The effect of electron backscatter and charge build up in media on beam current transformer signal for ultra-high dose rate (FLASH) electron beam monitoring.

Objective.Beam current transformers (BCT) are promising detectors for real-time beam monitoring in ultra-high dose rate (UHDR) electron radiotherapy. However, previous studies have reported a significant sensitivity of the BCT signal to changes in source-to-surface distance (SSD), field size, and phantom material which have until now been attributed to the fluctuating levels of electrons backscattered within the BCT. The purpose of this study is to evaluate this hypothesis, with the goal of understanding and mitigating the variations in BCT signal due to changes in irradiation conditions.Approach.Monte Carlo simulations and experimental measurements were conducted with a UHDR-capable intra-operative electron linear accelerator to analyze the impact of backscattered electrons on BCT signal. The potential influence of charge accumulation in media as a mechanism affecting BCT signal perturbation was further investigated by examining the effects of phantom conductivity and electrical grounding. Finally, the effectiveness of Faraday shielding to mitigate BCT signal variations is evaluated.Main Results.Monte Carlo simulations indicated that the fraction of electrons backscattered in water and on the collimator plastic at 6 and 9 MeV is lower than 1%, suggesting that backscattered electrons alone cannot account for the observed BCT signal variations. However, our experimental measurements confirmed previous findings of BCT response variation up to 15% for different field diameters. A significant impact of phantom type on BCT response was also observed, with variations in BCT signal as high as 14.1% when comparing measurements in water and solid water. The introduction of a Faraday shield to our applicators effectively mitigated the dependencies of BCT signal on SSD, field size, and phantom material.Significance.Our results indicate that variations in BCT signal as a function of SSD, field size, and phantom material are likely driven by an electric field originating in dielectric materials exposed to the UHDR electron beam. Strategies such as Faraday shielding were shown to effectively prevent these electric fields from affecting BCT signal, enabling reliable BCT-based electron UHDR beam monitoring.

Phantosmia during proton radiation and differences in frequency of phantosmia rates based on proton craniospinal irradiation technique for pediatric brain tumor patients.

BACKGROUND: Unusual olfactory perception, often referred to as "phantosmia" or "cacosmia" has been reported during brain radiotherapy (RT), but is infrequent and does not typically interfere with the ability to deliver treatment. We seek to determine the rate of phantosmia for patients treated with proton craniospinal irradiation (CSI) and identify any potential clinical or treatment-related associations. METHODS: We performed a retrospective review of 127 pediatric patients treated with CSI, followed by a boost to the brain for primary brain tumors in a single institution between 2016 and 2021. Proton CSI was delivered with passive scattering (PS) proton technique (n = 53) or pencil beam scanning technique (PBS) (n = 74). Within the PBS group, treatment delivery to the CSI utilized a single posterior (PA) field (n = 24) or two posterior oblique fields (n = 50). We collected data on phantom smell, nausea/vomiting, and the use of medical intervention. RESULTS: Our cohort included 80 males and 47 females. The median age of patients was 10 years (range: 3-21). Seventy-one patients (56%) received concurrent chemotherapy. During RT, 104 patients (82%) developed worsening nausea, while 63 patients (50%) reported episodes of emesis. Of those patients who were awake during CSI (n = 59), 17 (29%) reported phantosmia. In the non-sedated group, we found a higher rate of phantosmia in patients treated with PBS (n = 16, 42%) than PS (n = 1, 4.7%) (p = .002). Seventy-eight patients (61%) required medical intervention after developing nausea/vomiting or phantosmia during RT. Two patients required sedation due to the malodorous smell during CSI. We did not find any significant difference in nausea/vomiting based on treatment technique. CONCLUSION: Proton technique significantly influenced olfactory perception with greater rates of phantosmia with PBS compared to PS. Prospective studies should be performed to determine the cause of these findings and determine techniques to minimize phantosmia during radiation therapy.

Voxel-wise dose rate calculation in clinical pencil beam scanning proton therapy.

