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The interplay between respiratory tumor motion and dose application by intensity modulated radiotherapy (IMRT) techniques can potentially lead to undesirable and non-intuitive deviations from the planned dose distribution. We developed a 4D Monte Carlo (MC) dose recalculation framework featuring statistical breathing curve sampling, to precisely simulate the dose distribution for moving target volumes aiming at a comprehensive assessment of interplay effects.We implemented a dose accumulation tool that enables dose recalculations of arbitrary breathing curves including the actual breathing curve of the patient. This MC dose recalculation framework is based on linac log-files, facilitating a high temporal resolution up to 0.1 s. By statistical analysis of 128 different breathing curves, interplay susceptibility of different treatment parameters was evaluated for an exemplary patient case. To facilitate prospective clinical application in the treatment planning stage, in which patient breathing curves or linac log-files are not available, we derived a log-file free version with breathing curves generated by a random walk approach. Interplay was quantified by standard deviations σ in DInterplay induced dose deviations for single fractions were observed and evaluated for IMRT and volumetric arc therapy (σIt is feasible to combine statistically sampled breathing curves with MC dose calculations. The universality of the presented framework allows comprehensive assessment of interplay effects in retrospective and prospective clinically relevant scenarios.

Inter-fractional variations of breathing pattern and patient anatomy introduce dose uncertainties in proton therapy. One approach to monitor these variations is to utilize the cone-beam computed tomography (CT, CBCT) scans routinely taken for patient positioning, reconstruct them as 4DCBCTs, and generate ‘virtual CTs’ (vCTs), combining the accurate CT numbers of the diagnostic 4DCT and the geometry of the daily 4DCBCT by using deformable image registration (DIR). In this study different algorithms for 4DCBCT reconstruction and DIR were evaluated. For this purpose, CBCT scans of a moving ex vivo porcine lung phantom with 663 and 2350 projections respectively were acquired, accompanied by an additional 4DCT as reference. The CBCT projections were sorted in 10 phase bins with the Amsterdam-shroud method and reconstructed phase-by-phase using first a FDK reconstruction from the Reconstruction Toolkit (RTK) and again an iterative reconstruction algorithm implemented in the Gadgetron Toolkit. The resulting 4DCBCTs were corrected by DIR of the corresponding 4DCT phases, using both a morphons algorithm from REGGUI and a b-spline deformation from Plastimatch. The resulting 4DvCTs were compared to the 4DCT by visual inspection and by calculating water equivalent thickness (WET) maps from the phantom's surface to the distal edge of a target from various angles. The optimized procedure was successfully repeated with mismatched input phases and on a clinical patient dataset. Proton treatment plans were simulated on the 4DvCTs and the dose distributions compared to the reference based on the 4DCT via gamma pass rate analysis. A combination of iterative reconstruction and morphons DIR yielded the most accurate 4DvCTs, with median WET differences under 2 mm and 3%/3 mm gamma pass rates per phase between 89% and 99%. These results suggest that image correction of iteratively reconstructed 4DCBCTs with a morphons DIR of the planning CT may yield sufficiently accurate 4DvCTs for daily time resolved proton dose calculations.

