Physically constrained voxel-based penalty adaptation for ultra-fast IMRT planning.

Conventional treatment planning in intensity-modulated radiation therapy (IMRT) is a trial-and-error process that usually involves tedious tweaking of optimization parameters. Here, we present an algorithm that automates part of this process, in particular the adaptation of voxel-based penalties within normal tissue. Thereby, the proposed algorithm explicitly considers a priori known physical limitations of photon irradiation. The efficacy of the developed algorithm is assessed during treatment planning studies comprising 16 prostate and 5 head and neck cases. We study the eradication of hot spots in the normal tissue, effects on target coverage and target conformity, as well as selected dose volume points for organs at risk. The potential of the proposed method to generate class solutions for the two indications is investigated. Run-times of the algorithms are reported. Physically constrained voxel-based penalty adaptation is an adequate means to automatically detect and eradicate hot-spots during IMRT planning while maintaining target coverage and conformity. Negative effects on organs at risk are comparably small and restricted to lower doses. Using physically constrained voxel-based penalty adaptation, it was possible to improve the generation of class solutions for both indications. Considering the reported run-times of less than 20 s, physically constrained voxel-based penalty adaptation has the potential to reduce the clinical workload during planning and automated treatment plan generation in the long run, facilitating adaptive radiation treatments.

A digital male pelvis phantom series showing anatomical variations over the course of fractionated radiotherapy treatment.

BACKGROUND: Daily IGRT images show day-to-day anatomical variations in patients undergoing fractionated prostate radiotherapy. This is of particular importance in particle beam treatments. PURPOSE: To develop a digital phantom series showing variation in pelvic anatomy for evaluating treatment planning and IGRT procedures in particle radiotherapy. METHODS: A pelvic phantom series was developed from the planning MRI and kVCT (planning CT) images along with six of the daily serial MVCT images taken of a single patient treated with a full bladder on a Tomotherapy unit. The selected patient had clearly visible yet unexceptional internal anatomy variation. Prostate, urethra, bladder, rectum, bowel, bowel gas, bone and soft tissue were contoured and a single Hounsfield Unit was assigned to each region. Treatment plans developed on the kVCT for photon, proton and carbon beams were recalculated on each phantom to demonstrate a clinical application of the series. Proton plans were developed with and without robust optimization. RESULTS: Limited to axial slices with prostate, the bladder volume varied from 6 to 46 cm3, the rectal volume (excluding gas) from 22 to 52 cm3, and rectal gas volume from zero to 18 cm3. The water equivalent path length to the prostate varied by up to 1.5 cm . The variations resulted in larger changes in the RBE-weighted Dose Volume Histograms of the non-robust proton plan and the carbon plan compared to the robust proton plan, the latter similar to the photon plan. The prostate coverage (V100%) decreased by an average of 18% in the carbon plan, 16% in the non-robust proton plan, 1.8% in the robust proton plan, and 4.4% in the photon plan. The volume of rectum receiving 75% of the prescription dose (V75%) increased by an average of 3.7 cm3, 4.7 cm3, 1.9 cm3, and 0.6 cm3 in those four plans, respectively. CONCLUSIONS: The digital pelvic phantom series provides for quantitative investigation of IGRT procedures and new methods for improving accuracy in particle therapy and may be used in cross-institutional comparisons for clinical trial quality assurance.

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.

A Novel Inverse Algorithm To Solve the Integrated Optimization of Dose, Dose Rate, and Linear Energy Transfer of Proton FLASH Therapy With Sparse Filters.