OBJECTIVE: Clinical outcomes after proton therapy have shown some variability that is not fully understood. Different approaches have been suggested to explain the biological outcome, but none has yet provided a comprehensive and satisfactory rationale for observed toxicities. The relatively recent transition from passive scattering (PS) to pencil beam scanning (PBS) treatments has significantly increased the voxel-wise dose rate in proton therapy. In addition, the dose rate distribution is no longer uniform along the cross section of the target but rather highly heterogeneous, following the spot placement. We suggest investigating dose rate as potential contributor to a more complex proton RBE model. Approach. Due to the time structure of the PBS beam delivery the instantaneous dose rate is highly variable voxel by voxel. Several possible parameters to represent voxel-wise dose rate for a given clinical PBS treatment plan are detailed. These quantities were implemented in the scripting environment of our treatment planning system, and computations experimentally verified. Sample applications to treated patient plans are shown. Main Results. Computed dose rates we experimentally confirmed. Dose rate maps vary depending on which method is used to represent them. Mainly, the underlying time and dose intervals chosen determine the topography of the resultant distributions. The maximum dose rates experienced by any target voxel in a given PBS treatment plan in our system range from ~100 to ~450 Gy(RBE)/min, a factor of 10 - 100 increase compared to PS. These dose rate distributions are very heterogeneous, with distinct hot spots. Significance. Voxel-wise dose rates for current clinical PBS treatment plans vary greatly from clinically established practice with PS. The exploration of different dose rate measures to evaluate potential correlations with observed clinical outcomes is suggested, potentially adding a missing component in the understanding of proton RBE.

Evaluating the effect of setup uncertainty reduction and adaptation to geometric changes on normal tissue complication probability using online adaptive head and neck intensity modulated proton therapy.

Objective. To evaluate the impact of setup uncertainty reduction (SUR) and adaptation to geometrical changes (AGC) on normal tissue complication probability (NTCP) when using online adaptive head and neck intensity modulated proton therapy (IMPT).Approach.A cohort of ten retrospective head and neck cancer patients with daily scatter corrected cone-beam CT (CBCT) was studied. For each patient, two IMPT treatment plans were created: one with a 3 mm setup uncertainty robustness setting and one with no explicit setup robustness. Both plans were recalculated on the daily CBCT considering three scenarios: the robust plan without adaptation, the non-robust plan without adaptation and the non-robust plan with daily online adaptation. Online-adaptation was simulated using an in-house developed workflow based on GPU-accelerated Monte Carlo dose calculation and partial spot-intensity re-optimization. Dose distributions associated with each scenario were accumulated on the planning CT, where NTCP models for six toxicities were applied. NTCP values from each scenario were intercompared to quantify the reduction in toxicity risk induced by SUR alone, AGC alone and SUR and AGC combined. Finally, a decision tree was implemented to assess the clinical significance of the toxicity reduction associated with each mechanism.Main results. For most patients, clinically meaningful NTCP reductions were only achieved when SUR and AGC were performed together. In these conditions, total reductions in NTCP of up to 30.48 pp were obtained, with noticeable NTCP reductions for aspiration, dysphagia and xerostomia (mean reductions of 8.25, 5.42 and 5.12 pp respectively). While SUR had a generally larger impact than AGC on NTCP reductions, SUR alone did not induce clinically meaningful toxicity reductions in any patient, compared to only one for AGC alone.SignificanceOnline adaptive head and neck proton therapy can only yield clinically significant reductions in the risk of long-term side effects when combining the benefits of SUR and AGC.

Large anatomical changes in head-and-neck cancers - A dosimetric comparison of online and offline adaptive proton therapy.

