Robust radiotherapy planning

The potential of mixed carbon-helium beams for online treatment verification: a simulation and treatment planning study

Objective.Recently, a new and promising approach for range verification was proposed. This method requires the use of two different ion species. Due to their equal magnetic rigidity, fully ionized carbon and helium ions can be simultaneously accelerated in accelerators like synchrotrons. At sufficiently high treatment energies, helium ions can exit the patient distally, reaching approximately three times the range of carbon ions at an equal energy per nucleon. Therefore, the proposal involves adding a small helium fluence to the carbon ion beam and utilizing helium as an online range probe during radiation therapy. This work aims to develop a software framework for treatment planning and motion verification in range-guided radiation therapy using mixed carbon-helium beams.Approach.The developed framework is based on the open-source treatment planning toolkit matRad. Dose distributions and helium radiographs were simulated using the open-source Monte Carlo package TOPAS. Beam delivery system parameters were obtained from the Heidelberg Ion Therapy Center, and imaging detectors along with reconstruction were facilitated by ProtonVDA. Methods for reconstructing the most likely patient positioning error scenarios and the motion phase of 4DCT are presented for prostate and lung cancer sites.Main results.The developed framework provides the capability to calculate and optimize treatment plans for mixed carbon-helium ion therapy. It can simulate the treatment process and generate helium radiographs for simulated patient geometry, including small beam views. Furthermore, motion reconstruction based on these radiographs seems possible with preliminary validation.Significance.The developed framework can be applied for further experimental work with the promising mixed carbon-helium ion implementation of range-guided radiotherapy. It offers opportunities for adaptation in particle therapy, improving dose accumulation, and enabling patient anatomy reconstruction during radiotherapy.

Simultaneous dose and dose rate optimization via dose modifying factor modeling for FLASH effective dose

BACKGROUND

Although the FLASH radiotherapy (FLASH) can improve the sparing of organs-at-risk (OAR) via the FLASH effect, it is generally a tradeoff between the physical dose coverage and the biological FLASH coverage, for which the concept of FLASH effective dose (FED) is needed to quantify the net improvement of FLASH, compared to the conventional radiotherapy (CONV).

PURPOSE

This work will develop the first-of-its-kind treatment planning method called simultaneous dose and dose rate optimization via dose modifying factor modeling (SDDRO-DMF) for proton FLASH that directly optimizes FED.

METHODS

SDDRO-DMF models and optimizes FED using FLASH dose modifying factor (DMF) models, which can be classified into two categories: (1) the phenomenological model of the FLASH effect, such as the FLASH effectiveness model (FEM); (2) the mechanistic model of the FLASH radiobiology, such as the radiolytic oxygen depletion (ROD) model. The general framework of SDDRO-DMF will be developed, with specific DMF models using FEM and ROD, as a demonstration of general applicability of SDDRO-DMF for proton FLASH via transmission beams (TB) or Bragg peaks (BP) with single-field or multi-field irradiation. The FLASH dose rate is modeled as pencil beam scanning dose rate. The solution algorithm for solving the inverse optimization problem of SDDRO-DMF is based on iterative convex relaxation method.

RESULTS

SDDRO-DMF is validated in comparison with IMPT and a state-of-the-art method called SDDRO, with demonstrated efficacy and improvement for reducing the high dose and the high-dose volume for OAR in terms of FED. For example, in a SBRT lung case of the dose-limiting factor that the max dose of brachial plexus should be no more than 26 Gy, only SDDRO-DMF met this max dose constraint; moreover, SDDRO-DMF completely eliminated the high-dose (V70%) volume to zero for CTV10mm (a high-dose region as a 10 mm ring expansion of CTV).

CONCLUSION

We have proposed a new proton FLASH optimization method called SDDRO-DMF that directly optimizes FED using phenomenological or mechanistic models of DMF, and have demonstrated the efficacy of SDDO-DMF in reducing the high-dose volume or/and the high-dose value for OAR, compared to IMPT and a state-of-the-art method SDDRO.

Cardinality-constrained plan-quality and delivery-time optimization method for proton therapy

BACKGROUND

While minimizing plan delivery time is beneficial for proton therapy in terms of motion management, patient comfort, and treatment throughput, it often poses a tradeoff with optimizing plan quality. A key component of plan delivery time is the energy switching time, which is approximately proportional to the number of energy layers, that is, the cardinality.

PURPOSE

This work aims to develop a novel optimization method that can efficiently compute the pareto surface between plan quality and energy layer cardinality, for the planner to navigate through this quality-and-efficiency tradeoff and select the appropriate plan of a balanced tradeoff.

METHODS

A new IMPT method CARD is proposed that (1) explicitly incorporates the minimization of energy layer cardinality as an optimization objective, and (2) automatically generates a set of plans sequentially with a descending order in number of energy layers. The energy layer cardinality is penalized through the l1,0-norm regularization with an upper bound, and the upper bound is monotonically decreased to compute a series of treatment plans with gradually decreased energy layer cardinality on the quality-and-efficiency pareto surface. For any given treatment plan, the plan optimality is enforced using dose-volume planning objectives and the plan deliverability is imposed through minimum-monitor-unit (MMU) constraints, with optimization solution algorithm based on iterative convex relaxation.

RESULTS

The new method CARD was validated in comparison with the benchmark plan of all energy layers (P0), and a state-of-the-art method called MMSEL, using prostate, head-and-neck (HN), lung, pancreas, liver and brain cases. While labor-intensive and time-consuming manual parameter tuning was needed for MMSEL to generate plans of predefined energy layer cardinality, CARD automatically and efficiently computed all plans with sequentially decreasing predefined energy layer cardinality all at once. With the acceptable plan quality (i.e., no more than 110% of total optimization objective value from P0), CARD achieved the reduction of number of energy layers to 52% (from 77 to 40), 48% (from 135 to 65), 59% (from 85 to 50), 67% (from 52 to 35), 80% (from 50 to 40), and 30% (from 66 to 20), for prostate, HN, lung, pancreas, liver, and brain cases, respectively, compared to P0, with overall better plan quality than MMSEL. Moreover, due to the nonconvexity of the MMU constraint, CARD provided the similar or even smaller optimization objective than P0, at the same time with fewer number of energy layers, that is, 55 versus 77, 85 versus 135, 45 versus 52, and 25 versus 66 for prostate, HN, pancreas, and brain cases, respectively.

CONCLUSIONS

We have developed a novel optimization algorithm CARD that can efficiently and automatically compute a series of treatment plans of any given energy layer sequentially, which allows the planner to navigate through the plan-quality and energy-layer-cardinality tradeoff and select the appropriate plan of a balanced tradeoff.

Biological optimization for hybrid proton-photon radiotherapy

Objective.Hybrid proton-photon radiotherapy (RT) is a cancer treatment option to broaden access to proton RT. Additionally, with a refined treatment planning method, hybrid RT has the potential to offer superior plan quality compared to proton-only or photon-only RT, particularly in terms of target coverage and sparing organs-at-risk (OARs), when considering robustness to setup and range uncertainties. However, there is a concern regarding the underestimation of the biological effect of protons on OARs, especially those in close proximity to targets. This study seeks to develop a hybrid treatment planning method with biological dose optimization, suitable for clinical implementation on existing proton and photon machines, with each photon or proton treatment fraction delivering a uniform target dose.Approach.The proposed hybrid biological dose optimization method optimized proton and photon plan variables, along with the number of fractions for each modality, minimizing biological dose to the OARs and surrounding normal tissues. To mitigate underestimation of hot biological dose spots, proton biological dose was minimized within a ring structure surrounding the target. Hybrid plans were designed to be deliverable separately and robustly on existing proton and photon machines, with enforced uniform target dose constraints for the proton and photon fraction doses. A probabilistic formulation was utilized for robust optimization of setup and range uncertainties for protons and photons. The nonconvex optimization problem, arising from minimum monitor unit constraint and dose-volume histogram constraints, was solved using an iterative convex relaxation method.Main results.Hybrid planning with biological dose optimization effectively eliminated hot spots of biological dose, particularly in normal tissues surrounding the target, outperforming proton-only planning. It also provided superior overall plan quality and OAR sparing compared to proton-only or photon-only planning strategies.Significance.This study presents a novel hybrid biological treatment planning method capable of generating plans with reduced biological hot spots, superior plan quality to proton-only or photon-only plans, and clinical deliverability on existing proton and photon machines, separately and robustly.

Dosimetric impact of VMAT delivery angles for early glottic cancer treatment

This study investigates the dosimetric effects of different gantry rotation angles used in volumetric modulated arc therapy (VMAT) for early glottic carcinoma. VMAT treatment plans using full-arc, half-arc, and partial-arc gantry rotation angles were generated from 22 computed tomography datasets of early-stage (T1-2N0) glottic laryngeal cancer. Dosimetric parameters associated with the planning target volume (PTV) and organs at risk (OARs), specifically the carotid arteries and thyroid, were compared. To assess the robustness of the VMAT plans, dose variations were analyzed by introducing positional shifts of 1, 3, and 5 mm from the isocenter of each plan along the superior-inferior, left-right, and anterior-posterior axes. Furthermore, we examined the size of the PTV, the air cavity volume within the PTV, and the variability of the beam path length through the gantry angles to investigate their correlations with PTV dose variations in the presence of positioning errors. Compared to full-arc and half-arc plans, the dosimetric parameters of partial-arc plans were found to be higher in PTV (D2%, D5%, D50%, and Dmean) and lower in OARs, while their dose variations of OAR parameters were greater for positioning errors. In addition, a correlation was observed between PTV size and PTV dose variations. Air cavity volume and depth variability were also correlated with some PTV parameters, depending on the arc plan. The results presented in this study suggest that the partial-arc gantry angles can allow higher PTV doses while minimizing OAR doses in VMAT treatment planning for early glottic cancer. However, the small delivery angles may lead to greater dose variations in the OARs when positioning errors occur.

Robust Optimization for Prostate Radiation Therapy: Assessment of Delivered Dose by Incorporating Intrafraction Prostate Position Deviations

Purpose

To assess the robustness of the dose delivered to the clinical target volume (CTV) between planning target volume (PTV)-based and robust optimization planning approaches in localized prostate cancer radiation therapy.

Methods and Materials

Retrospective data of 20 patients with prostate cancer, including radiation therapy and real-time prostate position, were analyzed. Two sets of volumetric modulated arc therapy plans were generated per patient: PTV-based and robust optimization. PTV-based planning used a 7-mm CTV-PTV margin, whereas robust planning considered same-magnitude position deviations. Differences in CTV dose delivered to 99% volume (D99), PTV dose delivered to 95% volume (D95), and bladder and rectum V40 (volume receiving 40 Gy) and V60 (volume receiving 60 Gy) values were evaluated. The target position, determined by in-house position monitoring system, was incorporated for dose assessment with and without position deviation correction.

