Search

PURPOSE: To ensure target coverage in the treatment of esophageal cancer, a density override to the region of diaphragm motion can be applied in the optimization process. Here, we evaluate the benefit of this approach during robust optimization for intensity modulated proton therapy (IMPT) planning.MATERIALS AND METHODS: For ten esophageal cancer patients, two robustly optimized IMPT plans were created either using (WDO) or not using (NDO) a diaphragm density override of 1.05 g/cm3 during plan optimization. The override was applied to the excursion of the diaphragm between exhale and inhale. Initial robustness evaluation was performed for plan acceptance (setup errors of 8 mm, range errors of ±3%), and subsequently, on all weekly repeated 4DCTs (setup errors of 2 mm, range errors of ±3%). Target coverage and hotspots were analyzed on the resulting voxel-wise minimum (Vwmin ) and voxel-wise maximum (Vwmax ) dose distributions.RESULTS: The nominal dose distributions were similar for both WDO and NDO plans. However, visual inspection of the Vwmax of the WDO plans showed hotspots behind the right diaphragm override region. For one patient, target coverage and hotspots improved by applying the diaphragm override. We found no differences in target coverage in the weekly evaluations between the two approaches.CONCLUSION: The diaphragm override approach did not result in a clinical benefit in terms of planning and interfractional robustness. Therefore, we don't see added value in employing this approach as a default option during robust optimization for IMPT planning in esophageal cancer.

PURPOSE: The capability of proton therapy to provide highly conformal dose distributions is impaired by range uncertainties. The aim of this work is to apply range probing (RP), a form of a proton radiography-based quality control (QC) procedure for range accuracy assessment in head and neck cancer (HNC) patients in a clinical setting.METHODS AND MATERIALS: This study included seven HNC patients. RP acquisition was performed using a multi-layer ionization chamber (MLIC). Per patient, two RP frames were acquired within the first two weeks of treatment, on days when a repeated CT scan was obtained. Per RP frame, integral depth dose (IDD) curves of 81 spots around the treatment isocentre were acquired. Range errors are determined as a discrepancy between calculated IDDs in the treatment planning system and measured residual ranges by the MLIC. Range errors are presented relative to the water equivalent path length of individual proton spots. In addition to reporting results for complete measurement frames, an analysis, excluding range error contributions due to anatomical changes, is presented.RESULTS: Discrepancies between measured and calculated ranges are smaller when performing RP calculations on the day-specific patient anatomy rather than the planning CT. The patient-specific range evaluation shows an agreement between calculated and measured ranges for spots in anatomically consistent areas within 3% (1.5 standard deviation).CONCLUSIONS: The results of a RP-based QC procedure implemented in the clinical practice for HNC patients have been demonstrated. The agreement of measured and simulated proton ranges confirms the 3% uncertainty margin for robust optimization. Anatomical variations show a predominant effect on range accuracy, motivating efforts towards the implementation of adaptive radiotherapy.

PURPOSE: Cone beam CT (CBCT) based synthetic CTs (sCT) produced with a deep convolutional neural network (DCNN) show high image quality, suggesting their potential usability in adaptive proton therapy workflows. However, the nature of such workflows involving DCNNs prevents the user from having direct control over their output. Therefore, quality control (QC) tools that monitor the sCTs and detect failures or outliers in the generated images are needed. This work evaluates the potential of using a range probing (RP) based QC tool to verify sCTs generated by a DCNN. Such a RP QC tool experimentally assesses the CT number accuracy in sCTs.METHODS: A RP QC dataset consisting of repeat CTs (rCT), CBCTs and RP acquisitions of 7 head and neck cancer patients was retrospectively assessed. CBCT based sCTs were generated using a DCNN. The CT number accuracy in the sCTs was evaluated by computing relative range errors between measured RP fields and RP field simulations based on rCT and sCT images.RESULTS: Mean relative range errors showed agreement between measured and simulated RP fields, ranging from -1.2% to 1.5% in rCTs, and from -0.7% to 2.7% in sCTs.CONCLUSIONS: The agreement between measured and simulated RP fields suggests the suitability of sCTs for proton dose calculations. This outcome brings sCTs generated by DCNNs closer towards clinical implementation within adaptive proton therapy treatment workflows. The proposed RP QC tool allows for CT number accuracy assessment in sCTs and can provide means of in vivo range verification.

