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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.

Background The aim of this study was to evaluate and compare the performance of intensity modulated radiation therapy (IMRT) plans, planned for low-field strength magnetic resonance (MR) guided linear accelerator (linac) delivery (labelled IMRT MRL plans), and clinical conventional volumetric modulated arc therapy (VMAT) plans, for the treatment of prostate cancer (PCa). Both plans used the original planning target volume (PTV) margins. Additionally, the potential dosimetric benefits of MR-guidance were estimated, by creating IMRT MRL plans using smaller PTV margins. Materials and methods 20 PCa patients previously treated with conventional VMAT were considered. For each patient, two different IMRT MRL plans using the low-field MR-linac treatment planning system were created: one with original (orig.) PTV margins and the other with reduced (red.) PTV margins. Dose indices related to target coverage, as well as dose-volume histogram (DVH) parameters for the target and organs at risk (OAR) were compared. Additionally, the estimated treatment delivery times and the number of monitor units (MU) of each plan were evaluated. Results The dose distribution in the high dose region and the target volume DVH parameters (D98%, D50%, D2% and V95%) were similar for all three types of treatment plans, with deviations below 1% in most cases. Both IMRT MRL plans (orig. and red. PTV margins) showed similar homogeneity indices (HI), however worse values for the conformity index (CI) were also found when compared to VMAT. The IMRT MRL plans showed similar OAR sparing when the orig. PTV margins were used but a significantly better sparing was feasible when red. PTV margins were applied. Higher number of MU and longer predicted treatment delivery times were seen for both IMRT MRL plans. Conclusions A comparable plan quality between VMAT and IMRT MRL plans was achieved, when applying the same PTV margin. However, online MR-guided adaptive radiotherapy allows for a reduction of PTV margins. With a red. PTV margin, better sparing of the surrounding tissues can be achieved, while maintaining adequate target coverage. Nonetheless, longer treatment delivery times, characteristic for the IMRT technique, have to be expected.

PURPOSE The implementation of volumetric in-room imaging for online adaptive radiotherapy makes extensive testing of this image data for treatment planning necessary. Especially for proton beams the higher sensitivity to stopping power properties of the tissue results in more stringent requirements. Current approaches mainly focus on recalculation of the plans on the new image data, lacking experimental verification, and ignoring the impact on the plan re-optimization process. The aim of this study was to use gel and film dosimetry coupled with a three-dimensional (3D) printed head phantom (based on the planning CT of the patient) for 3D range verification of intensity-corrected cone beam computed tomography (CBCT) image data for adaptive proton therapy. METHODS Single field uniform dose pencil beam scanning proton plans were optimized for three different patients on the patients' planning CT (planCT) and the patients' intensity-corrected CBCT (scCBCT) for the same target volume using the same optimization constraints. The CBCTs were corrected on projection level using the planCT as a prior. The dose optimized on planCT and recalculated on scCBCT was compared in terms of proton range differences (80% distal fall-off, recalculation). Moreover, the dose distribution resulting from recalculation of the scCBCT-optimized plan on the planCT and the original planCT dose distribution were compared (simulation). Finally, the two plans of each patient were irradiated on the corresponding patient-specific 3D printed head phantom using gel dosimetry inserts for one patient and film dosimetry for all three patients. Range differences were extracted from the measured dose distributions. The measured and the simulated range differences were corrected for range differences originating from the initial plans and evaluated. RESULTS The simulation approach showed high agreement with the standard recalculation approach. The median values of the range differences of these two methods agreed within 0.1 mm and the interquartile ranges (IQRs) within 0.3 mm for all three patients. The range differences of the film measurement were accurately matching with the simulation approach in the film plane. The median values of these range differences deviated less than 0.1 mm and the IQRs less than 0.4 mm. For the full 3D evaluation of the gel range differences, the median value and IQR matched those of the simulation approach within 0.7 and 0.5 mm, respectively. scCBCT- and planCT-based dose distributions were found to have a range agreement better than 3 mm (median and IQR) for all considered scenarios (recalculation, simulation, and measurement). CONCLUSIONS The results of this initial study indicate that an online adaptive proton workflow based on scatter-corrected CBCT image data for head irradiations is feasible. The novel presented measurement- and simulation-based method was shown to be equivalent to the standard literature recalculation approach. Additionally, it has the capability to catch effects of image differences on the treatment plan optimization. This makes the measurement-based approach particularly interesting for quality assurance of CBCT-based online adaptive proton therapy. The observed uncertainties could be kept within those of the registration and positioning. The proposed validation could also be applied for other alternative in-room images, e.g. for MR-based pseudoCTs.

Purpose Cancer remains to be a leading cause of death worldwide, accounting for almost 10 million deaths in 2018. Radiation therapy (RT) is a common nonsurgical treatment in the management of patients with cancer that reduces disease recurrence and improves overall survival. However, preclinical RT modeling for accelerated bench-to-bedside translation of combination therapies is largely missing. While genetically engineered mouse models (GEMM) faithfully recapitulate human disease, conventional linear particle accelerator systems, commonly utilized in clinical settings, are not suited for state-of-the-art, image-guided targeted RT (IGRT) of these murine autochthonous tumors. Thus, we employed the CyberKnife (Accuray) platform for IGRT of GEMM-derived non-small cell lunger cancer (NSCLC) lesions. The CyberKnife (CK) is a stereotactic radiosurgery system (SRS) delivering high-dose RT precisely to the target area with minimal damage to the surrounding tissues based on intra-fraction image-guidance. Material and methods GEMM-derived NSCLC flank tumors driven by oncogenic Kras (KrasLSL-G12D/+) and deletion of Tp53 (Trp53fl/fl) were irradiated using the CK RT platform. We applied IGRT of 2, 4, 6, and 8 Gy using field sizes of 5 to 12.5 mm to average gross tumor volumes (GTV) of 0.9 cm3 (minimal 0.03 cm3) using Xsight Spine Tracking (Accuray) spine-based tumor localization. Results We found that a 0 mm planning target volume (PTV) margin is sufficient for IGRT of murine tumors using the CK. Furthermore, we analyzed the impact of CK-mediated IGRT on tumor infiltrating leukocytes by flow cytometry. We observed that higher RT doses (6-8 Gy) decreased absolute cell numbers of lymphocytes and myeloid cells by approximately half compared to low doses (2-4 Gy) or mock treated tumors within one hour, but even with low dose RT (2 Gy) tumor infiltrating leukocytes (TIL) were found to be reduced after 8 to 24 hours and recovered partly after 3 days. Conclusion In summary, we here demonstrate that the CK RT system allows for targeted IGRT of murine tumors with high precision and thus constitutes a novel promising platform for translational mouse RT studies, particularly performed in a longitudinal multimodal manner.