Results

In the robust optimization approach, compared with PTV-based planning, the mean (standard deviation) V40 and V60 values of the bladder were reduced by 5.2% (4.1%) and 5.1% (1.9%), respectively. Similarly, for the rectum, the reductions were 0.8% (0.5%) and 0.6% (0.6%). In corrected treatment scenarios, both planning approaches resulted in a mean (standard deviation) CTV D99 difference of 0.1 Gy (0.1 Gy). In the not corrected scenario, PTV-based planning reduced CTV D99 by 0.1 Gy (0.5 Gy), whereas robust planning reduced it by 0.2 Gy (0.6 Gy). There was no statistically significant difference observed in the planned and delivered rectum and bladder dose for both corrected and not corrected scenarios.

Conclusions

Robust optimization resulted in lower V40 and V60 values for the bladder compared with PTV-based planning. However, no difference in CTV dose accuracy was found between the 2 approaches.

Robustness analysis of dynamic trajectory radiotherapy and volumetric modulated arc therapy plans for head and neck cancer

Background and purpose

Dynamic trajectory radiotherapy (DTRT) has been shown to improve healthy tissue sparing compared to volumetric arc therapy (VMAT). This study aimed to assess and compare the robustness of DTRT and VMAT treatment-plans for head and neck (H&N) cancer to patient-setup (PS) and machine-positioning uncertainties.

Materials and methods

The robustness of DTRT and VMAT plans previously created for 46 H&N cases, prescribed 50-70 Gy to 95 % of the planning-target-volume, was assessed. For this purpose, dose distributions were recalculated using Monte Carlo, including uncertainties in PS (translation and rotation) and machine-positioning (gantry-, table-, collimator-rotation and multi-leaf collimator (MLC)). Plan robustness was evaluated by the uncertainties' impact on normal tissue complication probabilities (NTCP) for xerostomia and dysphagia and on dose-volume endpoints. Differences between DTRT and VMAT plan robustness were compared using Wilcoxon matched-pair signed-rank test (α  = 5 %).

Results

Average NTCP for moderate-to-severe xerostomia and grade ≥ II dysphagia was lower for DTRT than VMAT in the nominal scenario (0.5 %, p = 0.01; 2.1 %, p < 0.01) and for all investigated uncertainties, except MLC positioning, where the difference was not significant. Average differences compared to the nominal scenario were ≤ 3.5 Gy for rotational PS (≤ 3°) and machine-positioning (≤ 2°) uncertainties, <7 Gy for translational PS uncertainties (≤ 5 mm) and < 20 Gy for MLC-positioning uncertainties (≤ 5 mm).

Conclusions

DTRT and VMAT plan robustness to the investigated uncertainties depended on uncertainty direction and location of the structure-of-interest to the target. NTCP remained on average lower for DTRT than VMAT even when considering uncertainties.

Pencil beam scanning proton therapy for mediastinal lymphomas in deep inspiration breath-hold: a retrospective assessment of plan robustness

PURPOSE/BACKGROUND

The aim of this study was to evaluate pencil beam scanning (PBS) proton therapy (PT) in deep inspiration breath-hold (DIBH) for mediastinal lymphoma patients, by retrospectively evaluating plan robustness to the clinical target volume (CTV) and organs at risk (OARs) on repeated CT images acquired throughout treatment.  Methods: Sixteen mediastinal lymphoma patients treated with PBS-PT in DIBH were included. Treatment plans (TPs) were robustly optimized on the CTV (7 mm/4.5%). Repeated verification CTs (vCT) were acquired during the treatment course, resulting in 52 images for the entire patient cohort. The CTV and OARs were transferred from the planning CT to the vCTs with deformable image registration and the TPs were recalculated on the vCTs. Target coverage and OAR doses at the vCTs were compared to the nominal plan. Deviation in lung volume was also calculated.

RESULTS

The TPs demonstrated high robust target coverage throughout treatment with D98%,CTV deviations within 2% for 14 patients and above the desired requirement of 95% for 49/52 vCTs. However, two patients did not achieve a robust dose to CTV due to poor DIBH reproducibility, with D98%,CTV at 78 and 93% respectively, and replanning was performed for one patient. Adequate OAR sparing was achieved for all patients. Total lung volume variation was below 10% for 39/52 vCTs.

CONCLUSION

PBS PT in DIBH is generally a robust technique for treatment of mediastinal lymphomas. However, closely monitoring the DIBH-reproducibility during treatment is important to avoid underdosing CTV and achieve sufficient dose-sparing of the OARs.

Robust and optimal dose distribution for brain metastases with robotic radiosurgery system: recipe for an inflection point

Purpose.This study aims to establish a robust dose prescription methodology in stereotactic radiosurgery (SRS) and stereotactic radiotherapy (SRT) for brain metastases, considering geometrical uncertainty and minimising dose exposure to the surrounding normal brain tissue.Methods and Materials.Treatment plans employing 40%-90% isodose lines (IDL) at 10% IDL intervals were created for variously sized brain metastases. The plans were constructed to deliver 21 Gy in SRS. Robustness of each plan was analysed using parameters such as the near minimum dose to the tumour, the near maximum dose to the normal brain, and the volume of normal brain irradiated above 14 Gy.Results.Plans prescribed at 60% IDL demonstrated the least variation in the near minimum dose to the tumour and the near maximum dose to the normal brain under conditions of minimal geometrical uncertainty relative to tumour radius. When the IDL-percentage prescription was below 60%, geometrical uncertainties led to increases in these doses. Conversely, they decreased with IDL-percentage prescriptions above 60%. The volume of normal brain irradiated above 14 Gy was lowest at 60% IDL, regardless of geometrical uncertainty.Conclusions.To enhance robustness against geometrical uncertainty and to better spare healthy brain tissue, a 60% IDL prescription is recommended in SRS and SRT for brain metastases using a robotic radiosurgery system.

NRG Oncology and PTCOG Patterns of Practice Survey and Consensus Recommendations on Pencil-Beam Scanning Proton Stereotactic Body Radiation Therapy and Hypofractionated Radiation Therapy for Thoracic Malignancies

Stereotactic body radiation therapy (SBRT) and hypofractionation using pencil-beam scanning (PBS) proton therapy (PBSPT) is an attractive option for thoracic malignancies. Combining the advantages of target coverage conformity and critical organ sparing from both PBSPT and SBRT, this new delivery technique has great potential to improve the therapeutic ratio, particularly for tumors near critical organs. Safe and effective implementation of PBSPT SBRT/hypofractionation to treat thoracic malignancies is more challenging than the conventionally fractionated PBSPT because of concerns of amplified uncertainties at the larger dose per fraction. The NRG Oncology and Particle Therapy Cooperative Group Thoracic Subcommittee surveyed proton centers in the United States to identify practice patterns of thoracic PBSPT SBRT/hypofractionation. From these patterns, we present recommendations for future technical development of proton SBRT/hypofractionation for thoracic treatment. Among other points, the recommendations highlight the need for volumetric image guidance and multiple computed tomography-based robust optimization and robustness tools to minimize further the effect of uncertainties associated with respiratory motion. Advances in direct motion analysis techniques are urgently needed to supplement current motion management techniques.

Proton PBS Planning Techniques, Robustness Evaluation, and OAR Sparing for the Whole-Brain Part of Craniospinal Axis Irradiation

Proton therapy is a promising modality for craniospinal irradiation (CSI), offering dosimetric advantages over conventional treatments. While significant attention has been paid to spine fields, for the brain fields, only dose reduction to the lens of the eye has been reported. Hence, the objective of this study is to assess the potential gains and feasibility of adopting different treatment planning techniques for the entire brain within the CSI target. To this end, eight previously treated CSI patients underwent retrospective replanning using various techniques: (1) intensity modulated proton therapy (IMPT) optimization, (2) the modification/addition of field directions, and (3) the pre-optimization removal of superficially placed spots. The target coverage robustness was evaluated and dose comparisons for lenses, cochleae, and scalp were conducted, considering potential biological dose increases. The target coverage robustness was maintained across all plans, with minor reductions when superficial spot removal was utilized. Single- and multifield optimization showed comparable target coverage robustness and organ-at-risk sparing. A significant scalp sparing was achieved in adults but only limited in pediatric cases. Superficial spot removal contributed to scalp V30 Gy reduction at the expense of lower coverage robustness in specific cases. Lens sparing benefits from multiple field directions, while cochlear sparing remains impractical. Based on the results, all investigated plan types are deemed clinically adoptable.

Inverse planning of lung radiotherapy with photon and proton beams using a discrete ordinates Boltzmann solver

Objective. A discrete ordinates Boltzmann solver has recently been developed for use as a fast and accurate dose engine for calculation of photon and proton beams. The purpose of this study is to apply the algorithm to the inverse planning process for photons and protons and to evaluate the impact that this has on the quality of the final solution.Approach.The method was implemented into an iterative least-squares inverse planning optimiser, with the Boltzmann solver used every 20 iterations over the total of 100 iterations. Elemental dose distributions for the intensity modulation and the dose changes at the intermediate iterations were calculated by a convolution algorithm for photons and a simple analytical model for protons. The method was evaluated for 12 patients in the heterogeneous tissue environment encountered in radiotherapy of lung tumours. Photon arc and proton arc treatments were considered in this study. The results were compared with those for use of the Boltzmann solver solely at the end of inverse planning or not at all.Main results.Application of the Boltzmann solver at the end of inverse planning shows the dose heterogeneity in the planning target volume to be greater than calculated by convolution and empirical methods, with the median root-mean-square dose deviation increasing from 3.7 to 5.3 for photons and from 1.9 to 3.4 for proton arcs. Use of discrete ordinates throughout inverse planning enables homogeneity of target coverage to be maintained throughout, the median root-mean-square dose deviation being 3.6 for photons and 2.3 for protons. Dose to critical structures is similar with discrete ordinates and conventional methods. Time for inverse planning with discrete ordinates takes around 1-2 h using a contemporary computing environment.Significance.By incorporating the Boltzmann solver into an iterative least squares inverse planning optimiser, accurate dose calculation in a heterogeneous medium is obtained throughout inverse planning, with the result that the final dose distribution is of the highest quality.

Ultra-hypofractionated prostate cancer radiotherapy: Dosimetric impact of real-time intrafraction prostate motion and daily anatomical changes

PURPOSE

To retrospectively assess the differences between planned and delivered dose during ultra-hypofractionated (UHF) prostate cancer treatments, by evaluating the dosimetric impact of daily anatomical variations alone, and in combination with prostate intrafraction motion.