In this review article, we introduced the importance of “motion management” in advanced particle therapy. Several publications have reported that organ motion causes dose distribution disturbances due to interplay and blurring effects. Furthermore, motion can result in target dose miss and unwanted dose to healthy structures around the target. To avoid these problems, motion should be assessed and monitored prior and during treatment. In this review article, we give an overview about clinically available motion monitoring systems. Based on the acquired motion information an adequate motion mitigation technique should be chosen. This article reviews the clinical status of motion mitigation techniques like rescanning, gating and tracking. A limited number of centers have now started the treatment of targets in the thorax and abdomen using scanned particle beams. Therefore, the establishment of guidelines for motion monitoring and motion mitigation will be essential in the coming years.

Background and purpose Motion-induced uncertainties hamper the clinical implementation of pencil beam scanning proton therapy (PBS-PT). Prospective pretreatment evaluations only provide multiscenario predictions without giving a clear conclusion for the actual treatment. Therefore, in this proof-of-concept study we present a methodology for a fraction-wise retrospective four-dimensional (4D) dose reconstruction and accumulation aiming at the evaluation of treatment quality during and after treatment. Material and methods We implemented an easy-to-use, script-based 4D dose assessment of PBS-PT for patients with moving tumors in a commercially available treatment planning system. This 4D dose accumulation uses treatment delivery log files and breathing pattern records of each fraction as well as weekly repeated 4D-CT scans acquired during the treatment course. The approach was validated experimentally and was executed for an exemplary dataset of a lung cancer patient. Results The script-based 4D dose reconstruction and accumulation was implemented successfully, requiring minimal user input and a reasonable processing time (around 10 min for a fraction dose assessment). An experimental validation using a dynamic CIRS thorax phantom confirmed the precision of the 4D dose reconstruction methodology. In a proof-of-concept study, the accumulation of 33 reconstructed fraction doses showed a linear increase of D98 values. Projected treatment course D98 values revealed a CTV underdosage after fraction 25. This loss of target coverage was confirmed in a dose volume histogram comparison of the nominal, the projected (after 16 fractions) and the accumulated (after 33 fractions) dose distribution. Conclusions The presented method allows for the assessment of the conformity between planned and delivered dose as the treatment course progresses. The implemented approach considers the influence of changing patient anatomy and variations in the breathing pattern. This facilitates treatment quality evaluation and supports decisions regarding plan adaptation. In a next step, this approach will be applied to a larger patient cohort to investigate its capability as 4D quality control and decision support tool for treatment adaptation. (c) 2019 American Association of Physicists in Medicine

Purpose: Prospective respiratory-gated 4D CT has been shown to reduce tumor image artifacts by up to 50% compared to conventional 4D CT. However, to date no studies have quantified the impact of gated 4D CT on normal lung tissue imaging, which is important in performing dose calculations based on accurate estimates of lung volume and structure. To determine the impact of gated 4D CT on thoracic image quality, the authors developed a novel simulation framework incorporating a realistic deformable digital phantom driven by patient tumor motion patterns. Based on this framework, the authors test the hypothesis that respiratory-gated 4D CT can significantly reduce lung imaging artifacts. Methods: Our simulation framework synchronizes the 4D extended cardiac torso (XCAT) phantom with tumor motion data in a quasi real-time fashion, allowing simulation of three 4D CT acquisition modes featuring different levels of respiratory feedback: (i) “conventional” 4D CT that uses a constant imaging and couch-shift frequency, (ii) “beam paused” 4D CT that interrupts imaging to avoid oversampling at a given couch position and respiratory phase, and (iii) “respiratory-gated” 4D CT that triggers acquisition only when the respiratory motion fulfills phase-specific displacement gating windows based on prescan breathing data. Our framework generates a setmore » of ground truth comparators, representing the average XCAT anatomy during beam-on for each of ten respiratory phase bins. Based on this framework, the authors simulated conventional, beam-paused, and respiratory-gated 4D CT images using tumor motion patterns from seven lung cancer patients across 13 treatment fractions, with a simulated 5.5 cm{sup 3} spherical lesion. Normal lung tissue image quality was quantified by comparing simulated and ground truth images in terms of overall mean square error (MSE) intensity difference, threshold-based lung volume error, and fractional false positive/false negative rates. Results: Averaged across all simulations and phase bins, respiratory-gating reduced overall thoracic MSE by 46% compared to conventional 4D CT (p ∼ 10{sup −19}). Gating leads to small but significant (p < 0.02) reductions in lung volume errors (1.8%–1.4%), false positives (4.0%–2.6%), and false negatives (2.7%–1.3%). These percentage reductions correspond to gating reducing image artifacts by 24–90 cm{sup 3} of lung tissue. Similar to earlier studies, gating reduced patient image dose by up to 22%, but with scan time increased by up to 135%. Beam paused 4D CT did not significantly impact normal lung tissue image quality, but did yield similar dose reductions as for respiratory-gating, without the added cost in scanning time. Conclusions: For a typical 6 L lung, respiratory-gated 4D CT can reduce image artifacts affecting up to 90 cm{sup 3} of normal lung tissue compared to conventional acquisition. This image improvement could have important implications for dose calculations based on 4D CT. Where image quality is less critical, beam paused 4D CT is a simple strategy to reduce imaging dose without sacrificing acquisition time.« less