Purpose: This work evaluates an online adaptive (OA) workflow for head-and-neck (H&N) intensity-modulated proton therapy (IMPT) and compares it with full offline replanning (FOR) in patients with large anatomical changes. Methods: IMPT treatment plans are created retrospectively for a cohort of eight H&N cancer patients that previously required replanning during the course of treatment due to large anatomical changes. Daily cone-beam CTs (CBCT) are acquired and corrected for scatter, resulting in 253 analyzed fractions. To simulate the FOR workflow, nominal plans are created on the planning-CT and delivered until a repeated-CT is acquired; at this point, a new plan is created on the repeated-CT. To simulate the OA workflow, nominal plans are created on the planning-CT and adapted at each fraction using a simple beamlet weight-tuning technique. Dose distributions are calculated on the CBCTs with Monte Carlo for both delivery methods. The total treatment dose is accumulated on the planning-CT. Results: Daily OA improved target coverage compared to FOR despite using smaller target margins. In the high-risk CTV, the median D98 degradation was 1.1 % and 2.1 % for OA and FOR, respectively. In the low-risk CTV, the same metrics yield 1.3 % and 5.2 % for OA and FOR, respectively. Smaller setup margins of OA reduced the dose to all OARs, which was most relevant for the parotid glands. Conclusion: Daily OA can maintain prescription doses and constraints over the course of fractionated treatment, even in cases of large anatomical changes, reducing the necessity for manual replanning in H&N IMPT.

Artificial intelligence to predict outcomes of head and neck radiotherapy.

Head and neck radiotherapy induces important toxicity, and its efficacy and tolerance vary widely across patients. Advancements in radiotherapy delivery techniques, along with the increased quality and frequency of image guidance, offer a unique opportunity to individualize radiotherapy based on imaging biomarkers, with the aim of improving radiation efficacy while reducing its toxicity. Various artificial intelligence models integrating clinical data and radiomics have shown encouraging results for toxicity and cancer control outcomes prediction in head and neck cancer radiotherapy. Clinical implementation of these models could lead to individualized risk-based therapeutic decision making, but the reliability of the current studies is limited. Understanding, validating and expanding these models to larger multi-institutional data sets and testing them in the context of clinical trials is needed to ensure safe clinical implementation. This review summarizes the current state of the art of machine learning models for prediction of head and neck cancer radiotherapy outcomes.

Tattoo-Free Setup for Patients With Breast Cancer Receiving Regional Nodal Irradiation.

PURPOSE: Patients undergoing regional nodal irradiation (RNI) with either 3-dimensional conformal radiation therapy (3DCRT) planning or volumetric modulated arc therapy (VMAT) receive permanent tattoos to assist with daily setup alignment and verification. With the advent of surface imaging, tattoos may not be necessary to ensure setup accuracy. We compared the accuracy of conventional tattoo-based setups to those without reference to tattoos. METHODS AND MATERIALS: Forty-eight patients receiving RNI at our institution from July 2019 to December 2020 were identified. All patients received tattoos per standard of care. Twenty-four patients underwent setup using tattoos for initial positioning followed by surface and x-ray imaging. A subsequent 24 patients underwent positioning using surface imaging followed by x-ray imaging without reference to tattoos. Patient cohorts were balanced by treatment technique and use of deep inspiration breath hold. Treatment (including setup and delivery) time and x-ray-based shifts after surface imaging were recorded. RESULTS: Among patients in the tattoo group receiving 3DCRT RNI, the average treatment time per fraction was 21.35 versus 19.75 minutes in the 3DCRT RNI no-tattoo cohort (P = .03). Mean 3D vector shifts for patients in the tattoo cohort were 5.6 versus 4.4 mm in the no-tattoo cohort. The average treatment time per fraction for the tattoo VMAT RNI cohort was 23.16 versus 20.82 minutes in the no-tattoo VMAT RNI cohort (P = .08). Mean 3D vector shifts for the patients in the tattoo VMAT cohort were 5.5 versus 7.1 mm in the no-tattoo VMAT cohort. Breath hold technique and body mass index did not affect accuracy in a consistent or clinically relevant way. CONCLUSIONS: Using a combination of surface and x-ray imaging, without reference to tattoos, provides excellent accuracy in alignment and setup verification among patients receiving RNI for breast cancer, regardless of treatment technique and with reduced treatment time. Skin-based tattoos are no longer warranted for patients receiving supine RNI.

Low-Dose Computed Tomography Scanning Protocols for Online Adaptive Proton Therapy of Head-and-Neck Cancers.