Objective. CT-mesh hybrid phantoms (or ‘hybrid(s)’) made from integrated patient CT data and mesh-type reference computational phantoms (MRCPs) can be beneficial for patient-specific whole-body dose evaluation, but this benefit has yet to be evaluated for second cancer risk prediction. The purpose of this study is to compare the hybrid’s ability to predict risk throughout the body with a patient-scaled MRCP against ground truth whole-body CTs (WBCTs). Approach. Head and neck active scanning proton treatment plans were created for and simulated on seven hybrids and the corresponding scaled MRCPs and WBCTs. Equivalent dose throughout the body was calculated and input into five second cancer risk models for both excess absolute and excess relative risk (EAR and ERR). The hybrid phantom was evaluated by comparing equivalent dose and risk predictions against the WBCT. Main results. The hybrid most frequently provides whole-body second cancer risk predictions which are closer to the ground truth when compared to a scaled MRCP alone. The performance of the hybrid relative to the scaled MRCP was consistent across ERR, EAR, and all risk models. For all in-field organs, where the hybrid shares the WBCT anatomy, the hybrid was better than or equal to the scaled MRCP for both equivalent dose and risk prediction. For out-of-field organs across all patients, the hybrid’s equivalent dose prediction was superior than the scaled MRCP in 48% of all comparisons, equivalent for 34%, and inferior for 18%. For risk assessment in the same organs, the hybrid’s prediction was superior than the scaled MRCP in 51.8% of all comparisons, equivalent in 28.6%, and inferior in 19.6%. Significance. Whole-body risk predictions from the CT-mesh hybrid have shown to be more accurate than those from a reference phantom alone. These hybrids could aid in risk-optimized treatment planning and individual risk assessment to minimize second cancer incidence.

Purpose In photon radiotherapy, respiratory‐induced target motion can be accounted for by internal target volumes (ITV) or mid‐ventilation target volumes (midV) defined on the basis of four‐dimensional computed tomography (4D‐CT). Intrinsic limitations of these approaches can result in target volumes that are not representative for the gross tumor volume (GTV) motion over the course of treatment. To address these limitations, we propose a novel patient‐specific ITV definition method based on real‐time 4D magnetic resonance imaging (rt‐4DMRI). Methods Three lung cancer patients underwent weekly rt‐4DMRI scans. A total of 24 datasets were included in this retrospective study. The GTV was contoured on breath‐hold MR images and propagated to all rt‐4DMRI images by deformable image registration. Different targets were created for the first (reference) imaging sessions: ITVs encompassing all GTV positions over the complete (ITVurn:x-wiley:00942405:media:mp14023:mp14023-math-0001) or partial acquisition time (urn:x-wiley:00942405:media:mp14023:mp14023-math-0002), ITVs including only voxels with a GTV probability‐of‐presence (POP) of at least 5% (urn:x-wiley:00942405:media:mp14023:mp14023-math-0003) or 10% (urn:x-wiley:00942405:media:mp14023:mp14023-math-0004), and the mid‐ventilation GTV position. Reference planning target volumes (urn:x-wiley:00942405:media:mp14023:mp14023-math-0005) were created by adding margins around the ITVs and midV target volumes. The geometrical overlap of the urn:x-wiley:00942405:media:mp14023:mp14023-math-0006 with urn:x-wiley:00942405:media:mp14023:mp14023-math-0007 from the six to eight subsequent imaging sessions on days n was quantified in terms of the Dice similarity coefficient (DSC), sensitivity [SE: (urn:x-wiley:00942405:media:mp14023:mp14023-math-0008)/urn:x-wiley:00942405:media:mp14023:mp14023-math-0009] and precision [PRE: (urn:x-wiley:00942405:media:mp14023:mp14023-math-0010)/urn:x-wiley:00942405:media:mp14023:mp14023-math-0011] as surrogates for target coverage and normal tissue sparing. Results Patient‐specific analysis yielded a high variance of the overlap values of urn:x-wiley:00942405:media:mp14023:mp14023-math-0012, when different periods within the reference imaging session were sampled. The mid‐ventilation‐based PTVs were smaller than the ITV‐based PTVs. While the SE was high for patients with small breathing pattern variations, changes of the median breathing amplitudes in different imaging sessions led to inferior SE values for the mid‐ventilation PTV for one patient. In contrast, urn:x-wiley:00942405:media:mp14023:mp14023-math-0013 and urn:x-wiley:00942405:media:mp14023:mp14023-math-0014 showed higher SE values with a higher robustness against interfractional changes, at the cost of larger target volumes. Conclusions The results indicate that rt‐4DMRI could be valuable for the definition of target volumes based on the GTV POP to achieve a higher robustness against interfractional changes than feasible with today’s 4D‐CT‐based target definition concepts.

Objective. As cancer survivorship increases, there is growing interest in minimizing the late effects of radiation therapy such as radiogenic second cancer, which may occur anywhere in the body. Assessing the risk of late effects requires knowledge of the dose distribution throughout the whole body, including regions far from the treatment field, beyond the typical anatomical extent of clinical computed tomography (CT) scans. Approach. A hybrid phantom was developed which consists of in-field patient CT images extracted from ground truth whole-body CT scans, out-of-field mesh phantoms scaled to basic patient measurements, and a blended transition region. Four of these hybrid phantoms were created, representing male and female patients receiving proton therapy treatment in pelvic and cranial sites. To assess the performance of the hybrid approach, we simulated treatments using the hybrid phantoms, the scaled and unscaled mesh phantoms, and the ground truth whole-body CTs. We calculated absorbed dose and equivalent dose in and outside of the treatment field, with a focus on neutrons induced in the patient by proton therapy. Proton and neutron dose was calculated using a general purpose Monte Carlo code. Main results. The hybrid phantom provided equal or superior accuracy in calculated organ dose and equivalent dose values relative to those obtained using the mesh phantoms in 78% in all selected organs and calculated dose quantities. Comparatively the default mesh and scaled mesh were equal or superior to the other phantoms in 21% and 28% of cases respectively. Significance. The proposed methodology for hybrid synthesis provides a tool for whole-body organ dose estimation for individual patients without requiring CT scans of their entire body. Such a capability would be useful for personalized assessment of late effects and risk-optimization of treatment plans.