METHODS

Prostate intrafraction motion was recorded with a transperineal ultrasound probe in 15 patients treated by UHF radiotherapy (36.25 Gy/5 fractions). The dosimetric objective was to cover 99 % of the clinical target volume with the 100 % prescription isodose line. After treatment, planning CT (pCT) images were deformably registered onto daily Cone Beam CT to generate pseudo-CT for dose accumulation (accumulated CT, aCT). The interplay effect was accounted by synchronizing prostatic shifts and beam geometry. Finally, the shifted dose maps were accumulated (moved-accumulated CT, maCT).

RESULTS

No significant change in daily CTV volumes was observed. Conversely, CTV V100% was 98.2 ± 0.8 % and 94.7 ± 2.6 % on aCT and maCT, respectively, compared with 99.5 ± 0.2 % on pCT (p < 0.0001). Bladder volume was smaller than planned in 76 % of fractions and D5cc was 33.8 ± 3.2 Gy and 34.4 ± 3.4 Gy on aCT (p = 0.02) and maCT (p = 0.01) compared with the pCT (36.0 ± 1.1 Gy). The rectum was smaller than planned in 50.3 % of fractions, but the dosimetric differences were not statistically significant, except for D1cc, found smaller on the maCT (33.2 ± 3.2 Gy, p = 0.02) compared with the pCT (35.3 ± 0.7 Gy).

CONCLUSIONS

Anatomical variations and prostate movements had more important dosimetric impact than anatomical variations alone, although, in some cases, the two phenomena compensated. Therefore, an efficient IGRT protocol is required for treatment implementation to reduce setup errors and control intrafraction motion.

Modelling small block aperture in an in-house developed GPU-accelerated Monte Carlo-based dose engine for pencil beam scanning proton therapy

Purpose. To enhance an in-house graphic-processing-unit accelerated virtual particle (VP)-based Monte Carlo (MC) proton dose engine (VPMC) to model aperture blocks in both dose calculation and optimization for pencil beam scanning proton therapy (PBSPT)-based stereotactic radiosurgery (SRS).Methods and materials. A module to simulate VPs passing through patient-specific aperture blocks was developed and integrated in VPMC based on simulation results of realistic particles (primary protons and their secondaries). To validate the aperture block module, VPMC was first validated by an opensource MC code, MCsquare, in eight water phantom simulations with 3 cm thick brass apertures: four were with aperture openings of 1, 2, 3, and 4 cm without a range shifter, while the other four were with same aperture opening configurations with a range shifter of 45 mm water equivalent thickness. Then, VPMC was benchmarked with MCsquare and RayStation MC for 10 patients with small targets (average volume 8.4 c.c. with range of 0.4-43.3 c.c.). Finally, 3 typical patients were selected for robust optimization with aperture blocks using VPMC.Results. In the water phantoms, 3D gamma passing rate (2%/2 mm/10%) between VPMC and MCsquare was 99.71 ± 0.23%. In the patient geometries, 3D gamma passing rates (3%/2 mm/10%) between VPMC/MCsquare and RayStation MC were 97.79 ± 2.21%/97.78 ± 1.97%, respectively. Meanwhile, the calculation time was drastically decreased from 112.45 ± 114.08 s (MCsquare) to 8.20 ± 6.42 s (VPMC) with the same statistical uncertainties of ~0.5%. The robustly optimized plans met all the dose-volume-constraints (DVCs) for the targets and OARs per our institutional protocols. The mean calculation time for 13 influence matrices in robust optimization by VPMC was 41.6 s and the subsequent on-the-fly 'trial-and-error' optimization procedure took only 71.4 s on average for the selected three patients.Conclusion. VPMC has been successfully enhanced to model aperture blocks in dose calculation and optimization for the PBSPT-based SRS.

Deep learning based uncertainty prediction of deformable image registration for contour propagation and dose accumulation in online adaptive radiotherapy

Objective.Online adaptive radiotherapy aims to fully leverage the advantages of highly conformal therapy by reducing anatomical and set-up uncertainty, thereby alleviating the need for robust treatments. This requires extensive automation, among which is the use of deformable image registration (DIR) for contour propagation and dose accumulation. However, inconsistencies in DIR solutions between different algorithms have caused distrust, hampering its direct clinical use. This work aims to enable the clinical use of DIR by developing deep learning methods to predict DIR uncertainty and propagating it into clinically usable metrics.Approach.Supervised and unsupervised neural networks were trained to predict the Gaussian uncertainty of a given deformable vector field (DVF). Since both methods rely on different assumptions, their predictions differ and were further merged into a combined model. The resulting normally distributed DVFs can be directly sampled to propagate the uncertainty into contour and accumulated dose uncertainty.Main results.The unsupervised and combined models can accurately predict the uncertainty in the manually annotated landmarks on the DIRLAB dataset. Furthermore, for 5 patients with lung cancer, the propagation of the predicted DVF uncertainty into contour uncertainty yielded for both methods anexpected calibration errorof less than 3%. Additionally, theprobabilisticly accumulated dose volume histograms(DVH) encompass well the accumulated proton therapy doses using 5 different DIR algorithms. It was additionally shown that the unsupervised model can be used for different DIR algorithms without the need for retraining.Significance.Our work presents first-of-a-kind deep learning methods to predict the uncertainty of the DIR process. The methods are fast, yield high-quality uncertainty estimates and are useable for different algorithms and applications. This allows clinics to use DIR uncertainty in their workflows without the need to change their DIR implementation.

Treatment planning of scanned proton beams in RayStation

The use of scanned proton beams in external beam radiation therapy has seen a rapid development over the past decade. This technique places new demands on treatment planning, as compared to conventional photon-based radiation therapy. In this article, several proton specific functions as implemented in the treatment planning system RayStation are presented. We will cover algorithms for energy layer and spot selection, basic optimization including the handling of spot weight limits, optimization of the linear energy transfer (LET) distribution, robust optimization including the special case of 4D optimization, proton arc planning, and automatic planning using deep learning. We will further present the Monte Carlo (MC) proton dose engine in RayStation to some detail, from the material interpretation of the CT data, through the beam model parameterization, to the actual MC transport mechanism. Useful tools for plan evaluation, including robustness evaluation, and the versatile scripting interface are also described. The overall aim of the paper is to give an overview of some of the key proton planning functions in RayStation, with example usages, and at the same time provide the details about the underlying algorithms that previously have not been fully publicly available.

Assessment of residual geometrical errors of clinical target volumes and their impact on dose accumulation for head and neck radiotherapy

PURPOSE

To assess the residual geometrical errors (dr) and their impact on the clinical target volumes (CTV) dose coverage for head and neck cancer (HNC) proton therapy patients.

METHODS

We analysed 28 HNC patients treated with 70 Gy (RBE) and 54.25 Gy (RBE) to the therapeutic CTV70 and prophylactic CTV54.25, respectively. Daily cone beam CTs were converted to high quality synthetic CTs (sCTs). The CTVs from the nominal CT were propagated to the corresponding sCTs using a hybrid deformable image registration (propagated CTVs) in RayStation 11B. For 11 patients, all propagated CTVs were reviewed by our HNC radiation oncologist (physician corrected CTVs). The residual geometrical error dr was quantified as a function of the daily CTVs volume overlap with the nominal plan CTV. The errors dr(propagated CTVs) and dr(physician corrected CTVs) and the difference in dice similarity coefficients (ΔDSC) were determined. Using clinical plans, dose coverage and the tumor control probability (TCP) for the nominal, accumulated and voxel-wise minimum scenarios were determined.

RESULTS

The difference in the residual geometrical error dr (propagated CTVs - physician corrected CTVs) and mean DSC (|ΔDSC|mean) were minor: Δdr(CTV70) = 0.16 mm, Δdr(CTV54.25) = 0.26 mm, |ΔDSC|mean < 0.9%. For all 28 patients, dr(CTV70) = 1.91 mm and dr(CTV54.25) = 1.90 mm. However, CTV54.25 above and below the cricoid cartilage differed substantially (1.00 mm c.f. 3.93 mm). The CTV54.25 coverage below the cricoid was then almost always lower, although the TCP of the accumulated dose was higher than the TCP of the voxel-wise minimum dose.

CONCLUSIONS

Setup uncertainty setting of 2 mm is possible. The feasibility of using propagated CTVs for error determination is demonstrated.

Plan robustness evaluation strategies in whole-pelvic proton therapy for high-risk prostate cancer patients within a randomised clinical trial

BACKGROUND

Inter-fractional anatomical changes challenge robust delivery of whole-pelvic proton therapy for high-risk prostate cancer. Pre-treatment robust evaluation (PRE) takes uncertainties in isocenter shifts and distal beam edge in treatment plans into account. Using weekly control computed tomography scans (cCTs), the aim of this study was to evaluate the PRE strategy by comparing to an off-line during-treatment robust evaluation (DRE) while also assessing plan robustness with respect to protocol planning constraints.

MATERIAL AND METHODS

Treatment plans and cCTs from ten patients included in the pilot phase of the PROstate PROTON Trial 1 were analysed. Treatment planning followed protocol guidelines with 78 Gy to the primary clinical target volume (CTVp) and 56 Gy to the elective target (CTVe) in 39 fractions. Recalculations of the treatment plans were performed for a total of 64 cCTs and dose/volume measures corresponding to clinical constraints were evaluated for this DRE against the simulated scenario interval from the PRE.

RESULTS

Of the 64 cCTs, 59 showed DRE CTVp measures within the robustness range from the PRE; this was also the case for 39 of the cCTs for the CTVe measures. However, DRE CTVe coverage was still within constraints for 57 of the 64 cCTs. DRE dose/volume measures for CTVp fulfilled target coverage constraints in 59 of 64 cCTs. All DRE measures for the rectum, bladder, and bowel were inside the PRE range in 63, 39, and 31 cCTs, respectively.

CONCLUSION

The PRE strategy predicted the DRE scenarios for CTVp and rectum. CTVe, bladder, and bowel showed more complex anatomical variations than simulated by the PRE isocenter shift. Both original and recalculated nominal treatment plans showed robust treatment delivery in terms of target coverage.

Deep-learning based fast and accurate 3D CT deformable image registration in lung cancer

BACKGROUND

Deformable Image Registration (DIR) is an essential technique required in many applications of radiation oncology. However, conventional DIR approaches typically take several minutes to register one pair of 3D CT images and the resulting deformable vector fields (DVFs) are only specific to the pair of images used, making it less appealing for clinical application.