PURPOSE: To evaluate the suitability of low-dose CT protocols for online plan adaptation of head-and-neck patients. METHODS: We acquired CT scans of a head phantom with protocols corresponding to CT dose index volume CTDIvol in the range of 4.2-165.9 mGy. The highest value corresponds to the standard protocol used for CT simulations of 10 head-and-neck patients included in the study. The minimum value corresponds to the lowest achievable tube current of the GE Discovery RT scanner used for the study. For each patient and each low-dose protocol, the noise relative to the standard protocol, derived from phantom images, was applied to a virtual CT (vCT). The vCT was obtained from a daily CBCT scan corresponding to the fraction with the largest anatomical changes. We ran an established adaptive workflow twice for each low-dose protocol using a high-quality daily vCT and the corresponding low-dose synthetic vCT. For a relative comparison of the adaptation efficacy, two adapted plans were recalculated in the high-quality vCT and evaluated with the contours obtained through deformable registration of the planning CT. We also evaluated the accuracy of dose calculation in low-dose CT volumes using the standard CT protocol as reference. RESULTS: The maximum differences in D98 between low-dose protocols and the standard protocol for the high-risk and low-risk CTV were found to be 0.6% and 0.3%, respectively. The difference in OAR sparing was up to 3%. The Dice similarity coefficient between propagated contours obtained with low-dose and standard protocols was above 0.982. The mean 2%/2 mm gamma pass rate for the lowest-dose image, using the standard protocol as reference, was found to be 99.99%. CONCLUSION: The differences between low-dose protocols and the standard scanning protocol were marginal. Thus, low-dose CT protocols are suitable for online adaptive proton therapy of head-and-neck cancers. As such, considering scanning protocols used in our clinic, the imaging dose associated with online adaption of head-and-neck cancers treated with protons can be reduced by a factor of 40.

Integrating Structure Propagation Uncertainties in the Optimization of Online Adaptive Proton Therapy Plans.

Currently, adaptive strategies require time- and resource-intensive manual structure corrections. This study compares different strategies: optimization without manual structure correction, adaptation with physician-drawn structures, and no adaptation. Strategies were compared for 16 patients with pancreas, liver, and head and neck (HN) cancer with 1-5 repeated images during treatment: 'reference adaptation', with structures drawn by a physician; 'single-DIR adaptation', using a single set of deformably propagated structures; 'multi-DIR adaptation', using robust planning with multiple deformed structure sets; 'conservative adaptation', using the intersection and union of all deformed structures; 'probabilistic adaptation', using the probability of a voxel belonging to the structure in the optimization weight; and 'no adaptation'. Plans were evaluated using reference structures and compared using a scoring system. The reference adaptation with physician-drawn structures performed best, and no adaptation performed the worst. For pancreas and liver patients, adaptation with a single DIR improved the plan quality over no adaptation. For HN patients, integrating structure uncertainties brought an additional benefit. If resources for manual structure corrections would prevent online adaptation, manual correction could be replaced by a fast 'plausibility check', and plans could be adapted with correction-free adaptation strategies. Including structure uncertainties in the optimization has the potential to make online adaptation more automatable.

CT-on-Rails Versus In-Room CBCT for Online Daily Adaptive Proton Therapy of Head-and-Neck Cancers.

PURPOSE: To compare the efficacy of CT-on-rails versus in-room CBCT for daily adaptive proton therapy. METHODS: We analyzed a cohort of ten head-and-neck patients with daily CBCT and corresponding virtual CT images. The necessity of moving the patient after a CT scan is the most significant difference in the adaptation workflow, leading to an increased treatment execution uncertainty σ. It is a combination of the isocenter-matching σi and random patient movements induced by the couch motion σm. The former is assumed to never exceed 1 mm. For the latter, we studied three different scenarios with σm = 1, 2, and 3 mm. Accordingly, to mimic the adaptation workflow with CT-on-rails, we introduced random offsets after Monte-Carlo-based adaptation but before delivery of the adapted plan. RESULTS: There were no significant differences in accumulated dose-volume histograms and dose distributions for σm = 1 and 2 mm. Offsets with σm = 3 mm resulted in underdosage to CTV and hot spots of considerable volume. CONCLUSION: Since σm typically does not exceed 2 mm for in-room CT, there is no clinically significant dosimetric difference between the two modalities for online adaptive therapy of head-and-neck patients. Therefore, in-room CT-on-rails can be considered a good alternative to CBCT for adaptive proton therapy.