This article reports on a 4D-treatment planning workshop (4DTPW), held on 7–8 December 2009 at the Paul Scherrer Institut (PSI) in Villigen, Switzerland. The participants were all members of institutions actively involved in particle therapy delivery and research. The purpose of the 4DTPW was to discuss current approaches, challenges, and future research directions in 4D-treatment planning in the context of actively scanned particle radiotherapy. Key aspects were addressed in plenary sessions, in which leaders of the field summarized the state-of-the-art. Each plenary session was followed by an extensive discussion. As a result, this article presents a summary of recommendations for the treatment of mobile targets (intrafractional changes) with actively scanned particles and a list of requirements to elaborate and apply these guidelines clinically.

Target sites affected by organ motion require a time resolved (4D) dose calculation. Typical 4D dose calculations use 4D-CT as a basis. Unfortunately, 4D-CT images have the disadvantage of being a "snap-shot" of the motion during acquisition and of assuming regularity of breathing. In addition, 4D-CT acquisitions involve a substantial additional dose burden to the patient making many, repeated 4D-CT acquisitions undesirable. Here the authors test the feasibility of an alternative approach to generate patient specific 4D-CT data sets.In this approach motion information is extracted from 4D-MRI. Simulated 4D-CT data sets [which the authors call 4D-CT(MRI)] are created by warping extracted deformation fields to a static 3D-CT data set. The employment of 4D-MRI sequences for this has the advantage that no assumptions on breathing regularity are made, irregularities in breathing can be studied and, if necessary, many repeat imaging studies (and consequently simulated 4D-CT data sets) can be performed on patients and/or volunteers. The accuracy of 4D-CT(MRI)s has been validated by 4D proton dose calculations. Our 4D dose algorithm takes into account displacements as well as deformations on the originating 4D-CT/4D-CT(MRI) by calculating the dose of each pencil beam based on an individual time stamp of when that pencil beam is applied. According to corresponding displacement and density-variation-maps the position and the water equivalent range of the dose grid points is adjusted at each time instance.4D dose distributions, using 4D-CT(MRI) data sets as input were compared to results based on a reference conventional 4D-CT data set capturing similar motion characteristics. Almost identical 4D dose distributions could be achieved, even though scanned proton beams are very sensitive to small differences in the patient geometry. In addition, 4D dose calculations have been performed on the same patient, but using 4D-CT(MRI) data sets based on variable breathing patterns to show the effect of possible irregular breathing on active scanned proton therapy. Using a 4D-CT(MRI), including motion irregularities, resulted in significantly different proton dose distributions.The authors have demonstrated that motion information from 4D-MRI can be used to generate realistic 4D-CT data sets on the basis of a single static 3D-CT data set. 4D-CT(MRI) presents a novel approach to test the robustness of treatment plans in the circumstance of patient motion.

Purpose:Protonradiotherapy activates positron emitters in tissue, which can be imaged with PET. However, the resulting PET image is not directly proportional to the delivered dose. We are investigating the spatial relationship between the dose distribution and its PET image without reverting to Monte Carlo methods. The first goal is to validate the proton range in the patient, and ultimately to reconstruct the spatial distribution of the actually delivered dose from its PET image.Method and Materials: The relationship between the protondose and its PET image can be described mathematically as a convolution (filtering). We derive the convolution kernel analytically. This filter is unique for a given activation channel, independent of beam energy and specific absorber. The straightforward application of the method to determine the PET signal by locally filtering the planned dose distribution was validated through comparisons with Monte Carlo calculations and measured PET data in homogeneous and inhomogeneous media. The challenging inversion of the relationship, determining the dose from the PET signal, was initially explored for a simplified mono‐energetic case in a homogeneous absorber. Results: Activity depth profiles obtained with the convolution approach agreed with measured and Monte Carlo data within 1 mm in depth. In terms of absolute intensity, the agreement was within 1.5% between filtered and simulated profiles and 10% between filtered and measured data in the distal region. Attempts to recover the dose distribution from its PET image through a de‐convolution yielded promising results for idealized data but were strongly noise dependent. Conclusion: We have derived the spatial relationship between dose and positron activation and demonstrated the possibility to obtain the PET image measured after proton treatment by locally filtering the planned dose distribution. The inverse approach, i.e., direct dose quantification from the measured PET, seems possible but is very sensitive to noise.