PURPOSE

A deep-learning-based DIR method using CT images is proposed for lung cancer patients to address the common drawbacks of the conventional DIR approaches and in turn can accelerate the speed of related applications, such as contour propagation, dose deformation, adaptive radiotherapy (ART), etc. METHODS: A deep neural network based on VoxelMorph was developed to generate DVFs using CT images collected from 114 lung cancer patients. Two models were trained with the weighted mean absolute error (wMAE) loss and structural similarity index matrix (SSIM) loss (optional) (i.e., the MAE model and the M+S model). In total, 192 pairs of initial CT (iCT) and verification CT (vCT) were included as a training dataset and the other independent 10 pairs of CTs were included as a testing dataset. The vCTs usually were taken 2 weeks after the iCTs. The synthetic CTs (sCTs) were generated by warping the vCTs according to the DVFs generated by the pre-trained model. The image quality of the synthetic CTs was evaluated by measuring the similarity between the iCTs and the sCTs generated by the proposed methods and the conventional DIR approaches, respectively. Per-voxel absolute CT-number-difference volume histogram (CDVH) and MAE were used as the evaluation metrics. The time to generate the sCTs was also recorded and compared quantitatively. Contours were propagated using the derived DVFs and evaluated with SSIM. Forward dose calculations were done on the sCTs and the corresponding iCTs. Dose volume histograms (DVHs) were generated based on dose distributions on both iCTs and sCTs generated by two models, respectively. The clinically relevant DVH indices were derived for comparison. The resulted dose distributions were also compared using 3D Gamma analysis with thresholds of 3 mm/3%/10% and 2 mm/2%/10%, respectively.

RESULTS

The two models (wMAE and M+S) achieved a speed of 263.7±163 / 265.8±190 ms and a MAE of 13.15±3.8 / 17.52±5.8 HU for the testing dataset, respectively. The average SSIM scores of 0.987±0.006 and 0.988±0.004 were achieved by the two proposed models, respectively. For both models, CDVH of a typical patient showed that less than 5% of the voxels had a per-voxel absolute CT-number-difference larger than 55 HU. The dose distribution calculated based on a typical sCT showed differences of ≤2cGy[RBE] for clinical target volume (CTV) D95 and D5 , within ±0.06% for total lung V5 , ≤1.5cGy[RBE] for heart and esophagus Dmean , and ≤6cGy[RBE] for cord Dmax compared to the dose distribution calculated based on the iCT. The good average 3D Gamma passing rates (> 96% for 3 mm/3%/10% and > 94% for 2 mm/2%/10%, respectively) were also observed.

CONCLUSION

A deep neural network-based DIR approach was proposed and has been shown to be reasonably accurate and efficient to register the initial CTs and verification CTs in lung cancer.

Superiorization of projection algorithms for linearly constrained inverse radiotherapy treatment planning

Objective

We apply the superiorization methodology to the constrained intensity-modulated radiation therapy (IMRT) treatment planning problem. Superiorization combines a feasibility-seeking projection algorithm with objective function reduction: The underlying projection algorithm is perturbed with gradient descent steps to steer the algorithm towards a solution with a lower objective function value compared to one obtained solely through feasibility-seeking.

Approach

Within the open-source inverse planning toolkit matRad, we implement a prototypical algorithmic framework for superiorization using the well-established Agmon, Motzkin, and Schoenberg (AMS) feasibility-seeking projection algorithm and common nonlinear dose optimization objective functions. Based on this prototype, we apply superiorization to intensity-modulated radiation therapy treatment planning and compare it with (i) bare feasibility-seeking (i.e., without any objective function) and (ii) nonlinear constrained optimization using first-order derivatives. For these comparisons, we use the TG119 water phantom, the head-and-neck and the prostate patient of the CORT dataset.

Main results

Bare feasibility-seeking with AMS confirms previous studies, showing it can find solutions that are nearly equivalent to those found by the established piece-wise least-squares optimization approach. The superiorization prototype solved the linearly constrained planning problem with similar dosimetric performance to that of a general-purpose nonlinear constrained optimizer while showing smooth convergence in both constraint proximity and objective function reduction.

Significance

Superiorization is a useful alternative to constrained optimization in radiotherapy inverse treatment planning. Future extensions with other approaches to feasibility-seeking, e.g., with dose-volume constraints and more sophisticated perturbations, may unlock its full potential for high performant inverse treatment planning.

Novel simulation for dosimetry impact of diaphragm respiratory motion in four-dimensional volumetric modulated arc therapy for esophageal cancer

BACKGROUND AND PURPOSE

The diaphragm respiratory motion (RM) could impact the target dose robustness in the lower esophageal cancer (EC). We aimed to develop a framework evaluating the impact of different RM patterns quantitatively in one patient, by creating virtual four-dimensional computed-tomography (v4DCT) images, which could lead to tailored treatment for the breathing pattern. We validated virtual 4D radiotherapy (v4DRT) along with exploring the acceptability of free-breathing volumetric modulated arc therapy (FB-VMAT).

METHODS AND MATERIALS

We assessed 10 patients with superficial EC through their real 4DCT (r4DCT) scans. v4DCT images were derived from the end-inhalation computed tomography (CT) image (reference CT) and the v4DRT dose was accumulated dose over all phases. r4DRT diaphragm shifts were applied with magnitudes derived from r4DCT scans; clinical target volume (CTV) dose of v4DRT was compared with that of r4DRT to validate v4DRT. CTV dosage modifications and planning organ at risk volume (PRV) margins of the spinal cord were examined with the diaphragm movement. The percentage dose differences (ΔDx) were determined between the v4DRT and the dose calculated on the reference CT image.

RESULTS

The CTV ΔDx between the r4DRT and v4DRT were within 1% in cases with RM ≦ 15 mm. The average ΔD100% and ΔDmean of the CTV ranging from 5 to 15 mm of diaphragm motion was 0.3% to 1.7% and 0.1% to 0.4%, respectively. All CTV index changes were within 3% and ΔD1cc and ΔD2cc of Cord PRV were within 1%.

CONCLUSION

We postulate a novel method for evaluating the CTV robustness, comparable to the conventional r4DCT method under the diaphragm RM ≦ 15 mm permitting an impact of within 3% in FB-VMAT for EC on the CTV dose distribution.

Selection of breathing phase number in 4D scanned proton treatment planning optimization for lung tumors

The standard four-dimensional (4D) treatment planning includes all breathing states in the optimization process, which is time-consuming. This work was aimed to optimize the number of intermediate phases needed for 4D proton treatment planning optimization to reduce the computational cost. Five 4D optimization strategies adopting different numbers of intermediate states and one three-dimensional (3D) optimization plan were studied for fifteen lung cancer patients treated with scanned protons, optimizing on all ten phases (4D_10), two extreme phases (4D_2), six phases during the exhalation stage (4D_6EX), six phases during the inhalation stage (4D_6IN), two extreme phases plus an intermediate state (4D_3) and average computed tomography image (3D), respectively. The 4D dose evaluation was conducted on all the ten phases, considering the interplay effect. The resulting doses accumulated on the reference phase were computed and compared. Compared to the 4D optimization plans, the 3D optimization plan performed inferiorly in target coverage, but superiorly in organ at risks (OARs) sparing. For the 4D optimization, all the five 4D plans showed similar performance in OARs protection. However, the 4D_6EX and 4D_6IN strategies out-performed the 4D_2 and 4D_3 plans in dose homogeneity. The computing times of the 4D_2, 4D_3, 4D_6EX and 4D_6IN approaches decreased to 32%, 41%, 66% and 67% of the 4D_10 method, respectively. Thus, our study suggested that the use of all phases during inhalation or exhalation stage might be a feasible approach substituting for the full phase strategy to reduce the calculation load while guaranteeing the plan quality for scanned proton therapy.

Exploring beamline momentum acceptance for tracking respiratory variability in lung cancer proton therapy: a simulation study

Objective. Investigating the aspects of proton beam delivery to track organ motion with pencil beam scanning therapy. Considering current systems as a reference, specify requirements for next-generation units aiming at real-time image-guided treatments.Approach. Proton treatments for six non-small cell lung cancer (NSCLC) patients were simulated using repeated 4DCTs to model respiratory motion variability. Energy corrections required for this treatment site were evaluated for different approaches to tumour tracking, focusing on the potential for energy adjustment within beamline momentum acceptance (dp/p). A respiration-synchronised tracking, taking into account realistic machine delivery limits, was compared to ideal tracking scenarios, in which unconstrained energy corrections are possible. Rescanning and the use of multiple fields to mitigate residual interplay effects and dose degradation have also been investigated.Main results. Energy correction requirements increased with motion amplitudes, for all patients and tracking scenarios. Higher dose degradation was found for larger motion amplitudes, rescanning has beneficial effects and helped to improve dosimetry metrics for the investigated limited dp/pof 1.2% (realistic) and 2.4%. The median differences between ideal and respiratory-synchronised tracking show minimal discrepancies, 1% and 5% respectively for dose coverage (CTV V95) and homogeneity (D5-D95). Multiple-field planning improves D5-D95 up to 50% in the most extreme cases while it does not show a significant effect on V95.Significance. This work shows the potential of implementing tumour tracking in current proton therapy units and outlines design requirements for future developments. Energy regulation within momentum acceptance was investigated to tracking tumour motion with respiratory-synchronisation, achieving results in line with the performance of ideal tracking scenarios. ±5% Δp/p would allow to compensate for all range offsets in our NSCLC patient cohort, including breathing variability. However, the realistic momentum of 1.2% dp/prepresentative of existing medical units limitations, has been shown to preserve plan quality.

A discrete ordinates Boltzmann solver for application to inverse planning of photons and protons

The aim of this work is to develop a discrete ordinates Boltzmann solver that can be used for calculation of absorbed dose from both photons and protons within an inverse planning optimiser, so as to perform accurate dose calculation throughout the whole of the inverse planning process. With photons, five transport sweeps were performed to obtain scattered photon fluence, and unscattered electron fluence was then obtained and used as a fixed source for solution of the electron transport equations. With protons, continuous slowing down was treated as a fixed source, and five transport sweeps were used to calculate scattered fluence. The total electron or proton fluence was multiplied by the stopping power ratio for the transport medium to obtain absorbed dose. The method was evaluated in homogeneous media and in a lung case where the planning target volume was surrounded by low-density lung material. Photon arc, proton passive scattering and proton arc treatments were considered. The results were compared to a clinically validated convolution dose calculation for photons, and with an analytical method for protons. In water-equivalent media, the discrete ordinates method agrees with the alternative algorithms to within 2%. Convergence is found to be sufficiently complete for water-, lung- and bone-equivalent materials after five iterations. The dose calculated by the relatively simple angular quadrature is seen to be very close to that calculated by a more comprehensive quadrature. For inhomogeneous lung plans, the method shows more heterogeneity of dose to the planning target volume than the comparative methods. The discrete ordinates Boltzmann solver provides a general framework for dose calculation with both photons and protons. The method is suitable for incorporation into an inverse planning optimiser, so that accurate dose calculation in a heterogeneous medium can be obtained throughout inverse planning, with the result that the final dose distribution is as predicted by the optimiser.