To date, no universally accepted technique for the evaluation of the overall dosimetric performance of hybrid integrated magnetic resonance imaging (MR) - linear accelerators (linacs) is available. We report on the suitability and reliability of a novel phantom with modular inserts for combined polymer gel (PG) and ionisation chamber (IC) measurements at a 0.35 T MR-linac.Three 3D-printed, modular head phantoms, based on real patient anatomy, were used for repeated (2 times) PG irradiations of cranial treatment plans on a 0.35 T MR-linac. The PG readout was performed on two 1.5 T diagnostic MR-scanners to reduce scanning time. The PG dose volumes were normalised to the IC dose (normalised dose N1) and to the median planning target volume dose (normalised dose N2). Linearity of the PG dose response was validated and dose profiles, centres of mass (COM) of the 95% isodoses and dose volume histograms (DVH) were compared between planned and measured dose distributions and a 3D gamma analysis was performed.Dose linearity of the PG was good (RThe new modular phantom was found practical for use at a 0.35 T MR-linac. In contrast to the high dose region, larger mean deviations were found in the mid dose range. The PG measurements showed high reproducibility. The MR-linac performed well in a non-adaptive setting in terms of spatial and dosimetric accuracy.

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.

Introduction: The recent developments of magnetic resonance (MR) based adaptive strategies for photon and, potentially for proton therapy, require a fast and reliable conversion of MR images to X-ray computed tomography (CT) values. CT values are needed for photon and proton dose calculation. The improvement of conversion results employing a 3D deep learning approach is evaluated. Material and methods: A database of 89 T1-weighted MR head scans with about 100 slices each, including rigidly registered CTs, was created. Twenty-eight validation patients were randomly sampled, and four patients were selected for application. The remaining patients were used to train a 2D and a 3D U-shaped convolutional neural network (Unet). A stack size of 32 slices was used for 3D training. For all application cases, volumetric modulated arc therapy photon and single-field uniform dose pencil-beam scanning proton plans at four different gantry angles were optimized for a generic target on the CT and recalculated on 2D and 3D Unet-based pseudoCTs. Mean (absolute) error (MAE/ME) and a gradient sharpness estimate were used to quantify the image quality. Three-dimensional gamma and dose difference analyses were performed for photon (gamma criteria: 1%, 1 mm) and proton dose distributions (gamma criteria: 2%, 2 mm). Range (80% fall off) differences for beam’s eye view profiles were evaluated for protons. Results: Training 36 h for 1000 epochs in 3D (6 h for 200 epochs in 2D) yielded a maximum MAE of 147 HU (135 HU) for the application patients. Except for one patient gamma pass rates for photon and proton dose distributions were above 96% for both Unets. Slice discontinuities were reduced for 3D training at the cost of sharpness. Conclusions: Image analysis revealed a slight advantage of 2D Unets compared to 3D Unets. Similar dose calculation performance was reached for the 2D and 3D network.

Purpose The need for four-dimensional (4D) treatment planning becomes indispensable when it comes to radiation therapy for moving tumors in the thoracic and abdominal regions. The primary purpose of this study is to combine the actual breathing trace during each individual treatment fraction with the Linac's log file information and Monte Carlo 4D dose calculations. We investigated this workflow on multiple computed tomography (CT) datasets in a clinical environment for stereotactic body radiation therapy (SBRT) treatment planning. Methods We have developed a workflow, which allows us to recalculate absorbed dose to a 4DCT dataset using Monte Carlo calculation methods and accumulate all 4D doses in order to compare them to the planned dose using the Linac's log file, a 4DCT dataset, and the patient's actual breathing curve for each individual fraction. For five lung patients, three-dimensional-conformal radiation therapy (3D-CRT) and volumetric modulated arc treatment (VMAT) treatment plans were generated on four different CT image datasets: a native free-breathing 3DCT, an average intensity projection (AIP) and a maximum intensity projection (MIP) CT both obtained from a 4DCT, and a 3DCT with density overrides based on the 3DCT (DO). The Monte Carlo 4D dose has been calculated on each 4DCT phase using the Linac's log file and the patient's breathing trace as a surrogate for tumor motion and dose was accumulated to the gross tumor volume (GTV) at the 50% breathing phase (end of exhale) using deformable image registration. Results Δ D 98 % and Δ D 2 % between 4D dose and planned dose differed largely for 3DCT-based planning and also for DO in three patients. Least dose differences between planned and recalculated dose have been found for AIP and MIP treatment planning which both tend to be superior to DO, but the results indicate a dependency on the breathing variability, tumor motion, and size. An interplay effect has not been observed in the small patient cohort. Conclusions We have developed a workflow which, to our best knowledge, is the first incorporation of the patient breathing trace over the course of all individual treatment fractions with the Linac's log file information and 4D Monte Carlo recalculations of the actual treated dose. Due to the small patient cohort, no clear recommendation on which CT can be used for SBRT treatment planning can be given, but the developed workflow, after adaption for clinical use, could be used to enhance a priori 4D Monte Carlo treatment planning in the future and help with the decision on which CT dataset treatment planning should be carried out.