Purpose: Target sites affected by organ motion require a time resolved (4D) dose calculation. Typical 4D dose calculations use 4D-CT as a basis. Unfortunately, 4D-CT images have the disadvantage of being a 'snap-shot' of the motion during acquisition and of assuming regularity of breathing. In addition, 4D-CT acquisitions involve a substantial additional dose burden to the patient making many, repeated 4D-CT acquisitions undesirable. Here the authors test the feasibility of an alternative approach to generate patient specific 4D-CT data sets. Methods: In this approach motion information is extracted from 4D-MRI. Simulated 4D-CT data sets [which the authors call 4D-CT(MRI)] are created by warping extracted deformation fields to a static 3D-CT data set. The employment of 4D-MRI sequences for this has the advantage that no assumptions on breathing regularity are made, irregularities in breathing can be studied and, if necessary, many repeat imaging studies (and consequently simulated 4D-CT data sets) can be performed on patients and/or volunteers. The accuracy of 4D-CT(MRI)s has been validated by 4D proton dose calculations. Our 4D dose algorithm takes into account displacements as well as deformations on the originating 4D-CT/4D-CT(MRI) by calculating the dose of each pencil beam based on an individual time stamp of whenmore » that pencil beam is applied. According to corresponding displacement and density-variation-maps the position and the water equivalent range of the dose grid points is adjusted at each time instance. Results: 4D dose distributions, using 4D-CT(MRI) data sets as input were compared to results based on a reference conventional 4D-CT data set capturing similar motion characteristics. Almost identical 4D dose distributions could be achieved, even though scanned proton beams are very sensitive to small differences in the patient geometry. In addition, 4D dose calculations have been performed on the same patient, but using 4D-CT(MRI) data sets based on variable breathing patterns to show the effect of possible irregular breathing on active scanned proton therapy. Using a 4D-CT(MRI), including motion irregularities, resulted in significantly different proton dose distributions. Conclusions: The authors have demonstrated that motion information from 4D-MRI can be used to generate realistic 4D-CT data sets on the basis of a single static 3D-CT data set. 4D-CT(MRI) presents a novel approach to test the robustness of treatment plans in the circumstance of patient motion.« less

Purpose: At present a very promising method for an in vivo and non‐invasive monitoring of radiation treatments with protonbeams is positron emission tomography(PET). Offline (post treatment) PET/CT imaging is currently under investigation for proton range verification. However, the long (∼15min) time interval between irradiation and PETimaging and the fact that the patients need to move to a different room for the PET scan introduces various uncertainties. This study investigates the feasibility and the potential advantages of using a mobile NeuroPET scanner in the treatment room compared to the offline PET/CT scanner.Method and Materials: A circular SOBP proton field of 16 cm water equivalent range and 10 cm modulation was delivered to a PMMA phantom (9 × 9 × 20 cm3). A total dose of 8 GyE(RBE=1.1) was administered. PET data were acquired in listmode starting within 7 min after irradiation once at the NeuroPET scanner and once at a commercial PET/CT scanner. For image coregistration markers that are recognizable in the CT as well as in the PET were used. Results: The 50% distal fall‐off position along the activity depth profiles of the two scanners were found to agree within 2.5 mm. The NeuroPET scanner showed comparable performance over the scanning period of 30 minutes than the conventional PET/CT scanner.Conclusion: The promising results of the phantom study encourage first patient NeuroPET scans following protontreatment. The use of the movable NeuroPET scanner in the treatment room promises to enable high precision PET range verification with minimized disturbing influences of biological washout and reduced geometrical uncertainties due to patient repositioning.