PURPOSE: The recently proposed Integrated Physical Optimization Intensity Modulated Proton Therapy (IPO-IMPT) framework allows simultaneous optimization of dose, dose rate, and linear energy transfer (LET) for ultra-high dose rate (FLASH) treatment planning. Finding solutions to IPO-IMPT is difficult because of computational intensiveness. Nevertheless, an inverse solution that simultaneously specifies the geometry of a sparse filter and weights of a proton intensity map is desirable for both clinical and preclinical applications. Such solutions can reduce effective biologic dose to organs at risk in patients with cancer as well as reduce the number of animal irradiations needed to derive extra biologic dose models in preclinical studies. METHODS AND MATERIALS: Unlike the initial forward heuristic, this inverse IPO-IMPT solution includes simultaneous optimization of sparse range compensation, sparse range modulation, and spot intensity. The daunting computational tasks vital to this endeavor were resolved iteratively with a distributed computing framework to enable Simultaneous Intensity and Energy Modulation and Compensation (SIEMAC). SIEMAC was demonstrated on a human patient with central lung cancer and a minipig. RESULTS: SIEMAC simultaneously improves maps of spot intensities and patient-field-specific sparse range compensators and range modulators. For the patient with lung cancer, at our maximum nozzle current of 300 nA, dose rate coverage above 100 Gy/s increased from 57% to 96% in the lung and from 93% to 100% in the heart, and LET coverage above 4 keV/µm dropped from 68% to 9% in the lung and from 26% to <1% in the heart. For a simple minipig plan, the full-width half-maximum of the dose, dose rate, and LET distributions decreased by 30%, 1.6%, and 57%, respectively, again with similar target dose coverage, thus reducing uncertainty in these quantities for preclinical studies. CONCLUSIONS: The inverse solution to IPO-IMPT demonstrated the capability to simultaneously modulate subspot proton energy and intensity distributions for clinical and preclinical studies.

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.

Combined BNCT-CIRT treatment planning for glioblastoma using the effect-based optimization.

Objective. Boron neutron capture therapy (BNCT) and carbon ion radiotherapy (CIRT) are emerging treatment modalities for glioblastoma. In this study, we investigated the methodology and feasibility to combine BNCT and CIRT treatments. The combined treatment plan illustrated how the synergistic utilization of BNCT's biological targeting and CIRT's intensity modulation capabilities could lead to optimized treatment outcomes.Approach. The Monte Carlo toolkit, TOPAS, was employed to calculate the dose distribution for BNCT, while matRad was utilized for the optimization of CIRT. The biological effect-based approach, instead of the dose-based approach, was adopted to develop the combined BNCT-CIRT treatment plans for six patients diagnosed with glioblastoma, considering the different radiosensitivity and fraction. Five optional combined treatment plans with specific BNCT effect proportions for each patient were evaluated to identify the optimal treatment that minimizes damage on normal tissue.Main results. Individual BNCT exhibits a significant effect gradient along with the beam direction in the large tumor, while combined BNCT-CIRT treatments can achieve uniform effect delivery within the clinical target volume (CTV) through the effect filling with reversed gradient by the CIRT part. In addition, the increasing BNCT effect proportion in combined treatments can reduce damage in the normal brain tissue near the CTV. Besides, the combined treatments effectively minimize damage to the skin compared to individual BNCT treatments.Significance. The initial endeavor to combine BNCT and CIRT treatment plans is achieved by the effect-based optimization. The observed advantages of the combined treatment suggest its potential applicability for tumors characterized by pleomorphic, infiltrative, radioresistant and voluminous features.

An Integrated Physical Optimization Framework for Proton Stereotactic Body Radiation Therapy FLASH Treatment Planning Allows Dose, Dose Rate, and Linear Energy Transfer Optimization Using Patient-Specific Ridge Filters.

PURPOSE: Patient-specific ridge filters provide a passive means to modulate proton energy to obtain a conformal dose. Here we describe a new framework for optimization of filter design and spot maps to meet the unique demands of ultrahigh-dose-rate (FLASH) radiation therapy. We demonstrate an integrated physical optimization Intensity-modulated proton therapy (IMPT) (IPO-IMPT) approach for optimization of dose, dose-averaged dose rate (DADR), and dose-averaged linear energy transfer (LETd). METHODS AND MATERIALS: We developed an inverse planning software to design patient-specific ridge filters that spread the Bragg peak from a fixed-energy, 250-MeV beam to a proximal beam-specific planning target volume. The software defines patient-specific ridge filter pin shapes and uses a Monte Carlo calculation engine, based on Geant4, to provide dose and LET influence matrices. Plan optimization, using matRAD, accommodates the IPO-IMPT objective function considering dose, dose rate, and LET simultaneously with minimum monitor unit constraints. The framework enables design of both regularly spaced and sparse-optimized ridge filters, from which some pins are omitted to allow faster delivery and selective LET optimization. To demonstrate the framework, we designed ridge filters for 3 example patients with lung cancer and optimized the plans using IPO-IMPT. RESULTS: The IPO-IMPT framework selectively spared the organs at risk by reducing LET and increasing dose rate, relative to IMPT planning. Sparse-optimized ridge filters were superior to regularly spaced ridge filters in dose rate. Depending on which parameter is prioritized, volume distributions and histograms for dose, DADR, and LETd, using evaluation structures specific to heart, lung, and esophagus, show high levels of FLASH dose-rate coverage and/or reduced LETd, while maintaining dose coverage within the beam specific planning target volume. CONCLUSIONS: This proof-of-concept study demonstrates the feasibility of using an IPO-IMPT framework to accomplish proton FLASH stereotactic body proton therapy, accounting for dose, DADR, and LETd simultaneously.