Clinical benefit of range uncertainty reduction in proton treatment planning based on dual-energy CT for neuro-oncological patients

OBJECTIVE

Several studies have shown that dual-energy CT (DECT) can lead to improved accuracy for proton range estimation. This study investigated the clinical benefit of reduced range uncertainty, enabled by DECT, in robust optimisation for neuro-oncological patients.

METHODS

DECT scans for 27 neuro-oncological patients were included. Commercial software was applied to create stopping-power ratio (SPR) maps based on the DECT scan. Two plans were robustly optimised on the SPR map, keeping the beam and plan settings identical to the clinical plan. One plan was robustly optimised and evaluated with a range uncertainty of 3% (as used clinically; denoted 3%-plan); the second plan applied a range uncertainty of 2% (2%-plan). Both plans were clinical acceptable and optimal. The dose-volume histogram parameters were compared between the two plans. Two experienced neuro-radiation oncologists determined the relevant dose difference for each organ-at-risk (OAR). Moreover, the OAR toxicity levels were assessed.

RESULTS

For 24 patients, a dose reduction >0.5/1 Gy (relevant dose difference depending on the OAR) was seen in one or more OARs for the 2%-plan; e.g. for brainstem D0.03cc in 10 patients, and hippocampus D40% in 6 patients. Furthermore, 12 patients had a reduction in toxicity level for one or two OARs, showing a clear benefit for the patient.

CONCLUSION

Robust optimisation with reduced range uncertainty allows for reduction of OAR toxicity, providing a rationale for clinical implementation. Based on these results, we have clinically introduced DECT-based proton treatment planning for neuro-oncological patients, accompanied with a reduced range uncertainty of 2%.

ADVANCES IN KNOWLEDGE

This study shows the clinical benefit of range uncertainty reduction from 3% to 2% in robustly optimised proton plans. A dose reduction to one or more OARs was seen for 89% of the patients, and 44% of the patients had an expected toxicity level decrease.

Being certain about uncertainties: a robust evaluation method for high-dose-rate prostate brachytherapy treatment plans including the combination of uncertainties

In high-dose-rate (HDR) prostate brachytherapy the combined effect of uncertainties cause a range of possible dose distributions deviating from the nominal plan, and which are not considered during treatment plan evaluation. This could lead to dosimetric misses for critical structures and overdosing of organs at risk. A robust evaluation method to assess the combination of uncertainties during plan evaluation is presented and demonstrated on one HDR prostate ultrasound treatment plan retrospectively. A range of uncertainty scenarios are simulated by changing six parameters in the nominal plan and calculating the corresponding dose distribution. Two methods are employed to change the parameters, a probabilistic approach using random number sampling to evaluate the likelihood of variation in dose distributions, and a combination of the most extreme possible values to access the worst-case dosimetric outcomes. One thousand probabilistic scenarios were run on the single treatment plan with 43.2% of scenarios passing seven of the eight clinical objectives. The prostate D90 had a standard deviation of 4.4%, with the worst case decreasing the dose by up to 27.2%. The urethra D10 was up to 29.3% higher than planned in the worst case. All DVH metrics in the probabilistic scenarios were found to be within acceptable clinical constraints for the plan under statistical tests for significance. The clinical significance of the results from the robust evaluation method presented on any individual treatment plan needs to be compared in the context of a historical data set that contains patient outcomes with robustness analysis data to ascertain a baseline acceptance.

Robustness analysis of CTV and OAR dose in clinical PBS-PT of neuro-oncological tumors: prescription-dose calibration and inter-patient variation with the Dutch proton robustness evaluation protocol

Objective. The Dutch proton robustness evaluation protocol prescribes the dose of the clinical target volume (CTV) to the voxel-wise minimum (VWmin) dose of 28 scenarios. This results in a consistent but conservative near-minimum CTV dose (D98%,CTV). In this study, we analyzed (i) the correlation between VWmin/voxel-wise maximum (VWmax) metrics and actually delivered dose to the CTV and organs at risk (OARs) under the impact of treatment errors, and (ii) the performance of the protocol before and after its calibration with adequate prescription-dose levels.Approach. Twenty-one neuro-oncological patients were included. Polynomial chaos expansion was applied to perform a probabilistic robustness evaluation using 100,000 complete fractionated treatments per patient. Patient-specific scenario distributions of clinically relevant dosimetric parameters for the CTV and OARs were determined and compared to clinical VWmin and VWmax dose metrics for different scenario subsets used in the robustness evaluation protocol.Main results. The inclusion of more geometrical scenarios leads to a significant increase of the conservativism of the protocol in terms of clinical VWmin and VWmax values for the CTV and OARs. The protocol could be calibrated using VWmin dose evaluation levels of 93.0%-92.3%, depending on the scenario subset selected. Despite this calibration of the protocol, robustness recipes for proton therapy showed remaining differences and an increased sensitivity to geometrical random errors compared to photon-based margin recipes.Significance. The Dutch proton robustness evaluation protocol, combined with the photon-based margin recipe, could be calibrated with a VWmin evaluation dose level of 92.5%. However, it shows limitations in predicting robustness in dose, especially for the near-maximum dose metrics to OARs. Consistent robustness recipes could improve proton treatment planning to calibrate residual differences from photon-based assumptions.

Dosimetric comparison of autocontouring techniques for online adaptive proton therapy

Objective.Anatomical and daily set-up uncertainties impede high precision delivery of proton therapy. With online adaptation, the daily plan is reoptimized on an image taken shortly before the treatment, reducing these uncertainties and, hence, allowing a more accurate delivery. This reoptimization requires target and organs-at-risk (OAR) contours on the daily image, which need to be delineated automatically since manual contouring is too slow. Whereas multiple methods for autocontouring exist, none of them are fully accurate, which affects the daily dose. This work aims to quantify the magnitude of this dosimetric effect for four contouring techniques.Approach.Plans reoptimized on automatic contours are compared with plans reoptimized on manual contours. The methods include rigid and deformable registration (DIR), deep-learning based segmentation and patient-specific segmentation.Main results.It was found that independently of the contouring method, the dosimetric influence of usingautomaticOARcontoursis small (<5% prescribed dose in most cases), with DIR yielding the best results. Contrarily, the dosimetric effect of using theautomatic target contourwas larger (>5% prescribed dose in most cases), indicating that manual verification of that contour remains necessary. However, when compared to non-adaptive therapy, the dose differences caused by automatically contouring the target were small and target coverage was improved, especially for DIR.Significance.The results show that manual adjustment of OARs is rarely necessary and that several autocontouring techniques are directly usable. Contrarily, manual adjustment of the target is important. This allows prioritizing tasks during time-critical online adaptive proton therapy and therefore supports its further clinical implementation.

Emerging technologies for cancer therapy using accelerated particles

Cancer therapy with accelerated charged particles is one of the most valuable biomedical applications of nuclear physics. The technology has vastly evolved in the past 50 years, the number of clinical centers is exponentially growing, and recent clinical results support the physics and radiobiology rationale that particles should be less toxic and more effective than conventional X-rays for many cancer patients. Charged particles are also the most mature technology for clinical translation of ultra-high dose rate (FLASH) radiotherapy. However, the fraction of patients treated with accelerated particles is still very small and the therapy is only applied to a few solid cancer indications. The growth of particle therapy strongly depends on technological innovations aiming to make the therapy cheaper, more conformal and faster. The most promising solutions to reach these goals are superconductive magnets to build compact accelerators; gantryless beam delivery; online image-guidance and adaptive therapy with the support of machine learning algorithms; and high-intensity accelerators coupled to online imaging. Large international collaborations are needed to hasten the clinical translation of the research results.

Beam angle optimization for proton therapy via group-sparsity based angle generation method

BACKGROUND

In treatment planning, beam angle optimization (BAO) refers to the selection of a subset with a given number of beam angles from all available angles that provides the best plan quality. BAO is a NP-hard combinatorial problem. Although exhaustive search (ES) can exactly solve BAO by exploring all possible combinations, ES is very time-consuming and practically infeasible.

PURPOSE

To the best of our knowledge, (1) no optimization method has been demonstrated that can provide the exact solution to BAO, and (2) no study has validated an optimization method for solving BAO by benchmarking with the optimal BAO solution (e.g., via ES), both of which will be addressed by this work.

METHODS

This work considers BAO for proton therapy, for example, the selection of 2-4 beam angles for IMPT. The optimal BAO solution is obtained via ES and serves as the ground truth. A new BAO algorithm, namely angle generation (AG) method, is proposed, and demonstrated to provide nearly-exact solutions for BAO in reference to the ES solution. AG iteratively optimizes the angular set via group-sparsity (GS) regularization, until the planning objective does not decrease further.

RESULTS

Since GS alone can also solve BAO, AG was validated and compared with GS for 2-angle brain, 3-angle lung, and 4-angle brain cases, in reference to the optimal BAO solutions obtained by ES: the AG solution had the rank (1/276, 1/2024, 4/10 626), while the GS solution had the rank (42/276, 279/2024, 4328/10 626).

CONCLUSIONS

A new BAO algorithm called AG is proposed and shown to provide substantially improved accuracy for BAO from current methods with nearly-exact solutions to BAO, in reference to the ground truth of optimal BAO solution via ES.

Image guidance and interfractional anatomical variation in paediatric abdominal radiotherapy

OBJECTIVES

To identify variables predicting interfractional anatomical variations measured with cone-beam CT (CBCT) throughout abdominal paediatric radiotherapy, and to assess the potential of surface-guided radiotherapy (SGRT) to monitor these changes.

METHODS

Metrics of variation in gastrointestinal (GI) gas volume and separation of the body contour and abdominal wall were calculated from 21 planning CTs and 77 weekly CBCTs for 21 abdominal neuroblastoma patients (median 4 years, range: 2 - 19 years). Age, sex, feeding tubes, and general anaesthesia (GA) were explored as predictive variables for anatomical variation. Furthermore, GI gas variation was correlated with changes in body and abdominal wall separation, as well as simulated SGRT metrics of translational and rotational corrections between CT/CBCT.

RESULTS

GI gas volumes varied 74 ± 54 ml across all scans, while body and abdominal wall separation varied 2.0 ± 0.7 mm and 4.1 ± 1.5 mm from planning, respectively. Patients < 3.5 years (p = 0.04) and treated under GA (p < 0.01) experienced greater GI gas variation; GA was the strongest predictor in multivariate analysis (p < 0.01). Absence of feeding tubes was linked to greater body contour variation (p = 0.03). GI gas variation correlated with body (R = 0.53) and abdominal wall (R = 0.63) changes. The strongest correlations with SGRT metrics were found for anterior-posterior translation (R = 0.65) and rotation of the left-right axis (R = -0.36).