Proton therapy treatment for lungs remains challenging as images enabling the detection of inter- and intra-fractional motion, which could be used for proton dose adaptation, are not readily available. 4D computed tomography (4DCT) provides high image quality but is rarely available in-room, while in-room 4D cone beam computed tomography (4DCBCT) suffers from image quality limitations stemming mostly from scatter detection. This study investigated the feasibility of using virtual 4D computed tomography (4DvCT) as a prior for a phase-per-phase scatter correction algorithm yielding a 4D scatter corrected cone beam computed tomography image (4DCBCTcor), which can be used for proton dose calculation. 4DCT and 4DCBCT scans of a porcine lung phantom, which generated reproducible ventilation, were acquired with matching breathing patterns. Diffeomorphic Morphons, a deformable image registration algorithm, was used to register the mid-position 4DCT to the mid-position 4DCBCT and yield a 4DvCT. The 4DCBCT was reconstructed using motion-aware reconstruction based on spatial and temporal regularization (MA-ROOSTER). Successively for each phase, digitally reconstructed radiographs of the 4DvCT, simulated without scatter, were exploited to correct scatter in the corresponding CBCT projections. The 4DCBCTcor was then reconstructed with MA-ROOSTER using the corrected CBCT projections and the same settings and deformation vector fields as those already used for reconstructing the 4DCBCT. The 4DCBCTcor and the 4DvCT were evaluated phase-by-phase, performing proton dose calculations and comparison to those of a ground truth 4DCT by means of dose-volume-histograms (DVH) and gamma pass-rates (PR). For accumulated doses, DVH parameters deviated by at most 1.7% in the 4DvCT and 2.0% in the 4DCBCTcor case. The gamma PR for a (2%, 2 mm) criterion with 10% threshold were at least 93.2% (4DvCT) and 94.2% (4DCBCTcor), respectively. The 4DCBCTcor technique enabled accurate proton dose calculation, which indicates the potential for applicability to clinical 4DCBCT scans.

Particle therapy treatment planning requires accurate volumetric maps of the relative stopping power, which can directly be acquired using proton computed tomography (pCT). With fluence-modulated pCT (FMpCT) imaging fluence is concentrated in a region-of-interest (ROI), which can be the vicinity of the treatment beam path, and imaging dose is reduced elsewhere. In this work we present a novel optimization algorithm for FMpCT which, for the first time, calculates modulated imaging fluences for joint imaging dose and image variance objectives. Thereby, image quality is maintained in the ROI to ensure accurate calculations of the treatment dose, and imaging dose is minimized outside the ROI with stronger minimization penalties given to imaging organs-at-risk. The optimization requires an initial scan at uniform fluence or a previous x-ray CT scan. We simulated and optimized FMpCT images for three pediatric patients with tumors in the head region. We verified that the target image variance inside the ROI was achieved and demonstrated imaging dose reductions outside of the ROI of 74% on average, reducing the imaging dose from 1.2 to 0.3 mGy. Such dose savings are expected to be relevant compared to the therapeutic dose outside of the treatment field. Treatment doses were re-calculated on the FMpCT images and compared to treatment doses re-recalculated on uniform fluence pCT scans using a 1% criterion. Passing rates were above 98.3% for all patients. Passing rates comparing FMpCT treatment doses to the ground truth treatment dose were above 88.5% for all patients. Evaluation of the proton range with a 1 mm criterion resulted in passing rates above 97.5% (FMpCT/pCT) and 95.3% (FMpCT/ground truth). Jointly optimized fluence-modulated pCT images can be used for proton dose calculation maintaining the full dosimetric accuracy of pCT but reducing the required imaging dose considerably by three quarters. This may allow for daily imaging during particle therapy ensuring a safe and accurate delivery of the therapeutic dose and avoiding excess dose from imaging.

The purpose of this work is to investigate the potentiality of using a limited number of in-room proton radiographies to compensate anatomical changes in adaptive proton therapy. The treatment planning CT is adapted to the treatment delivery scenario relying on 2D-3D deformable image registration (DIR). The proton radiographies, expressed in water equivalent thickness (WET) are simulated for both list-mode and integration-mode detector configurations in pencil beam scanning. Geometrical and analytical simulations of an anthropomorphic phantom in the presence of anatomical changes due to breathing are adopted. A Monte Carlo simulation of proton radiographies based on a clinical CT image in the presence of artificial anatomical changes is also considered. The accuracy of the 2D-3D DIR, calculated as root mean square error, strongly depends on the considered anatomical changes and is considered adequate for promising adaptive proton therapy when comparable to the accuracy of conventional 3D-3D DIR. In geometrical simulation, this is achieved with a minimum of eight/nine radiographies (more than 90% accuracy). Negligible improvement (sim1%) is obtained with the use of 180 radiographies. Comparing different detector configurations, superior accuracy is obtained with list-mode than integration-mode max (WET with maximum occurrence) and mean (average WET weighted by occurrences). Moreover, integration-mode max performs better than integration-mode mean. Results are minimally affected by proton statistics. In analytical simulation, the anatomical changes are approximately compensated (about 60%–70% accuracy) with two proton radiographies and minor improvement is observed with nine proton radiographies. In clinical data, two proton radiographies from list-mode have demonstrated better performance than nine from integration-mode (more than 100% and about 50%–70% accuracy, respectively), even avoiding the finer grid spacing of the last numerical optimization stage. In conclusion, the choice of detector configuration as well as the amount and complexity of the considered anatomical changes determine the minimum number of radiographies to be used.