Ionization detail parameters and cluster dose: a mathematical model for selection of nanodosimetric quantities for use in treatment planning in charged particle radiotherapy.

Objective. To propose a mathematical model for applying ionization detail (ID), the detailed spatial distribution of ionization along a particle track, to proton and ion beam radiotherapy treatment planning (RTP).Approach. Our model provides for selection of preferred ID parameters (Ip) for RTP, that associate closest to biological effects. Cluster dose is proposed to bridge the large gap between nanoscopicIpand macroscopic RTP. Selection ofIpis demonstrated using published cell survival measurements for protons through argon, comparing results for nineteenIp:Nk,k= 2, 3, …, 10, the number of ionizations in clusters ofkor more per particle, andFk,k= 1, 2, …, 10, the number of clusters ofkor more per particle. We then describe application of the model to ID-based RTP and propose a path to clinical translation.Main results. The preferredIpwereN4andF5for aerobic cells,N5andF7for hypoxic cells. Significant differences were found in cell survival for beams having the same LET or the preferredNk. Conversely, there was no significant difference forF5for aerobic cells andF7for hypoxic cells, regardless of ion beam atomic number or energy. Further, cells irradiated with the same cluster dose for theseIphad the same cell survival. Based on these preliminary results and other compelling results in nanodosimetry, it is reasonable to assert thatIpexist that are more closely associated with biological effects than current LET-based approaches and microdosimetric RBE-based models used in particle RTP. However, more biological variables such as cell line and cycle phase, as well as ion beam pulse structure and rate still need investigation.Significance. Our model provides a practical means to select preferredIpfrom radiobiological data, and to convertIpto the macroscopic cluster dose for particle RTP.

Long short-term memory networks for proton dose calculation in highly heterogeneous tissues.

PURPOSE: To investigate the feasibility and accuracy of proton dose calculations with artificial neural networks (ANNs) in challenging three-dimensional (3D) anatomies. METHODS: A novel proton dose calculation approach was designed based on the application of a long short-term memory (LSTM) network. It processes the 3D geometry as a sequence of two-dimensional (2D) computed tomography slices and outputs a corresponding sequence of 2D slices that forms the 3D dose distribution. The general accuracy of the approach is investigated in comparison to Monte Carlo reference simulations and pencil beam dose calculations. We consider both artificial phantom geometries and clinically realistic lung cases for three different pencil beam energies. RESULTS: For artificial phantom cases, the trained LSTM model achieved a 98.57% γ-index pass rate ([1%, 3 mm]) in comparison to MC simulations for a pencil beam with initial energy 104.25 MeV. For a lung patient case, we observe pass rates of 98.56%, 97.74%, and 94.51% for an initial energy of 67.85, 104.25, and 134.68 MeV, respectively. Applying the LSTM dose calculation on patient cases that were fully excluded from the training process yields an average γ-index pass rate of 97.85%. CONCLUSIONS: LSTM networks are well suited for proton dose calculation tasks. Further research, especially regarding model generalization and computational performance in comparison to established dose calculation methods, is warranted.

Combined proton-photon treatment for breast cancer.

Objective.Proton therapy remains a limited resource due to gantry size and its cost. Recently, a new design without a gantry has been suggested. It may enable combined proton-photon therapy (CPPT) in conventional bunkers and allow the widespread use of protons. In this work, we explore this concept for breast cancer.Methods.The treatment room consists of a LINAC for intensity modulated radiation therapy (IMRT), a fixed proton beamline (FBL) with beam scanning and a motorized couch for treatments in lying positions with accurate patient setup. Thereby, proton and photon beams are delivered in the same fraction. Treatment planning is performed by simultaneously optimizing IMRT and IMPT plans based on the cumulative dose. The concept is investigated for three breast cancers where the goal is to minimize mean dose to the heart and lung while delivering 40.05 Gy in 15 fractions to the PTV with a SIB of 48 Gy to the tumor bed. The probabilistic approach is applied to mitigate the sensitivity to range uncertainties.Results. CPPT is particularly advantageous for irradiating concave target volumes that wrap around a curved chest wall. There, protons may deliver dose to the peripheral and medial parts of the target volume including lymph nodes. Thereby, the mean dose in normal tissues is reduced compared to single-modality IMRT. However, tangential photon beams may treat parts of the target volume near the interface to the lung. To ensure target coverage for range undershoot in an IMPT plan, proton beams have to deliberately overshoot into the lung tissue-a problem that can be mitigated via the photon component which ensures plan conformity and robustness.Conclusion.CPPT using an FBL may represent a realistic approach to make protons available to more patients. In addition, CPPT may generally improve treatment quality compared to both single-modality proton and photon treatments.