CONCLUSIONS

Young age, GA, and absence of feeding tubes were linked to stronger interfractional anatomical variation and are likely indicative of patients benefiting from adaptive/robust planning pathways. Our data suggest a role for SGRT to inform the need for CBCT at each treatment fraction in this patient group.

ADVANCES IN KNOWLEDGE

This is the first study to suggest the potential role of SGRT for the management of internal interfractional anatomical variation in paediatric abdominal radiotherapy.

Online adaptive planning methods for intensity-modulated radiotherapy

Online adaptive radiation therapy aims at adapting a patient's treatment plan to their current anatomy to account for inter-fraction variations before daily treatment delivery. As this process needs to be accomplished while the patient is immobilized on the treatment couch, it requires time-efficient adaptive planning methods to generate a quality daily treatment plan rapidly. The conventional planning methods do not meet the time requirement of online adaptive radiation therapy because they often involve excessive human intervention, significantly prolonging the planning phase. This article reviews the planning strategies employed by current commercial online adaptive radiation therapy systems, research on online adaptive planning, and artificial intelligence's potential application to online adaptive planning.

Considerations for radiotherapy planning with MV photons using dose-to-medium

Background and purpose

Radiotherapy planning considerations were developed for the previous calculation algorithms yielding dose to water-in-water (D_w,w). Advanced algorithms improve accuracy, but their dose values in terms of dose to medium-in-medium (D_m,m) depend on the medium considered. This work aimed to show how mimicking D_w,w planning with D_m,m can introduce new issues.

Materials and methods

A head and neck case involving bone and metal heterogeneities outside the CTV was considered. Two different commercial algorithms were used to obtain D_m,m and D_w,w distributions. First, a plan was optimised to irradiate the PTV uniformly and get a homogeneous D_w,w distribution. Second, another plan was optimised to achieve homogeneous D_m,m. Both plans were calculated with D_w,w and D_m,m, and the differences between their dose distributions, clinical impact, and robustness were evaluated.

Results

Uniform irradiation produced D_m,m cold spots in bone (-4%) and implants (-10%). Uniform D_m,m compensated them by increasing fluence but, when recalculated in D_w,w, the fluence compensations produced higher doses that affected homogeneity. Additionally, doses were 1% higher for the target, and + 4% for the mandible, thus increasing toxicity risk. Robustness was impaired when increased fluence regions and heterogeneities mismatched.

Conclusion

Planning with D_m,m as with D_w,w can impact clinical outcome and impair robustness. In optimisation, uniform irradiation instead of homogeneous D_m,m distributions should be pursued when media with different D_m,m responses are involved. However, this requires adapting evaluation criteria or avoiding medium effects. Regardless of the approach, there can be systematic differences in dose prescription and constraints.

Automated Robust Planning for IMPT in Oropharyngeal Cancer Patients Using Machine Learning

PURPOSE

The aim of this study was to evaluate an automated treatment planning method for robustly optimized intensity modulated proton therapy (IMPT) plans for oropharyngeal carcinoma patients and to compare the results with manually optimized robust IMPT plans.

METHODS AND MATERIALS

An atlas regression forest-based machine learning (ML) model for dose prediction was trained on CT scans, contours, and dose distributions of robust IMPT plans of 88 oropharyngeal cancer (OPC) patients. The ML model was combined with a robust voxel and dose volume histogram-based dose mimicking optimization algorithm for 21 perturbed scenarios to generate a machine-deliverable plan from the predicted dose distribution. Machine learning optimization (MLO) configuration was performed using a cross-validation approach with 3 × 8 tuning patients and comprised of adjustments to the mimicking optimization, to generate higher-quality MLO plans. Independent testing of the MLO algorithm was performed with another 25 patients. Plan quality of clinical and MLO plans was evaluated by the clinical target volume (D98% voxel-wise minimum dose >94%), organ at risk (OAR) doses, and the normal tissue complication probability (NTCP) (sum (Σ) of grade-2 and grade-3 dysphagia and xerostomia).

RESULTS

Adequate robust target coverage was achieved in 24/25 clinical plans and in 23/25 MLO plans in the primary clinical target volume (CTV). In the elective CTV, 22/25 clinical plans and 24/25 MLO plans passed the robust target coverage evaluation threshold. The MLO average Σgrade 2 and Σgrade 3 NTCPs were comparable to the clinical plans (Σgrade 2 NTCPs: clinical 47.5% vs MLO 48.4%, Σgrade 3 NTCPs: clinical 11.9% vs MLO 12.3%). Significant increases in OAR average doses in MLO plans were found in the pharynx constrictor muscles (163 cGy, P = .002) and cervical esophagus (265 cGy, P = .002). The MLO plans were created within 45 minutes.

CONCLUSION

This study showed that automated MLO planning can generate robustly optimized MLO plans with quality comparable to clinical plans in OPC patients.

A survey of practice patterns for adaptive particle therapy for interfractional changes

Background and purpose

Anatomical changes may compromise the planned target coverage and organs-at-risk dose in particle therapy. This study reports on the practice patterns for adaptive particle therapy (APT) to evaluate current clinical practice and wishes and barriers to further implementation.

Materials and methods

An institutional questionnaire was distributed to PT centres worldwide (7/2020-6/2021) asking which type of APT was used, details of the workflow, and what the wishes and barriers to implementation were. Seventy centres from 17 countries participated. A three-round Delphi consensus analysis (10/2022) among the authors followed to define recommendations on required actions and future vision.

Results

Out of the 68 clinically operational centres, 84% were users of APT for at least one treatment site with head and neck being most common. APT was mostly performed offline with only two online APT users (plan-library). No centre used online daily re-planning. Daily 3D imaging was used for APT by 19% of users. Sixty-eight percent of users had plans to increase their use or change their technique for APT. The main barrier was "lack of integrated and efficient workflows". Automation and speed, reliable dose deformation for dose accumulation and higher quality of in-room volumetric imaging were identified as the most urgent task for clinical implementation of online daily APT.

Conclusion

Offline APT was implemented by the majority of PT centres. Joint efforts between industry research and clinics are needed to translate innovations into efficient and clinically feasible workflows for broad-scale implementation of online APT.

Built-in wavelet-induced smoothness to reduce plan complexity in intensity modulated radiation therapy (IMRT)

Objective.Reducing plan complexity in intensity modulated radiation therapy (IMRT) to ensure dosimetric accuracy and delivery efficiency of the radiation treatment plans. We propose a novel approach by representing the beamlet intensities using an incomplete wavelet basis that explicitly excludes fluctuating intensity maps from the decision space (explicit hard constraint). This technique provides a built-in wavelet-induced smoothness that improves both dosimetric plan quality and delivery efficiency.Approach.The beamlet intensity maps need to be especially smooth in the leaf travel direction (referred to as theX-direction). We treat the intensity map of each beam as a 2D image and represent it using the wavelets corresponding to low-frequency changes in theX-direction (i.e. approximation and horizontal). The absence of wavelets corresponding to high-frequency changes (i.e. vertical and diagonal) induces built-in smoothness. We still utilize a regularization term in the objective function to promote smoothness in theY-direction (perpendicular to theX-direction) and further possible smoothness in theX-direction. This technique has been tested on three patient cases of different disease sites (paraspinal, lung, prostate) and all final evaluations and comparisons have been performed on an FDA-approved commercial treatment planning system (Varian Eclipse^TM).Main results.Wavelet-induced smoothness reduced monitor units by about 10%, 45%, and 14% for paraspinal, lung, and prostate cases, respectively. It also improved organ at risk sparing, especially on the complex paraspinal case where it resulted in about 7%, 13%, and 14% less mean dose to esophagus, lung, and cord, respectively. Moreover, built-in wavelet-induced smoothness desensitizes the results to changing the weight associated to the regularization term, and thereby mitigates the weight fine-tuning difficulty.Significance.Fluence smoothness is often achieved by smoothing the beamlet intensity maps using a proper regularization term in the objective function aiming at disincentivizing fluctuation in the beamlet intensities (implicit soft constraint). This work reports a novel application of wavelets in imposing an explicit smoothness hard constraint in the search space using an incomplete wavelet basis. This idea has been successfully applied to exclude complex and clinically irrelevant radiation plans from the search space. The code and pertained models along with a sample dataset are released on our LowDimRT GitHub (https://github.com/PortPy-Project/LowDimRT).

TVL1-IMPT: Optimization of Peak-to-Valley Dose Ratio Via Joint Total-Variation and L1 Dose Regularization for Spatially Fractionated Pencil-Beam-Scanning Proton Therapy

PURPOSE

Proton minibeam radiation therapy (pMBRT) is a novel proton modality of spatially fractionated RT. pMBRT can reduce the radiation damage to normal tissues via biological dose sparing of high peak-to-valley dose ratio (PVDR). This work will develop a new spatially fractionated IMPT treatment planning method for pMBRT that jointly optimizes the plan quality and maximizes the PVDR.

METHODS

The new optimization method simultaneously maximizes the normal-tissue PVDR and optimizes the dose distribution at tumor targets and organs at risk. The PVDR maximization is through the joint total variation (TV) and L1 regularization with respect to the normal-tissue dose. That is, the beam-eye view projects dose slices of several depths for each beam angle; the TV of dose is maximized, corresponding to the PVDR maximization; and the L1 of dose is minimized, corresponding to the minimization of the organs-at-risk dose and maximization of survival fraction (SF).

RESULTS

The new IMPT method with TV and L1 regularization was validated in comparison with the conventional IMPT method for pMBRT in several clinical cases. The results show that TVL1 provided larger PVDR and SF than the conventional IMPT method for biological sparing of normal tissues, with preserved plan quality in terms of physical dose distribution.

CONCLUSIONS

A new spatially fractionated IMPT treatment planning method was developed for pMBRT that can optimize and improve normal-tissue PVDR and SF by incorporating TV and L1 dose regularization with properly chosen regularization parameters into IMPT.

An organ deformation model using Bayesian inference to combine population and patient-specific data

Objective.Organ deformation models have the potential to improve delivery and reduce toxicity of radiotherapy, but existing data-driven motion models are based on either patient-specific or population data. We propose to combine population and patient-specific data using a Bayesian framework. Our goal is to accurately predict individual motion patterns while using fewer scans than previous models.Approach.We have derived and evaluated two Bayesian deformation models. The models were applied retrospectively to the rectal wall from a cohort of prostate cancer patients. These patients had repeat CT scans evenly acquired throughout radiotherapy. Each model was used to create coverage probability matrices (CPMs). The spatial correlations between these estimated CPMs and the ground truth, derived from independent scans of the same patient, were calculated.Main results.Spatial correlation with ground truth were significantly higher for the Bayesian deformation models than both patient-specific and population-derived models with 1, 2 or 3 patient-specific scans as input. Statistical motion simulations indicate that this result will also hold for more than 3 scans.Significance.The improvement over previous models means that fewer scans per patient are needed to achieve accurate deformation predictions. The models have applications in robust radiotherapy planning and evaluation, among others.