PURPOSE Spatial distortions in magnetic resonance imaging (MRI) are mainly caused by inhomogeneities of the static magnetic field, nonlinearities in the applied gradients, and tissue-specific magnetic susceptibility variations. These factors may significantly alter the geometrical accuracy of the reconstructed MR image, thus questioning the reliability of MRI for guidance in image-guided radiation therapy. In this work, we quantified MRI spatial distortions and created a quantitative model where different sources of distortions can be separated. The generated model was then integrated into a four-dimensional (4D) computational phantom for simulation studies in MRI-guided radiation therapy at extra-cranial sites. METHODS A geometrical spatial distortion phantom was designed in four modules embedding laser-cut PMMA grids, providing 3520 landmarks in a field of view of (345 × 260 × 480) mm3 . The construction accuracy of the phantom was verified experimentally. Two fast MRI sequences for extra-cranial imaging at 1.5 T were investigated, considering axial slices acquired with online distortion correction, in order to mimic practical use in MRI-guided radiotherapy. Distortions were separated into their sources by acquisition of images with gradient polarity reversal and dedicated susceptibility calculations. Such a separation yielded a quantitative spatial distortion model to be used for MR imaging simulations. Finally, the obtained spatial distortion model was embedded into an anthropomorphic 4D computational phantom, providing registered virtual CT/MR images where spatial distortions in MRI acquisition can be simulated. RESULTS The manufacturing accuracy of the geometrical distortion phantom was quantified to be within 0.2 mm in the grid planes and 0.5 mm in depth, including thickness variations and bending effects of individual grids. Residual spatial distortions after MRI distortion correction were strongly influenced by the applied correction mode, with larger effects in the trans-axial direction. In the axial plane, gradient nonlinearities caused the main distortions, with values up to 3 mm in a 1.5 T magnet, whereas static field and susceptibility effects were below 1 mm. The integration in the 4D anthropomorphic computational phantom highlighted that deformations can be severe in the region of the thoracic diaphragm, especially when using axial imaging with 2D distortion correction. Adaptation of the phantom based on patient-specific measurements was also verified, aiming at increased realism in the simulation. CONCLUSIONS The implemented framework provides an integrated approach for MRI spatial distortion modeling, where different sources of distortion can be quantified in time-dependent geometries. The computational phantom represents a valuable platform to study motion management strategies in extra-cranial MRI-guided radiotherapy, where the effects of spatial distortions can be modeled on synthetic images in a virtual environment.

Real-time motion monitoring of lung tumors with low-field magnetic resonance imaging-guided linear accelerators (MR-Linacs) is currently limited to sagittal 2D cine magnetic resonance imaging (MRI). To provide input data for improved intrafractional and interfractional adaptive radiotherapy, the 4D anatomy has to be inferred from data with lower dimensionality. The purpose of this study was to experimentally validate a previously proposed propagation method that provides continuous time-resolved estimated 4D-MRI based on orthogonal cine MRI for a low-field MR-Linac. Ex vivo porcine lungs were injected with artificial nodules and mounted in a dedicated phantom that allows for the simulation of periodic and reproducible breathing motion. The phantom was scanned with a research version of a commercial 0.35 T MR-Linac. Respiratory-correlated 4D-MRI were reconstructed and served as ground truth images. Series of interleaved orthogonal slices in sagittal and coronal orientation, intersecting the injected targets, were acquired at 7.3 Hz. Estimated 4D-MRI at 3.65 Hz were created in post-processing using the propagation method and compared to the ground truth 4D-MRI. Eight datasets at different breathing frequencies and motion amplitudes were acquired for three porcine lungs. The overall median (95 t h percentile) deviation between ground truth and estimated deformation vector fields was 2.3 mm (5.7 mm), corresponding to 0.7 (1.6) times the in-plane imaging resolution (3.5 × 3.5 mm2). Median (95 t h percentile) estimated nodule position errors were 1.5 mm (3.8 mm) for nodules intersected by orthogonal slices and 2.1 mm (7.1 mm) for nodules located more than 2 cm away from either of the orthogonal slices. The estimation error depended on the breathing phase, the motion amplitude and the location of the estimated position with respect to the orthogonal slices. By using the propagation method, the 4D motion within the porcine lung phantom could be accurately and robustly estimated. The method could provide valuable information for treatment planning, real-time motion monitoring, treatment adaptation, and post-treatment evaluation of MR-guided radiotherapy treatments.

Prompt gamma (PG) imaging is widely investigated as one of the most promising methods for proton range verification in proton therapy. The performance of this technique is affected by several factors like tissue heterogeneity, number of protons in the considered pencil beam and the detection device. Our previous work proposed a new treatment planning concept which boosts the number of protons of a few PG monitoring-friendly pencil beams (PBs), selected on the basis of two proposed indicators quantifying the conformity between the dose and PG at the emission level, above the desired detectability threshold. To further explore this method at the detection level, in this work we investigated the response of a knife-edge slit PG camera which was deployed in the first clinical application of PG to proton therapy monitoring. The REGistration Graphical User Interface (REGGUI) is employed to simulate the PG emission, PG detection as well as the corresponding dose distribution. As the PG signal detected by this kind of PG camera is sensitive to the relative position of the camera and PG signal falloff, we optimized our PB selection method for this camera by introducing a new camera position indicator identifying whether the expected falloff of the PG signal is centered in the field of view of the camera or not. Our camera-adapted PB selection method is investigated using computed tomography (CT) scans at two different treatment time points of a head and neck, and a prostate cancer patient under scenarios considering different statistics level. The results show that a precision of 0.8 mm for PG falloff identification can be achieved when a PB has more than 2 × 108 primary protons. Except for one case due to unpredictable and comparably large anatomical changes, the PG signals of most of the PBs recommended by all our indicators are observed to be reliable for proton range verification with deviations between the inter-fractional shift of proton range (as deduced from the PB dose distribution) and the detected PG signal within 2.0 mm. In contrast, a shift difference up to 9.6 mm has been observed for the rejected PBs. The magnitude of the proton range shift due to the inter-fractional anatomical changes is observed to be up to 23 mm. The proposed indicators are shown to be valuable for identifying and recommending reliable PBs to create new PG monitoring-friendly TPs. Comparison between our PB boosting method and the alternative PB aggregation, which combines the signal of nearby PBs to reach the desired counting statistics, is also discussed.