Anatomic changes in head and neck intensity-modulated proton therapy: Comparison between robust optimization and online adaptation.

BACKGROUND/PURPOSE: Setup variations and anatomical changes can severely affect the quality of head and neck intensity-modulated proton therapy (IMPT) treatments. The impact of these changes can be alleviated by increasing the plan's robustness a priori, or by adapting the plan online. This work compares these approaches in the context of head and neck IMPT. MATERIALS/METHODS: A representative cohort of 10 head and neck squamous cell carcinoma (HNSCC) patients with daily cone-beam computed tomography (CBCT) was evaluated. For each patient, three IMPT plans were created: 1- a classical robust optimization (cRO) plan optimized on the planning CT, 2- an anatomical robust optimization (aRO) plan additionally including the two first daily CBCTs and 3- a plan optimized without robustness constraints, but online-adapted (OA) daily, using a constrained spot intensity re-optimization technique only. RESULTS: The cumulative dose following OA fulfilled the clinical objective of both the high-risk and low-risk clinical target volumes (CTV) coverage in all 10 patients, compared to 8 for aRO and 4 for cRO. aRO did not significantly increase the dose to most organs at risk compared to cRO, although the integral dose was higher. OA significantly reduced the integral dose to healthy tissues compared to both robust methods, while providing equivalent or superior target coverage. CONCLUSION: Using a simple spot intensity re-optimization, daily OA can achieve superior target coverage and lower dose to organs at risk than robust optimization methods.

Comparison of weekly and daily online adaptation for head and neck intensity-modulated proton therapy.

The high conformality of intensity-modulated proton therapy (IMPT) dose distributions causes treatment plans to be sensitive to geometrical changes during the course of a fractionated treatment. This can be addressed using adaptive proton therapy (APT). One important question in APT is the frequency of adaptations performed during a fractionated treatment, which is related to the question whether plan adaptation has to be done online or offline. The purpose of this work is to investigate the impact of weekly and daily online IMPT plan adaptation on the treatment quality for head and neck patients. A cohort of ten head and neck patients with daily acquired cone-beam CT (CBCT) images was evaluated retrospectively. Dose tracking of the IMPT treatment was performed for three scenarios: base plan with no adaptation (BP), weekly online adaptation (OAW), and daily online adaptation (OAD). Both adaptation schemes used an in-house developed online APT workflow, performing Monte Carlo dose calculations on scatter-corrected CBCTs. IMPT plan adaptation was achieved by only tuning the weights of a subset of beamlets, based on deformable image registration from the planning CT to each CBCT. Although OADmitigated random delivery errors more effectively than OAWon a fraction per fraction basis, both OAWand OADachieved the clinical goals for all ten patients, while BP failed for six cases. In the high-risk CTV, accumulated values ofD98%ranged between 97.15% and 99.73% of the prescription dose for OAD, with a median of 98.07%. For OAW, values between 95.02% and 99.26% were obtained, with a median of 97.61% of the prescription dose. Otherwise, the dose to most organs at risk was similar for all three scenarios. Globally, our results suggest that OAWcould be used as an alternative approach to OADfor most patients in order to reduce the clinical workload.

Evaluation of CBCT scatter correction using deep convolutional neural networks for head and neck adaptive proton therapy.