Joint Optimization of Photon-Carbon Ion Treatments for Glioblastoma.

PURPOSE: Carbon ions are radiobiologically more effective than photons and are beneficial for treating radioresistant gross tumor volumes (GTV). However, owing to a reduced fractionation effect, they may be disadvantageous for treating infiltrative tumors, in which healthy tissue inside the clinical target volume (CTV) must be protected through fractionation. This work addresses the question: What is the ideal combined photon-carbon ion fluence distribution for treating infiltrative tumors given a specific fraction allocation between photons and carbon ions? METHODS AND MATERIALS: We present a method to simultaneously optimize sequentially delivered intensity modulated photon (IMRT) and carbon ion (CIRT) treatments based on cumulative biological effect, incorporating both the variable relative biological effect of carbon ions and the fractionation effect within the linear quadratic model. The method is demonstrated for 6 glioblastoma patients in comparison with the current clinical standard of independently optimized CIRT-IMRT plans. RESULTS: Compared with the reference plan, joint optimization strategies yield inhomogeneous photon and carbon ion dose distributions that cumulatively deliver a homogeneous biological effect distribution. In the optimal distributions, the dose to CTV is mostly delivered by photons and carbon ions are restricted to the GTV with variations depending on tumor size and location. Improvements in conformity of high-dose regions are reflected by a mean EQD2 reduction of 3.29 ± 1.22 Gy in a dose fall-off margin around the CTV. Carbon ions may deliver higher doses to the center of the GTV, and photon contributions are increased at interfaces with CTV and critical structures. This results in a mean EQD2 reduction of 8.3 ± 2.28 Gy, in which the brain stem abuts the target volumes. CONCLUSIONS: We have developed a biophysical model to optimize combined photon-carbon ion treatments. For 6 glioblastoma patient cases, we show that our approach results in a more targeted application of carbon ions that (1) reduces dose in normal tissues within the target volume, which can only be protected through fractionation; and (2) boosts central target volume regions to reduce integral dose. Joint optimization of IMRT-CIRT treatments enable the exploration of a new spectrum of plans that can better address physical and radiobiological treatment planning challenges.

Analytical probabilistic modeling of dose-volume histograms.

PURPOSE: Radiotherapy, especially with charged particles, is sensitive to executional and preparational uncertainties that propagate to uncertainty in dose and plan quality indicators, for example, dose-volume histograms (DVHs). Current approaches to quantify and mitigate such uncertainties rely on explicitly computed error scenarios and are thus subject to statistical uncertainty and limitations regarding the underlying uncertainty model. Here we present an alternative, analytical method to approximate moments, in particular expectation value and (co)variance, of the probability distribution of DVH-points, and evaluate its accuracy on patient data. METHODS: We use Analytical Probabilistic Modeling (APM) to derive moments of the probability distribution over individual DVH-points based on the probability distribution over dose. By using the computed moments to parameterize distinct probability distributions over DVH-points (here normal or beta distributions), not only the moments but also percentiles, that is, α - DVHs, are computed. The model is subsequently evaluated on three patient cases (intracranial, paraspinal, prostate) in 30- and single-fraction scenarios by assuming the dose to follow a multivariate normal distribution, whose moments are computed in closed-form with APM. The results are compared to a benchmark based on discrete random sampling. RESULTS: The evaluation of the new probabilistic model on the three patient cases against a sampling benchmark proves its correctness under perfect assumptions as well as good agreement in realistic conditions. More precisely, ca. 90% of all computed expected DVH-points and their standard deviations agree within 1% volume with their empirical counterpart from sampling computations, for both fractionated and single fraction treatments. When computing α - DVH, the assumption of a beta distribution achieved better agreement with empirical percentiles than the assumption of a normal distribution: While in both cases probabilities locally showed large deviations (up to ±0.2), the respective - DVHs for α={0.05,0.5,0.95} only showed small deviations in respective volume (up to ±5% volume for a normal distribution, and up to 2% for a beta distribution). A previously published model from literature, which was included for comparison, exhibited substantially larger deviations. CONCLUSIONS: With APM we could derive a mathematically exact description of moments of probability distributions over DVH-points given a probability distribution over dose. The model generalizes previous attempts and performs well for both choices of probability distributions, that is, normal or beta distributions, over DVH-points.

