using AI based synthetic-CT for SPArc in comparison with conventional IMPT delivery. <h3>Materials/Methods</h3> Twenty-seven advanced stage lung cancer patients' CT/CBCT data sets were used for training. A Generative-Adversarial-Network (GAN) model, including one generator with Residual-Unet (Res-GAN) architecture and one discriminator using a multi-layer convolutional neural net (CNN), were trained from scratch to predict synthetic CT from CBCT, with random 3D B-spline deformation as data augmentation. Another six patients with the same day re-plan CT (reCT) and CBCT were retrospectively selected for the dose validation comparison. Synthetic CT (synCT) was predicted from CBCT and compared to reCT in terms of Mean Absolute Error (MAE). Both IMPT 2-3 field and SPArc plans were generated on the reCT using the same robust optimization parameters (± 3mm setup and ± 3.5% range uncertainty) as well as planning objectives. Then, both plans were recalculated on the synCT from the same day CBCT. 3D Gamma Index was used to quantitatively assess the dose distribution discrepancy between the synCT and reCT for both IMPT and SPArc techniques. <h3>Results</h3> The result show that the image similarity between the synCT and reCT is very close with average MAE of 32.6 ± 7.5 HU. Overall, SPArc plans have superior 3D gamma index passing rate compared the multi-field IMPT plan in terms of 1%1mm; 2%2mm, 3%/3mm (<i>P</i> < 0.05) as shown in the table. In other words, SPArc effectively mitigated the dosimetric impact from the proton range differences or CT HU error in the synthetic CT. <h3>Conclusion</h3> Deep-learning CBCT-based synthetic CT could be used in dose validation not only for IMPT but also for SPArc even though SPArc plan has better dose conformity to the target. This finding paves the road map to the CBCT-based adaptation platform for SPArc adaptive therapy of lung cancer patient.

A Direct Machine-Specific Parameters Incorporated Spot-Scanning Proton Arc (SPArc) Algorithm

To address the challenges of generating a deliverable and efficient spot-scanning proton arc (SPArc) plan for a proton therapy system. We developed a novel SPArc optimization algorithm (SPArcDMSP) by directly incorporating the machine-specific parameters such as mechanical constraints and delivery sequence.A SPArc delivery sequence model (DSMarc) was built based on the machine-specific parameters of the prototype arc delivery system. The SPArcDMSP resamples and adjusts each control point's delivery speed based on the DSMarc calculation through the iterative approach. Users could set the expected arc delivery time and gantry max acceleration as a mechanical constraint during the SPArcDMSP optimization. Four cases (brain, liver, head neck, liver, and lung cancer) were selected to test SPArcDMSP. Two kinds of SPArc plans were generated using the same planning objective functions: (1) SPArcDMSP plan meeting the maximum allowable gantry acceleration speed (0.6deg/s2); (2) SPArcDMSP-user-speed plan with a user pre-defined delivery time and acceleration speed < 0.1 deg/s2. Arc delivery sequence such as gantry speed, delivery time was simulated based on the DSMarc and was compared.With a similar objective value, number of energy layers and spots, both SPArcDMSP and SPArcDMSP-user-speed plans could be delivered continuously within the ± 1 degree tolerance window (Table 1). The SPArcDMSP-user-speed plan could minimize the gantry momentum change based on clinical users input compared to the SPArcDMSP, which could help relieve mechanical challenge to accelerate or decelerate the hundreds of tons of gantry.For the first time, clinical users not only could generate a SPArc plan meeting the mechanical constraint of their proton system but also directly controlled the arc treatment speed and momentum changes of the gantry during the plan optimization. This work paved the roadmap for the clinical implementation of proton arc therapy in the treatment planning system.

Linear Energy Transfer Incorporated Spot-Scanning Proton Arc Therapy Optimization: A Feasibility Study.

PurposeTo integrate dose-averaged linear energy transfer (LETd) into spot-scanning proton arc therapy (SPArc) optimization and to explore its feasibility and potential clinical benefits.MethodsAn open-source proton planning platform (OpenREGGUI) has been modified to incorporate LETd into optimization for both SPArc and multi-beam intensity-modulated proton therapy (IMPT) treatment planning. SPArc and multi-beam IMPT plans with different beam configurations for a prostate patient were generated to investigate the feasibility of LETd-based optimization using SPArc in terms of spatial LETd distribution and plan delivery efficiency. One liver and one brain case were studied to further evaluate the advantages of SPArc over multi-beam IMPT.ResultsWith similar dose distributions, the efficacy of spatially optimizing LETd distributions improves with increasing number of beams. Compared with multi-beam IMPT plans, SPArc plans show substantial improvement in LETd distributions while maintaining similar delivery efficiency. Specifically, for the liver case, the average LETd in the GTV was increased by 124% for the SPArc plan, and only 9.6% for the 2-beam IMPT plan compared with the 2-beam non-LETd optimized IMPT plan. In case of LET optimization for the brain case, the SPArc plan could effectively increase the average LETd in the CTV and decrease the values in the critical structures while smaller improvement was observed in 3-beam IMPT plans.ConclusionThis work demonstrates the feasibility and significant advantages of using SPArc for LETd-based optimization, which could maximize the LETd distribution wherever is desired inside the target and averts the high LETd away from the adjacent critical organs-at-risk.