Dosimetric impact of adaptive proton therapy in head and neck cancer - A review

Background

Intensity Modulated Proton Therapy (IMPT) in head and neck cancer (HNC) is susceptible to anatomical changes and patient set-up inaccuracies during the radiotherapy course, which can cause discrepancies between planned and delivered dose. The discrepancies can be counteracted by adaptive replanning strategies. This article reviews the observed dosimetric impact of adaptive proton therapy (APT) and the timing to perform a plan adaptation in IMPT in HNC.

Methods

A literature search of articles published in PubMed/MEDLINE, EMBASE and Web of Science from January 2010 to March 2022 was performed. Among a total of 59 records assessed for possible eligibility, ten articles were included in this review.

Results

Included studies reported on target coverage deterioration in IMPT plans during the RT course, which was recovered with the application of an APT approach. All APT plans showed an average improved target coverage for the high- and low-dose targets as compared to the accumulated dose on the planned plans. Dose improvements up to 2.5 Gy (3.5 %) and up to 4.0 Gy (7.1 %) in the D98 of the high- and low dose targets were observed with APT. Doses to the organs at risk (OARs) remained equal or decreased slightly after APT was applied. In the included studies, APT was largely performed once, which resulted in the largest target coverage improvement, but eventual additional APT improved the target coverage further. There is no data showing what is the most appropriate timing for APT.

Conclusion

APT during IMPT for HNC patients improves target coverage. The largest improvement in target coverage was found with a single adaptive intervention, and an eventual second or more frequent APT application improved the target coverage further. Doses to the OARs remained equal or decreased slightly after applying APT. The most optimal timing for APT is yet to be determined.

Fraction optimization for hybrid proton-photon treatment planning

BACKGROUND

Hybrid proton-photon radiotherapy (RT) can provide better plan quality than proton or photon only RT, in terms of robustness of target coverage and sparing of organs-at-risk (OAR).

PURPOSE

This work develops a hybrid treatment planning method that can optimize the number of proton and photon fractions as well as proton and photon plan variables, so that the hybrid plans can be clinically delivered day-to-day using either proton or photon machine.

METHODS

In the new hybrid treatment planning method, the total dose distribution (sum of proton dose and photon dose) is optimized for robust target coverage and optimal OAR sparing, by jointly optimizing proton spots and photon fluences, while the target dose uniformity is also enforced individually on both proton dose and photon dose, so that the hybrid plans can be separately and robustly delivered on proton or photon machine. To ensure the target dose uniformity for proton and photon plans, the number of proton and photon fractions is explicitly modeled and optimized, so that the target dose for proton and photon dose components is constrained to be a constant fraction of the total prescription dose while the plan quality based on total dose is optimized. The feasibility of hybrid planning using the proposed method is validated with representative clinical cases including abdomen, lung, head-and-neck (HN), and brain.

RESULTS

For all cases, hybrid plans provided better overall plan quality and OAR sparing than proton-only or photon-only plans, better target dose uniformity and robustness than proton-only plans, quantified by treatment planning objectives and dosimetric parameters. Moreover, for HN and brain cases, hybrid plans also had better target coverage than photon-only plans.

CONCLUSIONS

We have developed a new hybrid treatment planning method that optimizes number of proton and photon fractions as well as proton spots and photon fluences, for generating hybrid plans that can be separately and robustly delivered on proton or photon machines. Preliminary results have demonstrated that hybrid plans generated by the new method have better plan quality than proton-only or photon-only plans.

Efficiency enhancements of a Monte Carlo beamlet based treatment planning process: implementation and parameter study

Objective.The computational effort to perform beamlet calculation, plan optimization and final dose calculation of a treatment planning process (TPP) generating intensity modulated treatment plans is enormous, especially if Monte Carlo (MC) simulations are used for dose calculation. The goal of this work is to improve the computational efficiency of a fully MC based TPP for static and dynamic photon, electron and mixed photon-electron treatment techniques by implementing multiple methods and studying the influence of their parameters.Approach.A framework is implemented calculating MC beamlets efficiently in parallel on each available CPU core. The user can specify the desired statistical uncertainty of the beamlets, a fractional sparse dose threshold to save beamlets in a sparse format and minimal distances to the PTV surface from which 2 × 2 × 2 = 8 (medium) or even 4 × 4 × 4 = 64 (large) voxels are merged. The compromise between final plan quality and computational efficiency of beamlet calculation and optimization is studied for several parameter values to find a reasonable trade-off. For this purpose, four clinical and one academic case are considered with different treatment techniques.Main results.Setting the statistical uncertainty to 5% (photon beamlets) and 15% (electron beamlets), the fractional sparse dose threshold relative to the maximal beamlet dose to 0.1% and minimal distances for medium and large voxels to the PTV to 1 cm and 2 cm, respectively, does not lead to substantial degradation in final plan quality compared to using 2.5% (photon beamlets) and 5% (electron beamlets) statistical uncertainty and no sparse format nor voxel merging. Only OAR sparing is slightly degraded. Furthermore, computation times are reduced by about 58% (photon beamlets), 88% (electron beamlets) and 96% (optimization).Significance.Several methods are implemented improving computational efficiency of beamlet calculation and plan optimization of a fully MC based TPP without substantial degradation in final plan quality.

RMSim: controlled respiratory motion simulation on static patient scans

Objective.This work aims to generate realistic anatomical deformations from static patient scans. Specifically, we present a method to generate these deformations/augmentations via deep learning driven respiratory motion simulation that provides the ground truth for validating deformable image registration (DIR) algorithms and driving more accurate deep learning based DIR.Approach.We present a novel 3D Seq2Seq deep learning respiratory motion simulator (RMSim) that learns from 4D-CT images and predicts future breathing phases given a static CT image. The predicted respiratory patterns, represented by time-varying displacement vector fields (DVFs) at different breathing phases, are modulated through auxiliary inputs of 1D breathing traces so that a larger amplitude in the trace results in more significant predicted deformation. Stacked 3D-ConvLSTMs are used to capture the spatial-temporal respiration patterns. Training loss includes a smoothness loss in the DVF and mean-squared error between the predicted and ground truth phase images. A spatial transformer deforms the static CT with the predicted DVF to generate the predicted phase image. 10-phase 4D-CTs of 140 internal patients were used to train and test RMSim. The trained RMSim was then used to augment a public DIR challenge dataset for training VoxelMorph to show the effectiveness of RMSim-generated deformation augmentation.Main results.We validated our RMSim output with both private and public benchmark datasets (healthy and cancer patients). The structure similarity index measure (SSIM) for predicted breathing phases and ground truth 4D CT images was 0.92 ± 0.04, demonstrating RMSim's potential to generate realistic respiratory motion. Moreover, the landmark registration error in a public DIR dataset was improved from 8.12 ± 5.78 mm to 6.58mm ± 6.38 mm using RMSim-augmented training data.Significance.The proposed approach can be used for validating DIR algorithms as well as for patient-specific augmentations to improve deep learning DIR algorithms. The code, pretrained models, and augmented DIR validation datasets will be released athttps://github.com/nadeemlab/SeqX2Y.

Dosimetric impact of a robust optimization approach to mitigate effects from rotational uncertainty in prostate intensity-modulated brachytherapy

BACKGROUND

Intensity-modulated brachytherapy (IMBT) is an emerging technology for cancer treatment, in which radiation sources are shielded to shape the dose distribution. The rotatable shields provide an additional degree of freedom, but also introduce an additional, directional, type of uncertainty, compared to conventional high-dose-rate brachytherapy (HDR BT).

PURPOSE

We propose and evaluate a robust optimization approach to mitigate the effects of rotational uncertainty in the shields with respect to planning criteria.

METHODS

A previously suggested prototype for platinum-shielded prostate ¹⁶⁹ Yb-based dynamic IMBT is considered. We study a retrospective patient data set (anatomical contours and catheter placement) from two clinics, consisting of six patients that had previously undergone conventional ¹⁹² Ir HDR BT treatment. The Monte Carlo-based treatment planning software RapidBrachyMCTPS is used for dose calculations. In our computational experiments, we investigate systematic rotational shield errors of ±10° and ±20°, and the same systematic error is applied to all dwell positions in each scenario. This gives us three scenarios, one nominal and two with errors. The robust optimization approach finds a compromise between the average and worst-case scenario outcomes.

RESULTS

We compare dose plans obtained from standard models and their robust counterparts. With dwell times obtained from a linear penalty model (LPM), for 10° errors, the dose to urethra ( D 0.1 c c $D_{0.1cc}$ ) and rectum ( D 0.1 c c $D_{0.1cc}$ and D 1 c c $D_{1cc}$ ) increase with up to 5% and 7%, respectively, in the worst-case scenario, while with the robust counterpart, the corresponding increases were 3% and 3%. For all patients and all evaluated criteria, the worst-case scenario outcome with the robust approach had lower deviation compared to the standard model, without compromising target coverage. We also evaluated shield errors up to 20° and while the deviations increased to a large extent with the standard models, the robust models were capable of handling even such large errors.

CONCLUSIONS

We conclude that robust optimization can be used to mitigate the effects from rotational uncertainty and to ensure the treatment plan quality of IMBT.

Distributed and scalable optimization for robust proton treatment planning

BACKGROUND

The importance of robust proton treatment planning to mitigate the impact of uncertainty is well understood. However, its computational cost grows with the number of uncertainty scenarios, prolonging the treatment planning process.

PURPOSE

We developed a fast and scalable distributed optimization platform that parallelizes the robust proton treatment plan computation over the uncertainty scenarios.

METHODS

We modeled the robust proton treatment planning problem as a weighted least-squares problem. To solve it, we employed an optimization technique called the alternating direction method of multipliers with Barzilai-Borwein step size (ADMM-BB). We reformulated the problem in such a way as to split the main problem into smaller subproblems, one for each proton therapy uncertainty scenario. The subproblems can be solved in parallel, allowing the computational load to be distributed across multiple processors (e.g., CPU threads/cores). We evaluated ADMM-BB on four head-and-neck proton therapy patients, each with 13 scenarios accounting for 3 mm setup and 3.5% range uncertainties. We then compared the performance of ADMM-BB with projected gradient descent (PGD) applied to the same problem.