Purpose Ventilation-induced tumour motion remains a challenge for the accuracy of proton therapy treatments in lung patients. We investigated the feasibility of using a 4D virtual CT (4D-vCT) approach based on deformable image registration (DIR) and motion-aware 4D CBCT reconstruction (MA-ROOSTER) to enable accurate daily proton dose calculation using a gantry-mounted CBCT scanner tailored to proton therapy. Methods Ventilation correlated data of 10 breathing phases were acquired from a porcine ex-vivo functional lung phantom using CT and CBCT. 4D-vCTs were generated by (1) DIR of the mid-position 4D-CT to the mid-position 4D-CBCT (reconstructed with the MA-ROOSTER) using a diffeomorphic Morphons algorithm and (2) subsequent propagation of the obtained mid-position vCT to the individual 4D-CBCT phases. Proton therapy treatment planning was performed to evaluate dose calculation accuracy of the 4D-vCTs. A robust treatment plan delivering a nominal dose of 60 Gy was generated on the average intensity image of the 4D-CT for an approximated internal target volume (ITV). Dose distributions were then recalculated on individual phases of the 4D-CT and the 4D-vCT based on the optimized plan. Dose accumulation was performed for 4D-vCT and 4D-CT using DIR of each phase to the mid position, which was chosen as reference. Dose based on the 4D-vCT was then evaluated against the dose calculated on 4D-CT both, phase-by-phase as well as accumulated, by comparing dose volume histogram (DVH) values (Dmean, D2%, D98%, D95%) for the ITV, and by a 3D-gamma index analysis (global, 3%/3 mm, 5 Gy, 20 Gy and 30 Gy dose thresholds). Results Good agreement was found between the 4D-CT and 4D-vCT-based ITV-DVH curves. The relative differences ((CT-vCT)/CT) between accumulated values of ITV Dmean, D2%, D95% and D98% for the 4D-CT and 4D-vCT-based dose distributions were −0.2%, 0.0%, −0.1% and −0.1%, respectively. Phase specific values varied between −0.5% and 0.2%, −0.2% and 0.5%, −3.5% and 1.5%, and −5.7% and 2.3%. The relative difference of accumulated Dmean over the lungs was 2.3% and Dmean for the phases varied between −5.4% and 5.8%. The gamma pass-rates with 5 Gy, 20 Gy and 30 Gy thresholds for the accumulated doses were 96.7%, 99.6% and 99.9%, respectively. Phase-by-phase comparison yielded pass-rates between 86% and 97%, 88% and 98%, and 94% and 100%. Conclusions Feasibility of the suggested 4D-vCT workflow using proton therapy specific imaging equipment was shown. Results indicate the potential of the method to be applied for daily 4D proton dose estimation.

The empirical conversion of the treatment planning x-ray computed tomography (CT) image to ion stopping power relative to water causes dose calculation inaccuracies in ion beam therapy. A patient-specific calibration of the CT image is enabled by the combination of an ion radiography (iRad) with the forward-projection of the empirically converted CT image along the estimated ion trajectories. This work investigated the patient-specific CT calibration for list-mode and integration-mode detector configurations, with reference to a ground truth ion CT (iCT) image. Analytical simulations of idealized carbon ion and proton trajectories in a numerical anthropomorphic phantom and realistic Monte Carlo simulations of proton, helium and carbon ion pencil beam scanning in a clinical CT image of a head-and-neck patient were considered. Controlled inaccuracy and noise levels were applied to the calibration curve and to the iRad, respectively. The impact of the selection of slices and angles of the iRads, as well as the choice of the optimization algorithm, were investigated. Accurate and robust CT calibration was obtained in analytical simulations of straight carbon ion trajectories. Analytical simulations of non-straight proton trajectories due to scattering suggested limitations for integration-mode but not for list-mode. To make the most of integration-mode, a dedicated objective function was proposed, demonstrating the desired accuracy for sufficiently high proton statistics in analytical simulations. In clinical data the inconsistencies between the iRad and the forward-projection of the ground truth iCT image were in the same order of magnitude as the applied inaccuracies (up to 5%). The accuracy of the CT calibration were within 2%-5% for integration-mode and 1%-3% for list-mode. The feasibility of successful patient-specific CT calibration depends on detector technologies and is primarily limited by these above mentioned inconsistencies that slightly penalize protons over helium and carbon ions due to larger scattering and beam spot size.

In a radiation therapy workflow based on Magnetic Resonance Imaging (MRI), dosimetric errors may arise due to geometric distortions introduced by MRI. The aim of this study was to quantify the dosimetric effect of system-dependent geometric distortions in an MRI-only workflow for proton therapy applied at extra-cranial sites. An approach was developed, in which computed tomography (CT) images were distorted using an MRI displacement map, which represented the MR distortions in a spoiled gradient-echo sequence due to gradient nonlinearities and static magnetic field inhomogeneities. A retrospective study was conducted on 4DCT/MRI digital phantoms and 18 4DCT clinical datasets of the thoraco-abdominal site. The treatment plans were designed and separately optimized for each beam in a beam specific Planning Target Volume on the distorted CT, and the final dose distribution was obtained as the average. The dose was then recalculated in undistorted CT using the same beam geometry and beam weights. The analysis was performed in terms of Dose Volume Histogram (DVH) parameters. No clinically relevant dosimetric impact was observed on organs at risk, whereas in the target structure, geometric distortions caused statistically significant variations in the planned dose DVH parameters and dose homogeneity index (DHI). The dosimetric variations in the target structure were smaller in abdominal cases (ΔD2%, ΔD98%, and ΔDmean all below 0.1% and ΔDHI below 0.003) compared to the lung cases. Indeed, lung patients with tumors isolated inside lung parenchyma exhibited higher dosimetric variations (ΔD2% ≥ 0.3%, ΔD98% ≥ 15.9%, ΔDmean ≥ 3.3% and ΔDHI ≥ 0.102) than lung patients with tumor close to soft tissue (ΔD2% ≤ 0.4%, ΔD98% ≤ 5.6%, ΔDmean ≤ 0.9% and ΔDHI ≤ 0.027) potentially due to higher density variations along the beam path. Results suggest the potential applicability of MRI-only proton therapy, provided that specific analysis is applied for isolated lung tumors.

The integration of magnetic resonance imaging (MRI) for guidance in external beam radiotherapy has faced significant research and development efforts in recent years. The current availability of linear accelerators with an embedded MRI unit, providing volumetric imaging at excellent soft tissue contrast, is expected to provide novel possibilities in the implementation of image-guided adaptive radiotherapy (IGART) protocols. This study reviews open medical physics issues in MR-guided radiotherapy (MRgRT) implementation, with a focus on current approaches and on the potential for innovation in IGART. Daily imaging in MRgRT provides the ability to visualize the static anatomy, to capture internal tumor motion and to extract quantitative image features for treatment verification and monitoring. Those capabilities enable the use of treatment adaptation, with potential benefits in terms of personalized medicine. The use of online MRI requires dedicated efforts to perform accurate dose measurements and calculations, due to the presence of magnetic fields. Likewise, MRgRT requires dedicated quality assurance (QA) protocols for safe clinical implementation. Reaction to anatomical changes in MRgRT, as visualized on daily images, demands for treatment adaptation concepts, with stringent requirements in terms of fast and accurate validation before the treatment fraction can be delivered. This entails specific challenges in terms of treatment workflow optimization, QA, and verification of the expected delivered dose while the patient is in treatment position. Those challenges require specialized medical physics developments towards the aim of fully exploiting MRI capabilities. Conversely, the use of MRgRT allows for higher confidence in tumor targeting and organs-at-risk (OAR) sparing. The systematic use of MRgRT brings the possibility of leveraging IGART methods for the optimization of tumor targeting and quantitative treatment verification. Although several challenges exist, the intrinsic benefits of MRgRT will provide a deeper understanding of dose delivery effects on an individual basis, with the potential for further treatment personalization.