Analytical incorporation of fractionation effects in probabilistic treatment planning for intensity-modulated proton therapy.

PURPOSE: We show that it is possible to explicitly incorporate fractionation effects into closed-form probabilistic treatment plan analysis and optimization for intensity-modulated proton therapy with analytical probabilistic modeling (APM). We study the impact of different fractionation schemes on the dosimetric uncertainty induced by random and systematic sources of range and setup uncertainty for treatment plans that were optimized with and without consideration of the number of treatment fractions. METHODS: The APM framework is capable of handling arbitrarily correlated uncertainty models including systematic and random errors in the context of fractionation. On this basis, we construct an analytical dose variance computation pipeline that explicitly considers the number of treatment fractions for uncertainty quantitation and minimization during treatment planning. We evaluate the variance computation model in comparison to random sampling of 100 treatments for conventional and probabilistic treatment plans under different fractionation schemes (1, 5, 30 fractions) for an intracranial, a paraspinal and a prostate case. The impact of neglecting the fractionation scheme during treatment planning is investigated by applying treatment plans that were generated with probabilistic optimization for 1 fraction in a higher number of fractions and comparing them to the probabilistic plans optimized under explicit consideration of the number of fractions. RESULTS: APM enables the construction of an analytical variance computation model for dose uncertainty considering fractionation at negligible computational overhead. It is computationally feasible (a) to simultaneously perform a robustness analysis for all possible fraction numbers and (b) to perform a probabilistic treatment plan optimization for a specific fraction number. The incorporation of fractionation assumptions for robustness analysis exposes a dose to uncertainty trade-off, i.e., the dose in the organs at risk is increased for a reduced fraction number and/or for more robust treatment plans. By explicit consideration of fractionation effects during planning, we demonstrate that it is possible to exploit this trade-off during optimization. APM optimization considering the fraction number reduced the dose in organs at risk compared to conventional probabilistic optimization neglecting the fraction number. CONCLUSION: APM enables computationally efficient incorporation of fractionation effects in probabilistic uncertainty analysis and probabilistic treatment plan optimization. The consideration of the fractionation scheme in probabilistic treatment planning reveals the trade-off between number of fractions, nominal dose, and treatment plan robustness.

Development of the open-source dose calculation and optimization toolkit matRad.

PURPOSE: We report on the development of the open-source cross-platform radiation treatment planning toolkit matRad and its comparison against validated treatment planning systems. The toolkit enables three-dimensional intensity-modulated radiation therapy treatment planning for photons, scanned protons and scanned carbon ions. METHODS: matRad is entirely written in Matlab and is freely available online. It re-implements well-established algorithms employing a modular and sequential software design to model the entire treatment planning workflow. It comprises core functionalities to import DICOM data, to calculate and optimize dose as well as a graphical user interface for visualization. matRad dose calculation algorithms (for carbon ions this also includes the computation of the relative biological effect) are compared against dose calculation results originating from clinically approved treatment planning systems. RESULTS: We observe three-dimensional γ-analysis pass rates ≥ 99.67% for all three radiation modalities utilizing a distance to agreement of 2 mm and a dose difference criterion of 2%. The computational efficiency of matRad is evaluated in a treatment planning study considering three different treatment scenarios for every radiation modality. For photons, we measure total run times of 145 s-1260 s for dose calculation and fluence optimization combined considering 4-72 beam orientations and 2608-13597 beamlets. For charged particles, we measure total run times of 63 s-993 s for dose calculation and fluence optimization combined considering 9963-45574 pencil beams. Using a CT and dose grid resolution of 0.3 cm3 requires a memory consumption of 1.59 GB-9.07 GB and 0.29 GB-17.94 GB for photons and charged particles, respectively. CONCLUSION: The dosimetric accuracy, computational performance and open-source character of matRad encourages a future application of matRad for both educational and research purposes.