Adaptive proton therapy (APT) is a promising approach for the treatment of head and neck cancers. One crucial element of APT is daily volumetric imaging of the patient in the treatment position. Such data can be acquired with cone-beam computed tomography (CBCT), although scatter artifacts make uncorrected CBCT images unsuitable for proton therapy dose calculation. The purpose of this work is to evaluate the performance of a U-shape deep convolutive neural network (U-Net) to perform projection-based scatter correction and enable fast and accurate dose calculation on CBCT images in the context of head and neck APT. CBCT projections are simulated for a cohort of 48 head and neck patients using a GPU accelerated Monte Carlo (MC) code . A U-Net is trained to reproduce MC projection-based scatter correction from raw projections. The accuracy of the scatter correction is experimentally evaluated using CT and CBCT images of an anthropomorphic head phantom. The potential of the method for head and neck APT is assessed by comparing proton therapy dose distributions calculated on scatter-free, uncorrected and scatter-corrected CBCT images. Finally, dose calculation accuracy is estimated in experimental patient images using a previously validated empirical scatter correction as reference. The mean and mean absolute HU differences between scatter-free and scatter-corrected images are -0.8 and 13.4 HU, compared to -28.6 and 69.6 HU for the uncorrected images. In the head phantom, the root-mean square difference of proton ranges calculated in the reference CT and corrected CBCT is 0.73 mm. The average 2%/2 mm gamma pass rate for proton therapy plans optimized in the scatter free images and re-calculated in the scatter-corrected ones is 98.89%. In experimental CBCT patient images, a 3%/3 mm passing rate of 98.72% is achieved between the proposed method and the reference one. All CBCT projection volume could be corrected in less than 5 seconds.

Parametrization of multi-energy CT projection data with eigentissue decomposition.

The purpose of this work is, firstly, to propose an optimized parametrization of the attenuation coefficient to describe human tissues in the context of projection-based material characterization with multi-energy CT. The approach is based on eigentissue decomposition (ETD). Secondly, to evaluate its benefits in terms of accuracy and precision of radiotherapy-related parameters against established parametrizations. The attenuation coefficient is parametrized as a linear combination of virtual materials, eigentissues, obtained by performing principal component analysis on a set of reference tissues in order to optimally represent human tissue composition. Two implementations of ETD are compared with other pre-reconstruction formalisms established for dual-energy and photon-counting CT in a simulation framework. The first implementation uses a single set of eigentissues to describe all human tissues, while the second uses different sets of eigentissues to characterize soft tissues and bones, and includes a post-reconstruction classification step. The simulation framework evaluates the reconstruction accuracy of various radiotherapy-related quantities over a range of 71 human tissues for various noise levels. Compared to conventional parametrizations, the first implementation of ETD reduces the mean error and root-mean-square error (RMSE) in two radiotherapy-related quantities (the proton stopping power and the mass energy absorption coefficient of 21 keV photons from 103Pd seeds used in brachytherapy) for all noise levels and modalities investigated. This illustrates that a decomposition basis selected with principal component analysis is superior to an arbitrary pair of materials to describe human tissues. The mean error on radiotherapy-related parameters can be further reduced with the classification-based approach. In the context of pre-reconstruction material characterization with multi-energy CT, parametrizing the attenuation coefficient with eigentissues provides a more accurate and precise evaluation of human tissues properties for radiotherapy. Accurate quantification can thus be achieved without the need to parametrize tissues using unphysical parameters, such as the energy-dependent effective atomic number.

Influence of intravenous contrast agent on dose calculation in proton therapy using dual energy CT.