RESULTS

For each patient, ADMM-BB generated a robust proton treatment plan that satisfied all clinical criteria with comparable or better dosimetric quality than the plan generated by PGD. However, ADMM-BB's total runtime averaged about 6 to 7 times faster. This speedup increased with the number of scenarios.

CONCLUSIONS

ADMM-BB is a powerful distributed optimization method that leverages parallel processing platforms, such as multicore CPUs, GPUs, and cloud servers, to accelerate the computationally intensive work of robust proton treatment planning. This results in (1) a shorter treatment planning process and (2) the ability to consider more uncertainty scenarios, which improves plan quality.

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 (SPArc_rot) 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 SPArc_rotuses a multi-CT robust optimization framework by taking into account of such rotational setup errors. Five cases representing different disease sites were evaluated. Both SPArc_originaland SPArc_rotplans 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 SPArc_rotcompared 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 SPArc_rot. 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 SPArc_rot.Significance.Rotational setup error is one of the major issues which could lead to significant dose perturbations. SPArc_rotplanning 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.

Proton beam range verification by means of ionoacoustic measurements at clinically relevant doses using a correlation-based evaluation

Purpose

The Bragg peak located at the end of the ion beam range is one of the main advantages of ion beam therapy compared to X-Ray radiotherapy. However, verifying the exact position of the Bragg peak within the patient online is a major challenge. The goal of this work was to achieve submillimeter proton beam range verification for pulsed proton beams of an energy of up to 220 MeV using ionoacoustics for a clinically relevant dose deposition of typically 2 Gy per fraction by i) using optimal proton beam characteristics for ionoacoustic signal generation and ii) improved signal detection by correlating the signal with simulated filter templates.

Methods

A water tank was irradiated with a preclinical 20 MeV proton beam using different pulse durations ranging from 50 ns up to 1 μs in order to maximise the signal-to-noise ratio (SNR) of ionoacoustic signals. The ionoacoustic signals were measured using a piezo-electric ultrasound transducer in the MHz frequency range. The signals were filtered using a cross correlation-based signal processing algorithm utilizing simulated templates, which enhances the SNR of the recorded signals. The range of the protons is evaluated by extracting the time of flight (ToF) of the ionoacoustic signals and compared to simulations from a Monte Carlo dose engine (FLUKA).

Results

Optimised SNR of 28.0 ± 10.6 is obtained at a beam current of 4.5 μA and a pulse duration of 130 ns at a total peak dose deposition of 0.5 Gy. Evaluated ranges coincide with Monte Carlo simulations better than 0.1 mm at an absolute range of 4.21 mm. Higher beam energies require longer proton pulse durations for optimised signal generation. Using the correlation-based post-processing filter a SNR of 17.8 ± 5.5 is obtained for 220 MeV protons at a total peak dose deposition of 1.3 Gy. For this clinically relevant dose deposition and proton beam energy, submillimeter range verification was achieved at an absolute range of 303 mm in water.

Conclusion

Optimal proton pulse durations ensure an ideal trade-off between maximising the ionoacoustic amplitude and minimising dose deposition. In combination with a correlation-based post-processing evaluation algorithm, a reasonable SNR can be achieved at low dose levels putting clinical applications for online proton or ion beam range verification into reach.

ALERT-RA: an aperture library-enabled real-time respiratory motion adaptive framework for 4D-VMAT

PURPOSE

To develop a framework for robust optimization of real-time respiratory motion adaptive VMAT treatment plans, and to evaluate the robustness of resulting plans to variations in tumor trajectory during delivery.

METHODS

The proposed framework is called aperture library-enabled real-time robust adaptation (ALERT-RA). A patient-specific library of optimized MLC apertures is defined for each combination of gantry angle and respiratory phase. The method assumes that the tumor is tracked in real-time throughout delivery, and the aperture corresponding to the current phase and gantry angle will be delivered. The aperture library is optimized by considering all possible tumor trajectories determined by a probabilistic respiratory motion model. Plan robustness to trajectory variations was evaluated by sampling a trajectory, and determining the corresponding dose, from the respiratory model for each fraction. The cumulative dose of the full treatment course was simulated 50 times. Percentile dose-volume histograms (PDVHs) were computed from these simulated treatments. The resulting plan quality and robustness of this method were compared to other previously published motion 4D-VMAT methods, including: an optimized tracking approach that assumes reproducible tumor motion, conformal tracking with aperture deformation, and a motion-encompassing method. Two fractionation schemes were tested to determine the possible effect on robustness: a conventional fractionation of 66 Gy in 33 fractions, and an SBRT course with 60 Gy in 5 fractions.

RESULTS

When considering target coverage, the ALERT-RA method was found to produce a plan which was more robust than those produced using the optimized or conformal tracking methods. Using the PDVH analysis, the 5th and 95th percentiles of the prescription dose volume for the conventionally fractioned plan were found to be (respectively) 79% and 82% for the optimized tracking approach, 81% and 83% for the conformal tracking approach, and 92% and 97% using the new ALERT-RA method. The motion-encompassing plan was slightly more robust than the ALERT-RA plan, with 5th and 95th percentiles at 94% and 95%, respectively. This came at a cost of higher dose to OARs, with the volume of lung receiving 5 Gy or more equal to 48% for the motion-encompassing plan versus 44% for the ALERT-RA plan. For the SBRT plan, the conformal tracking plan was similarly not robust, with 5th and 95th percentiles of the prescription dose volume equal to 88% and 89%. The optimized tracking SBRT plan gave values of 93% and 95%, and the motion-encompassing plan 94% and 95%, while the ALERT-RA gave values of 93% and 96%. The volume of lung receiving 20 Gy or more was slightly higher for the optimized tracking and motion-encompassing plans compared to the ALERT-RA plan, at 15%, 15%, and 14%, respectively.

CONCLUSIONS

Compared to other motion-adaptive VMAT approaches, the ALERT-RA algorithm is capable of delivering high-quality plans which are robust to variations in tumor motion trajectories.

Virtual particle Monte Carlo: A new concept to avoid simulating secondary particles in proton therapy dose calculation

BACKGROUND

In proton therapy dose calculation, Monte Carlo (MC) simulations are superior in accuracy but more time consuming, compared to analytical calculations. Graphic processing units (GPUs) are effective in accelerating MC simulations but may suffer thread divergence and racing condition in GPU threads that degrades the computing performance due to the generation of secondary particles during nuclear reactions.

PURPOSE

A novel concept of virtual particle (VP) MC (VPMC) is proposed to avoid simulating secondary particles in GPU-accelerated proton MC dose calculation and take full advantage of the computing power of GPU.

METHODS

Neutrons and gamma rays were ignored as escaping from the human body; doses of electrons, heavy ions, and nuclear fragments were locally deposited; the tracks of deuterons were converted into tracks of protons. These particles, together with primary and secondary protons, are considered to be the realistic particles. Histories of primary and secondary protons were replaced by histories of multiple VPs. Each VP corresponded to one proton (either primary or secondary). A continuous-slowing-down-approximation model, an ionization model, and a large angle scattering event model corresponding to nuclear interactions were developed for VPs by generating probability distribution functions (PDFs) based on simulation results of realistic particles using MCsquare. For efficient calculations, these PDFs were stored in the Compute Unified Device Architecture textures. VPMC was benchmarked with TOPAS and MCsquare in phantoms and with MCsquare in 13 representative patient geometries. Comparisons between the VPMC calculated dose and dose measured in water during patient-specific quality assurance (PSQA) of the selected 13 patients were also carried out. Gamma analysis was used to compare the doses derived from different methods and calculation efficiencies were also compared.

RESULTS

Integrated depth dose and lateral dose profiles in both homogeneous and inhomogeneous phantoms all matched well among VPMC, TOPAS, and MCsquare calculations. The 3D-3D gamma passing rates with a criterion of 2%/2 mm and a threshold of 10% was 98.49% between MCsquare and TOPAS and 98.31% between VPMC and TOPAS in homogeneous phantoms, and 99.18% between MCsquare and TOPAS and 98.49% between VPMC and TOPAS in inhomogeneous phantoms, respectively. In patient geometries, the 3D-3D gamma passing rates with 2%/2 mm/10% between dose distributions from VPMC and MCsquare were 98.56 ± 1.09% in patient geometries. The 2D-3D gamma analysis with 3%/2 mm/10% between the VPMC calculated dose distributions and the 2D measured planar dose distributions during PSQA was 98.91 ± 0.88%. VPMC calculation was highly efficient and took 2.84 ± 2.44 s to finish for the selected 13 patients running on four NVIDIA Ampere GPUs in patient geometries.

CONCLUSION

VPMC was found to achieve high accuracy and efficiency in proton therapy dose calculation.

Assessment of intrafractional prostate motion and its dosimetric impact in MRI-guided online adaptive radiotherapy with gating

PURPOSE

This study aimed to evaluate the intrafractional prostate motion captured during gated magnetic resonance imaging (MRI)-guided online adaptive radiotherapy for prostate cancer and analyze its impact on the delivered dose as well as the effect of gating.

METHODS

Sagittal 2D cine-MRI scans were acquired at 4 Hz during treatment at a ViewRay MRIdian (ViewRay Inc., Oakwood Village, OH, USA) MR linac. Prostate shifts in anterior-posterior (AP) and superior-inferior (SI) directions were extracted separately. Using the static dose cloud approximation, the planned fractional dose was shifted according to the 2D gated motion (residual motion in gating window) to estimate the delivered dose by superimposing and averaging the shifted dose volumes. The dose of a hypothetical non-gated delivery was reconstructed similarly using the non-gated motion. For the clinical target volume (CTV), rectum, and bladder, dose-volume histogram parameters of the planned and reconstructed doses were compared.

RESULTS

In total, 174 fractions (15.7 h of cine-MRI) from 10 patients were evaluated. The average (±1 σ) non-gated prostate motion was 0.6 ± 1.0 mm in the AP and 0.0 ± 0.6 mm in the SI direction with respect to the centroid position of the gating boundary. 95% of the shifts were within [-3.5, 2.7] mm in the AP and [-2.9, 3.2] mm in the SI direction. For the gated treatment and averaged over all fractions, CTV D_98% decreased by less than 2% for all patients. The rectum and the bladder D_2% increased by less than 3% and 0.5%, respectively. Doses reconstructed for gated and non-gated delivery were similar for most fractions.

CONCLUSION

A pipeline for extraction of prostate motion during gated MRI-guided radiotherapy based on 2D cine-MRI was implemented. The 2D motion data enabled an approximate estimation of the delivered dose. For the majority of fractions, the benefit of gating was negligible, and clinical dosimetric constraints were met, indicating safety of the currently adopted gated MRI-guided treatment workflow.