Die Arbeit gliedert sich in insgesamt vier Kapitel. Aus methodischer Perspektive wird kapitelubergreifend ein Schwerpunkt auf die praktische Rechtsanwendung und die Erprobung aller im Verlaufe der Arbeit aufgeworfenen Fragen und moglichen Antworten hierzu in der konkreten Subsumtion anhand von Beispielsfallen gelegt.

Im abschliesenden Kapitel gilt es, die Befunde der vorherigen drei Kapitel darzustellen und zu analysieren. Hierfur sollen die zentralen Ergebnisse der Arbeit in Thesenform gruppiert werden. Dabei werden mogliche Grunde und Grenzen von Nebenpflichten, die sich im Verlaufe der Untersuchung als sinnvoll herauskristallisiert haben, im Fettdruck gekennzeichnet.

PURPOSE To model four-dimensional (4D) relative biological effectiveness (RBE)-weighted dose variations in abdominal lesions treated with scanned carbon ion beam in case of irregular breathing motion. METHODS The proposed method, referred to as bioWED method, combines the simulation of tumor motion in a patient- and beam-specific water equivalent depth (WED)-space with RBE modeling, aiming at the estimation of RBE-weighted dose changes due to respiratory motion. The method was validated on a phantom, simulating gated and free breathing dose delivery, and on a patient case, for which free breathing irradiation was assumed and both amplitude and baseline breathing irregularities were simulated through a respiratory motion model. We quantified (a) the effect of motion on the equivalent uniform dose (EUD) and the RBE-weighted dose-volume histograms (DVH), by comparing the planned dose distribution with "ground truth" 4D RBE-weighted doses computed using 4D computed tomography data, and (ii) the estimation error, by comparing the doses estimated with the bioWED method to "ground truth" 4D RBE-weighted doses. RESULTS In the phantom validation, the estimation error on the EUD was limited with respect to the motion effect and the median estimation error on relevant RBE-weighted DVH metrics remained within 5%. In the patient study, the estimation error as computed on the EUD was smaller than the corresponding motion effect, exhibiting the largest values in the baseline irregularity simulation. However, the median estimation error over all simulations was below 3.2% considering relevant DVH metrics. CONCLUSIONS In the evaluated cases, the bioWED method showed proper accuracy when compared to deformable image registration-based 4D dose calculation. Therefore, it can be seen as a tool to test treatment plan robustness against irregular breathing motion, although its accuracy decreases as a function of increasing soft tissue deformation and should be evaluated on a larger patient dataset.

Warum ist die Begrundung schuldverhaltnisbedingter Nebenpflichten uberhaupt erforderlich? Grundsatzlich gilt rechtssystemubergreifend im Privatrecht ‒ zuruckgehend auf das romische Recht – die Sentenz casum sentit dominus. Damit ist gemeint, dass jeder die Schaden an seinen Rechtsgutern selbst zu tragen hat. Der Gedanke der Haftung fur fremde Rechtsgutsbeeintrachtigungen wird hierbei zunachst vollig ausgeklammert.

Nachdem herausgearbeitet wurde, dass Nebenpflichten bei teleologischer Auslegung des § 241 II BGB aus rechtspraktischer Perspektive Haftungslucken im Deliktsrecht fullen sollen, kann der Zweck des § 241 II BGB nun weiter untersucht werden. Erstere Erkenntnis hilft schlieslich bei der Subsumtion in dem Moment nicht mehr weiter, in dem eine Haftungslucke im Deliktsrecht festgestellt wurde. Die Entscheidung, ob diese Lucke dann durch eine Nebenpflicht – und eine korrespondierende Haftung aus §§ 280 I, 241 II BGB – geschlossen werden soll oder ganzlich offen bleibt, muss sich letztlich ebenfalls aus dem Zweck von § 241 II BGB heraus erklaren lassen.

Die grosere Haftungsstrenge des Schuldverhaltnisrechts gegenuber dem Deliktsrecht wird haufig ohne weitere Begrundung vorausgesetzt. Diese Aussage naher zu untersuchen, ist zum einen von Interesse, weil sie – unter Ruckgriff auf das RG – immer noch als Grund fur eine schuldverhaltnisbedingte Haftung aus Nebenpflichtverletzung angefuhrt wird. Zum anderen lasst sich nur dann sinnvoll uberprufen, wann eine Haftung aus Schuldverhaltnis gerechtfertigt ist, wenn zunachst die Folgen dieser Haftung bestimmt und mit den universal anwendbaren Haftungsregimen verglichen sind.

The aim of this work is to investigate in-room proton radiographies to compensate realistic rigid and non-rigid transformations in clinical-like scenarios based on 2D–3D deformable image registration (DIR) framework towards future clinical implementation of adaptive radiation therapy (ART). Monte Carlo simulations of proton radiographies (pRads) based on clinical x-ray CT of a head and neck, and a brain tumor patients are simulated for two different detector configurations (i.e. integration-mode and list-mode detectors) including high and low proton statistics. A realistic deformation, derived from cone beam CT of the patient, is applied to the treatment planning CT. Rigid inaccuracies in patient positioning are also applied and the effect of small, medium and large fields of view (FOVs) is investigated. A stopping criterion, as desirable in realistic scenarios devoid of ground truth proton CT (pCT), is proposed and investigated. Results show that rigid and non-rigid transformations can be compensated based on a limited number of low dose pRads. The root mean square error with respect to the pCT shows that the 2D–3D DIR of the treatment planning CT based on 10 pRads from integration-mode data and 2 pRads from list-mode data is capable of achieving comparable accuracy (∼90% and >90%, respectively) to conventional 3D–3D DIR. The dice similarity coefficient over the segmented regions of interest also verifies the improvement in accuracy prior to and after 2D–3D DIR. No relevant changes in accuracy are found between high and low proton statistics except for 2 pRads from integration-mode data. The impact of FOV size is negligible. The convergence of the metric adopted for the stopping criterion indicates the optimal convergence of the 2D–3D DIR. This work represents a further step towards the potential implementation of ART in proton therapy. Further computational optimization is however required to enable extensive clinical validation.