Proton radiotherapy (PT) requires accurate target alignment before each treatment fraction, ideally utilizing 3D in-room X-ray computed tomography (CT) imaging. Typically, the optimal patient position is determined based on anatomical landmarks or implanted markers. In the presence of non-rigid anatomical changes, however, the planning scenario cannot be exactly reproduced and positioning should rather aim at finding the optimal position in terms of the actually applied dose. In this work, dose-guided patient alignment, implemented as multicriterial optimization (MCO) problem, was investigated in the scope of intensity-modulated and double-scattered PT (IMPT and DSPT) for the first time. A method for automatically determining the optimal patient position with respect to pre-defined clinical goals was implemented. Linear dose interpolation was used to access a continuous space of potential patient shifts. Fourteen head and neck (H&N) and eight prostate cancer patients with up to five repeated CTs were included. Dose interpolation accuracy was evaluated and the potential dosimetric advantages of dose-guided over bony-anatomy-based patient alignment investigated by comparison of clinically relevant target and organ-at-risk (OAR) dose-volume histogram (DVH) parameters. Dose interpolation was found sufficiently accurate with average pass-rates of 90% and 99% for an exemplary H&N and prostate patient, respectively, using a 2% dose-difference criterion. Compared to bony-anatomy-based alignment, the main impact of automated MCO-based dose-guided positioning was a reduced dose to the serial OARs (spinal cord and brain stem) for the H&N cohort. For the prostate cohort, under-dosage of the target structures could be efficiently diminished. Limitations of dose-guided positioning were mainly found in reducing target over-dosage due to weight loss for H&N patients, which might require adaptation of the treatment plan. Since labor-intense online quality-assurance is not required for dose-guided patient positioning, it might, nevertheless, be considered an interesting alternative to full online re-planning for initially mitigating the effects of anatomical changes.

Purpose In proton radiation therapy, a relative biological effectiveness (RBE) equal to 1.1 is currently assumed, although biological experiments show that it is not constant. The purpose of this study was to quantify the uncertainties of a published biological model and explore their impact on variable RBE treatment plan (TP) optimization. Methods Two patient cases with a high and a low ( α / β ) x tumor were investigated. Firstly, intensity modulated proton therapy TPs assuming constant RBE were derived, and subsequently the variable RBE weighted dose (RWD), including the uncertainty originating in the fit to the experimental data and the uncertainty of the ( α / β ) x , were calculated. Secondly, TPs optimized for uniform biological effect assuming a variable RBE were created using the worst case tissue specific ( α / β ) x . Results For the nasopharyngeal cancer patient, the uncertainty of ( α / β ) x corresponded to a CTV D 98 confidence interval (CI) of (−2, +4)% while for the fit parameter CI was ( - 2 , + 1 ) %. For the standard fractionation prostate case the ( α / β ) x CI was ( - 7 , + 5 ) % and the fit parameter CI was ( - 3 , + 3 ) % . For the hypofractionated case both CIs were ( - 1 , + 1 ) %. In both patient cases, the RBE in most organs at risk (OARs) was significantly underestimated by the constant RBE approximation, whereas the situation was not as definite in the target volumes. Overdosage of OARs was reduced by using the biological effect optimization. Conclusion For the two patient cases, the RWD uncertainty from the fit parameter in the biological model contributed non-negligibly to the total uncertainty, depending on the patient case and the organ. The presented optimization strategy is a basic method for robust biological effect optimization to reduce potential consequences caused by the ( α / β ) x uncertainty.

Purpose Biological models to estimate the relative biological effectiveness (RBE) or the equivalent dose in 2 Gy fractions (EQD2) are needed for treatment planning and plan evaluation in carbon ion therapy. We present a model-independent, Monte Carlo based sensitivity analysis (SA) approach to quantify the impact of different uncertainties on the biological models. Methods and materials The Monte Carlo based SA is used for the evaluation of variations in biological parameters. The key property of this SA is the high number of simulation runs, each with randomized input parameters, allowing for a statistical variance-based ranking of the input variations. The potential of this SA is shown in a simplified one-dimensional treatment plan optimization. Physical properties of carbon ion beams (e.g. fragmentation) are simulated using the Monte Carlo code FLUKA. To estimate biological effects of ion beams compared to X-rays, we use the Local Effect Model (LEM) in the framework of the linear-quadratic (LQ) model. Currently, only uncertainties in the output of the biological models are taken into account. Results/conclusions The presented SA is suitable for evaluation of the impact of variations in biological parameters. Major advantages are the possibility to access and display the sensitivity of the evaluated quantity on several parameter variations at the same time. Main challenges for later use in three-dimensional treatment plan evaluation are computational time and memory usage. The presented SA can be performed with any analytical or numerical function and hence be applied to any biological model used in carbon ion therapy.