The purpose of this study is to evaluate the effect of an intravenous (IV) contrast agent on proton therapy dose calculation using dual-energy computed tomography (DECT). Two DECT methods are considered. The first one, [Formula: see text], attempts to accurately predict the proton stopping powers relative to water (SPR) of contrast enhanced (CE) DECT images, while the second generates a virtual non-contrast (VNC) volume that can be processed as a native non-contrast (NC) one. Both methods are compared against single-energy computed tomography (SECT). The accuracy of SPR predicted for different concentrations of IV contrast diluted in water is first evaluated using simulated data. Results then are validated in an experimental set-up comparing SPR predictions for both NC and CE images to measurements made with a multi-layer ionisation chamber (MLIC). Finally, the impact of IV contrast on dose calculation using both SECT and DECT is evaluated for one liver and one head and neck patient. Using simulated data, DECT is shown to be less sensitive to the presence of IV contrast than SECT, although the performance of the [Formula: see text] method is sensitive to the level of beam hardening considered. For different concentrations of IV contrast diluted in water, experimental MLIC measurement of SPR agrees with DECT predictions within 3% while SECT introduce errors above 20%. This error in the SPR value results in a range error of up to 3.2 mm (2.6%) for proton beams calculated on SECT CE patient images. The error is reduced below 1 mm using DECT with the [Formula: see text] and VNC methods. Globally, it is observed that the influence of IV contrast on proton therapy dose calculation is mitigated using DECT over SECT. In patient anatomies, the VNC approach provides the best agreement with the reference dose distribution.

The potential of photon-counting CT for quantitative contrast-enhanced imaging in radiotherapy.

The aim of this study is to use a simulation environment to evaluate the potential of using photon-counting CT (PCCT) against dual-energy CT (DECT) in the context of quantitative contrast-enhanced CT for radiotherapy. An adaptation of Bayesian eigentissue decomposition by Lalonde et al (2017 Med. Phys. 44 5293-302) that incorporates the estimation of contrast agent fractions and virtual non-contrast (VNC) parameters is proposed, and its performance is validated against conventional maximum likelihood material decomposition methods for single and multiple contrast agents. PCCT and DECT are compared using two simulation frameworks: one including ideal CT numbers with image-based Gaussian noise and another defined as a virtual patient with projection-based Poisson noise and beam hardening artifacts, with both scenarios considering spectral distortion for PCCT. The modalities are compared for their accuracy in estimating four key physical parameters: (1) the contrast agent fraction, as well as VNC parameters relevant to radiotherapy such as the (2) electron density, (3) proton stopping power and (4) photon linear attenuation coefficient. Considering both simulation frameworks, a reduction of root mean square (RMS) errors with PCCT is noted for all physical parameters evaluated, with the exception of the error on the contrast agent fraction being about constant through modalities in the virtual patient. Notably, for the virtual patient, RMS errors on VNC electron density and stopping power are respectively reduced from 2.0% to 1.4% and 2.7% to 1.4% when going from DECT to PCCT with four energy bins. The increase in accuracy is comparable to the differences between contrast-enhanced and non-contrast DECT. This study suggests that in a realistic simulation environment, the overall accuracy of radiotherapy-related parameters can be increased when using PCCT with four energy bins instead of DECT. This confirms the potential of PCCT to provide robust and quantitative tissue parameters for contrast-enhanced CT required in radiotherapy applications.

The impact of dual- and multi-energy CT on proton pencil beam range uncertainties: a Monte Carlo study.

The purpose of this work is to evaluate the impact of single-, dual- and multi-energy CT (SECT, DECT and MECT) on proton range uncertainties in a patient like geometry and a full Monte Carlo environment. A virtual patient is generated from a real patient pelvis CT scan, where known mass densities and elemental compositions are overwritten in each voxel. Simulated CT images for SECT, DECT and MECT are generated for two limiting cases: (1) theoretical and idealistic CT numbers only affected by Gaussian noise (case A, the best scenario) and (2) reconstructed polyenergetic sinograms containing beam hardening, projection-based Poisson noise, and reconstruction artifacts (case B, the worst scenario). Conversion of the simulated SECT images into Monte Carlo inputs is done following the stoichiometric calibration method. For DECT and MECT, the Bayesian eigentissue decomposition method of Lalonde (2017 Med. Phys. 44 5293-302) is used. Pencil beams from seven different angles around the virtual patient are simulated using TOPAS to assess the performance of each method. Percentage depth doses curves (PDD) are compared to ground truth in order to determine the accuracy of range prediction of each imaging modality. For the idealistic images of case A, MECT and DECT slightly outperforms SECT. Root mean square (RMS) errors or 0.78 mm, 0.49 mm and 0.42 mm on R 80 mm, are observed for SECT, DECT and MECT respectively. In case B, PDD calculated in the MECT derived Monte Carlo inputs generally shows the best agreement with ground truth in both shape and position, with RMS errors of 2.03 mm, 1.38 mm and 0.86 mm for SECT, DECT and MECT respectively. Overall, the Bayesian eigentissue decomposition used with DECT systematically predicts proton ranges more accurately than the gold standard SECT-based approach. When CT numbers are severely affected by imaging artifacts, MECT with four energy bins becomes more reliable than both DECT and SECT.