Purpose: To demonstrate a proof-of-concept for fast cone-beam CT (CBCT) intensity correction in projection space by the use of deep learning. Methods: The CBCT scans and corresponding projections were acquired from 30 prostate cancer patients. Reference shading correction was performed using a validated method (CBCT cor ), which estimates scatter and other low-frequency deviations in the measured CBCT projections on the basis of a prior CT image obtained from warping the planning CT to the CBCT. A convolutional neural network (ScatterNet) was designed, consisting of an attenuation conversion stage followed by a shading correction stage using a UNet-like architecture. The combined network was trained in 2D, utilizing pairs of measured and corrected projections of the reference method, in order to perform shading correction in projection space before reconstruction. The number of patients used for training, testing, and evaluation was 15, 7, and 8, respectively. The reconstructed CBCT ScatterNet was compared to CBCT cor in terms of mean and absolute errors (ME and MAE) for the eight evaluation patients (not included in the network training). Volumetric modulated arc photon therapy (VMAT) and intensity-modulated proton therapy (IMPT) plans were generated on CBCT cor . Dose was recalculated on CBCT cor to evaluate its dosimetric accuracy. Single-field uniform dose proton plans were utilized for proton range comparison of CBCT ScatterNet and CBCT cor . Results: The CBCT ScatterNet showed no cupping artifacts and a considerably smaller MAE and ME with respect to CBCT cor than the uncorrected CBCT (on average 144 Hounsfield units (HU) vs 46 HU for MAE and 138 HU vs −3 HU for ME). The pass-rates using a 2% dose-difference criterion at 50% dose cut-off, were close to 100% for the VMAT plans of all patients when comparing CBCT ScatterNet to CBCT cor . For IMPT plans pass-rates were clearly lower, ranging from 15% to 81%. Proton range differences of up to 5 mm occurred. Conclusions: Using a deep convolutional neural network for CBCT intensity correction was shown to be feasible in the pelvic region for the first time. Dose calculation accuracy on CBCT ScatterNet was high for VMAT, but unsatisfactory for IMPT. With respect to the reference technique (CBCT cor ), the neural network enabled a considerable increase in speed for intensity correction and might eventually allow for on-the-fly shading correction during CBCT acquisition.

Purpose Variance-based sensitivity analysis (SA) is described and applied to the radiation dosimetry model proposed by the Committee on Medical Internal Radiation Dose (MIRD) for the organ-level absorbed dose calculations in nuclear medicine. The uncertainties in the dose coefficients thus calculated are also evaluated. Methods A Monte Carlo approach was used to compute first-order and total-effect SA indices, which rank the input factors according to their influence on the uncertainty in the output organ doses. These methods were applied to the radiopharmaceutical (S)-4-(3-18 F-fluoropropyl)-L-glutamic acid (18 F-FSPG) as an example. Since 18 F-FSPG has 11 notable source regions, a 22-dimensional model was considered here, where 11 input factors are the time-integrated activity coefficients (TIACs) in the source regions and 11 input factors correspond to the sets of the specific absorbed fractions (SAFs) employed in the dose calculation. The SA was restricted to the foregoing 22 input factors. The distributions of the input factors were built based on TIACs of five individuals to whom the radiopharmaceutical 18 F-FSPG was administered and six anatomical models, representing two reference, two overweight, and two slim individuals. The self-absorption SAFs were mass-scaled to correspond to the reference organ masses. Results The estimated relative uncertainties were in the range 10%-30%, with a minimum and a maximum for absorbed dose coefficients for urinary bladder wall and heart wall, respectively. The applied global variance-based SA enabled us to identify the input factors that have the highest influence on the uncertainty in the organ doses. With the applied mass-scaling of the self-absorption SAFs, these factors included the TIACs for absorbed dose coefficients in the source regions and the SAFs from blood as source region for absorbed dose coefficients in highly vascularized target regions. For some combinations of proximal target and source regions, the corresponding cross-fire SAFs were found to have an impact. Conclusion Global variance-based SA has been for the first time applied to the MIRD schema for internal dose calculation. Our findings suggest that uncertainties in computed organ doses can be substantially reduced by performing an accurate determination of TIACs in the source regions, accompanied by the estimation of individual source region masses along with the usage of an appropriate blood distribution in a patient's body and, in a few cases, the cross-fire SAFs from proximal source regions.

INTRODUCTION Treatment planning for ion therapy must account for physical properties of the beam as well as differences in the relative biological effectiveness (RBE) of ions compared to photons. In this work, we present a fast RBE calculation approach, based on the decoupling of physical properties and the [Formula: see text] ratio commonly used to describe the radiosensitivity of irradiated cells or organs. MATERIAL AND METHODS In the framework of the mechanistic repair-misrepair-fixation (RMF) model, the biological modeling can be decoupled from the physical dose. This was implemented into a research treatment planning system for carbon ion therapy. RESULTS The presented implementation of the RMF model is very fast, allowing online changes of [Formula: see text]. For example, a change of [Formula: see text] including a complete biological modeling and a recalculation of RBE for [Formula: see text] voxel takes 4 ms on a 4 CPU, 3.2 GHz workstation. DISCUSSION AND CONCLUSION The derived decoupling within the RMF model allows fast changes in [Formula: see text], facilitating online adaption by the user. This provides new options for radiation oncologists, facilitating online variations of the radiobiological input parameters during the treatment plan evaluation process as well as uncertainty and sensitivity analyses.

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.