The adoption of cone beam computed tomography (CBCT) image guidance in proton therapy has spurred research on CBCT image correction for dose calculation. Initially, methods based on deformable image registration gained attention [1] , however projection correction approaches based on prior CT information [2] have been shown to perform well for several body sites [3] . The shading correction algorithm used in [2] , [3] relies on image processing of the subtraction of digitally reconstructed radiographs (DRR) of a DIR-registered prior CT, and CBCT projections. In this study we have performed Monte Carlo (MC) simulation of CBCT imaging to disentangle the different sources of projection degradation, and to evaluate the physicality of the shading correction. The GATE MC toolkit’s fixed-forced-detection actor was employed, along with source and detector models optimized for an Elekta XVI CBCT system, to simulate CBCT imaging of an electron density phantom. The shading correction algorithm was applied to the measured projections and the so-called scatter component (SCA) was compared to the MC simulated scatter. Measured and simulated CBCT projections (scatter + primary) agreed well, with the largest discrepancy found for a bone insert (3% of log transformed projections). Important disagreement was observed with the MC scatter signal when the contribution from beam hardening was kept in the SCA. Undoing the beam hardening correction from the SCA using functions derived from MC primary projections and DRRs greatly improved agreement of scatter signals from MC and SCA (3% on average), with the largest discrepancy found for the bone insert (12%). Residual discrepancies were shown to stem from the intrinsic limitations of the SCA. Proton therapy dose calculations on corrected CBCT will be presented in addition to the results above.

Purpose Proton therapy is widely used in state-of-the-art radiotherapy. However, precision of proton therapy could be affected by proton range uncertainties due to e.g. anatomy change, patient setting uncertainties. Prompt gamma (PG) imaging is investigated to monitor proton range in vivo by detecting PG emitted by nuclear de-excitation processes in the beam path. Despite ideal correlation between the penetration depth of PG signal and proton range in most cases, few literature studies reported that this correlation could deteriorate because of tissue heterogeneities. Hence, in this study, we investigate a new treatment planning approach accounting for PG imaging. Methods A research computational platform, combining Monte Carlo (MC, Geant4) pre-calculated pencil beams (subPB) with the Matlab-based TPS engine CERR (A computational Environment for Radiotherapy Research), was introduced for treatment planning. To optimize current treatment plans considering PG imaging, first, a MC treatment plan is created using a particle extension of CERR. Thereby, the PG emission and dose distribution for each subPB is obtained. Secondly, the PG fall-off position is evaluated and compared to the proton range (80% distal dose fall-off) for all subPBs. SubPBs with reliable PG-dose correlation are visualized in terms of beam-eye-view location and individual dose delivery. Few of these “good-correlation” subPBs are boosted in the new treatment plan via manual or automated selection to enable good detectability. Then, a re-optimized plan is created and recalculated on the same patient CT using Geant4 for evaluation. Results The new treatment planning approach was applied to 3 head & neck tumor patients. The dose distribution of the original and re-optimized treatment plan was found to be of comparable dosimetric quality. The positions and PG-dose correlations of highest-intensity subPBs in the original plan were random, while in the optimized plan the highest-intensity subPBs are with good PG-dose correlation and uniformly distributed across the entire field for a reliable and thorough monitoring. Conclusions A new treatment planning approach has been proposed, which could improve the treatment planning process in proton therapy by integrating the PG-based in vivo monitoring of the beam range for a safer and more controllable proton treatment. EU-MSCA GA n. 675265 (OMA).