Unsupervised classification of tissues composition for Monte Carlo dose calculation.

The purpose of this study is to investigate the potential of k-means clustering to efficiently reduce the variety of materials needed in Monte Carlo (MC) dose calculation. A numerical phantom with 31 human tissues surrounded by water is created. K-means clustering is used to group the tissues in clusters of constant elemental composition. Four different distance measures are used to perform the clustering technique: Euclidean, Standardized Euclidean, Chi-Squared and Cityblock. Dose distributions are calculated with MC simulations for both low-kV photons and MeV protons using the clustered and reference elemental composition. Comparison between the dose distributions in the clustered and non-clustered phantom are made to assess the impact of clustering with each distance measure. The statistical significance of the differences observed between the four different metrics is determined by comparing the accuracy of energy absorption coefficients (EAC) of low-kV photons and proton stopping powers relative to water (SPR) for repeated clustering procedures. The performance of the proposed approach for a larger number of original materials is evaluated similarly by using a population of 62 000 statistically generated materials grouped into classes defined with supervised and unsupervised classification. In the phantom geometry, the Chi-Squared distance is the one introducing the smallest error on dose distribution and significant differences are observed between the EAC and SPR values predicted by each distance metric. The proposed approach is also shown to be equivalent to a state-of-the-art supervised classification method for proton therapy, but beneficial for low-kV photons applications. In conclusion, k-means clustering successfully reduces the variety of materials needed for accurate MC dose calculation. Based on the performance of four distance measures, we conclude that k-means clustering using the Chi-Squared distance introduces the smallest errors on dose distribution. The method is shown to yield similar or improved accuracy on key physical parameters compared to supervised classification.

Optimized I-values for use with the Bragg additivity rule and their impact on proton stopping power and range uncertainty.

Novel imaging modalities can improve the estimation of patient elemental compositions for particle treatment planning. The mean excitation energy (I-value) is a main contributor to the proton range uncertainty. To minimize their impact on beam range errors and quantify their uncertainties, the currently used I-values proposed in 1982 are revisited. The study aims at proposing a new set of optimized elemental I-values for use with the Bragg additivity rule (BAR) and establishing uncertainties on the optimized I-values and the BAR. We optimize elemental I-values for the use in compounds based on measured material I-values. We gain a new set of elemental I-values and corresponding uncertainties, based on the experimental uncertainties and our uncertainty model. We evaluate uncertainties on I-values and relative stopping powers (RSP) of 70 human tissues, taking into account statistical correlations between tissues and water. The effect of new I-values on proton beam ranges is quantified using Monte Carlo simulations. Our elemental I-values describe measured material I-values with higher accuracy than ICRU-recommended I-values (RMSE: 6.17% (ICRU), 5.19% (this work)). Our uncertainty model estimates an uncertainty component from the BAR to 4.42%. Using our elemental I-values, we calculate the I-value of water as 78.73 ± 2.89 eV, being consistent with ICRU 90 (78 ± 2 eV). We observe uncertainties on tissue I-values between 1.82-3.38 eV, and RSP uncertainties between 0.002%-0.44%. With transport simulations of a proton beam in human tissues, we observe range uncertainties between 0.31% and 0.47%, as compared to current estimates of 1.5%. We propose a set of elemental I-values well suited for human tissues in combination with the BAR. Our model establishes uncertainties on elemental I-values and the BAR, enabling to quantify uncertainties on tissue I-values, RSP as well as particle range. This work is particularly relevant for Monte Carlo simulations where the interaction probabilities are reconstructed from elemental compositions.