Purpose: A non‐invasive method for verification of treatment delivery to ensure the high quality of proton therapy is offered by Positron Emission Tomography(PET), which takes advantage of the β+‐activation produced via nuclear reactions between the protons and the nuclei of the tissue during irradiation,. Since dose distributions and measured PETimages are correlated but not identical, a procedure to provide clinical feedback on the correct dose delivery and irradiation field position is necessary. This study aims to validate the measured activity patterns by means of activity distributions calculated using a novel and fast one‐dimensional filtering model recently proposed. This way the actual dose delivery can be validated without reverting to Monte Carlo simulated PET distributions. Method: We derived the analytical expressions of the filters by converting the dose into the specific isotope profiles along the penetration depth, for all the main reaction channels which yield positron emitters in biological tissue. For the application to inhomogeneous targets a dedicated MATLAB®‐based code has been developed. Results: The new filters were first applied to monoenergetic depth dose distributions at different beam energies and the results were validated against FLUKA‐MC β+isotope distributions. All resulting distributions were found in agreement with the MC distributions, confirming filter independence from the proton beam energy. The filter functions were then applied to more realistic spread out Bragg peaks, simulated in simple inhomogeneous targets consisting of PMMA, lung and bone equivalent inserts. Conclusion: Results have shown a fairly good agreement in terms of both 50% distal fall‐off position (

Purpose: At present the most promising method for an in vivo and noninvasive monitoring of radiation treatments with protonbeams is positron emission tomography(PET). This study investigates the sensitivity and accuracy of the PET/CT treatment verification method in the presence of highly inhomogeneous tissue regions as well as in the presence of metallic implants. Method and Materials: A circular SOBP proton field has been delivered to a sophisticated in house designed phantom consisting of polymethyl methacrylate (PMMA), lung and bone equivalent slabs. The bone material contained authentic dental gold implants as well as tin lead alloy implants in different shapes. PET data were acquired in listmode starting within 15 min after irradiation at a commercial PET/CT scanner. The measured PET distributions were compared to full‐blown simulations of the PET signal based on Geant4 and FLUKA Monte Carlo(MC) codes. Results: Activation profiles were analyzed behind air‐lung, air‐bone and lung‐bone interfaces parallel to the beam as well as downstream interfaces angled at 6°. In general, a good agreement between measured and simulated PET distributions was found. Measured PET images reflected even small characteristic changes in the dose distribution. This showed the potential of PET/CT treatment verification in the presence of highly inhomogeneous tissue regions. CT artifacts due to metal implants can trick all dose calculation algorithms and lead to the prediction of a proton range overshoot behind metal implants, whereas in reality a range undershoot occurs. The PET/CT treatment verification method can detect such dose calculation errors. Conclusion: This preliminary study indicates the feasibility of PET/CT treatment verification to detect the full characteristic of the delivered dose distribution even in the presence of complex tissue inhomogeneities and metal implants.

Purpose: To improve quality and reliability of dose calculation based on pencil beam model, in treatment planning for intensity‐modulated proton therapy (IMPT). Method and Materials: Inverse treatment planning system KonRad (developed at DKFZ Heidelberg, commercially distributed by Siemens) employs pencil beam algorithm for dose calculation. Dose distributions for proton energies of 60 through 250 MeV, and various widths of the beam were precalculated using a Monte Carlo code, based on GEANT4. The results, which included description of depth dose profiles and beam broadening in tissue due to multiple Coulomb scattering, were stored in the pencil beam database. The updated database was used to optimize IMPT treatment plans. Dose calculation, assuming delivery of optimized beam intensity patterns by continuous magnetic scanning, was repeated with Monte Carlo, and the results were then compared to those obtained with KonRad pencil‐beam‐based calculation. Results: Monte Carlo calculation was in good agreement with a set of proton depth dose measurements taken in a water phantom. With the nuclear interactions “turned off”, Monte Carlo description of multiple Coulomb scattering agreed well with the theoretical model. Nuclear interactions contributed substantially to broadening of the dose distribution for higher‐energy (over 150 MeV) proton beams, mainly at relatively shallow depths. Fitting both nuclear and non‐nuclear contributions to a single Gaussian, for the database, produced a negligible systematic error in KonRad dose calculation at shallow depths (less than 0.3% of the peak dose). For sample plans, Monte Carlo and KonRad dose distributions (point doses) agreed to within 5 percent of the target prescription, except in the lung (non‐cancerous) tissue, where disagreement of up to 15% was observed. Conclusion: The use of Monte Carlo data in parametrization of pencil beam kernels improved the precision of proton dose calculation without increasing the calculation time. Supported in part by the NCI grant 5P01‐CA21239‐25.