Purpose This study investigates for the first time the feasibility of using deep learning for cone-beam CT (CBCT) intensity correction to enable accurate daily dose calculation and treatment adaptation in volumetric-modulated arc therapy (VMAT) and intensity-modulated proton therapy (IMPT). Current CBCT intensity correction approaches often show a lack of either speed or accuracy, which might be overcome by deep learning approaches. Methods Pre-treatment CBCTs and corresponding projections of 30 prostate cancer patients were considered. A previously validated technique for CBCT intensity correction, based on deformable image registration (DIR) of the planning CT to the daily CBCT and scatter estimation in projection space, served as reference (CBCTcor) [1] . Two alternative methods were investigated: A U-shaped deep convolutional neural network (U-Net) was trained to perform scatter correction in projection space, i.e., going from measured to corrected projections before reconstruction (CBCTScatterNet). Moreover, a generative adversarial network (GAN) was trained to perform a translation from the original CBCT (CBCTorg) to CBCTcor in image space, generating a so-called CBCTcorGAN. CBCTScatterNet and CBCTcorGAN were compared to CBCTcor in terms of mean absolute error (MAE) and mean error (ME). For eight exemplary patients, dose calculation accuracy in VMAT and IMPT was evaluated with respect to CBCTorg. Results Both, CBCTScatterNet and CBCTcorGAN, showed a substantially improved agreement to CBCTcor compared to CBCTorg. Mean MAE and ME decreased from 158HU and 152HU for CBCTorg to 39HU and 4HU for CBCTScatterNet and 57HU and −2HU for CBCTcorGAN, respectively. In a 2% dose-difference test, considering only voxels above 50% of the prescribed dose, mean pass-rates were 53% and 64% for CBCTScatterNet and CBCTcorGAN in IMPT. In VMAT, pass-rates of 90% and 97% were obtained for CBCTScatterNet and CBCTcorGAN using a 1% dose-difference criterion. Conclusions CBCT intensity correction using two different implementations of deep learning was found feasible. For VMAT, dose calculation accuracy was high, while for IMPT further improvements may be required. Compared to the reference correction method, deep learning techniques were less affected by DIR inaccuracies and allowed considerably faster CBCT correction within few seconds instead of minutes.

Purpose The interplay between dose application by complex techniques and respiratory motion of a tumor can potentially lead to undesirable and non-intuitive deviations from the planned dose distribution. We developed a 4D dose calculation framework to precisely simulate dose distributions for moving target volumes. In this study we randomly sample the simulated application of delivered dose fragments on 4D-CT phases. Methods The workflow combines MC dose calculation with linac-log files and dose accumulation based on 4D-CT images. Treatment plan fragments of 0.2s duration are retrieved from linac-log data and calculated on ten 4D-CT phases using MCverify/Hyperion V2.4 (research version of Monaco 3.2). The resulting dose fragments allow the simulation of arbitrary respiratory curves (e.g. changes in breathing frequency and pattern) with a resolution of 0.2 s by assigning every fragment to a distinct 4D-CT phase. Using deformable image registration (plastimatch) the dose fragments are accumulated by AVID, a software system for automated processing and analysis of radiotherapy data. In addition to the patient’s recorded normalized breathing curve, three statistical approaches are implemented: (1) random phase shift of the breathing curve, (2) random phase assignment of every 0.2s dose fragment and (3) random phase assignment of 1MU dose fragments. Results An exemplary 3 Gy, VMAT, SBRT treatment of a 9 cm3 lung tumor with 1.6 cm crano-caudal movement was analyzed. 128 random treatments were simulated for the three statistical approaches. In all three cases the mean 4D-CT calculated dose (original plan calculated on ten 4D-CT phases) and the mean of the randomly simulated doses agree. The dose deviations for the 128 runs are very similar for the three methods (average dose deviations: σ D 2 % ≈ 0.41 % , σ D 50 % ≈ 0.25 % and σ D 98 % ≈ 0.68 % for the three approaches). Conclusions The described random assignments (2) and (3) have similar statistics as the random start phase approach (1) and are hence promising methods to comprehensively cover treatment plan and technique specific interplay effects without the need for daily breathing curves. The MU-based random sampling approach (3) is in addition independent of the linac-log data. The introduced framework has the potential to comprehensively include breathing motion induced interplay effects in the treatment planning and evaluation process.