The potential of dual-energy CT to reduce proton beam range uncertainties

Benefit of range uncertainty reduction in robust optimisation for proton therapy of brain, head-and-neck and breast cancer patients

Background and Purpose

The primary cause of range uncertainty in proton therapy is inaccuracy in estimating the stopping-power ratio from computed tomography. This study examined the impact on dose-volume metrics by reducing range uncertainty in robust optimisation for a diverse patient cohort and determined the level of range uncertainty that resulted in a relevant reduction in doses to organs-at-risk (OARs).

Materials and Methods

The effect of reducing range uncertainty on OAR doses was evaluated by robustly optimising six proton plans with varying range uncertainty levels (ranging from 3.5% in the original plan to 1.0%), keeping setup uncertainty fixed. All plans used the initial clinical treatment plan's beam directions and optimisation objectives and were optimised until a clinically acceptable plan was achieved across all setup and range scenarios. The effect of reduced range uncertainty on dose-volume metrics for OARs near the target was evaluated. This study included 30 brain cancer patients, as well as five head-and-neck and five breast cancer patients, investigating the relevance of reducing range uncertainty when different setup uncertainties were used.

Results

Lowering range uncertainty slightly reduced the nominal dose to surrounding tissue. For body volume receiving 80% of the prescribed dose, reducing range uncertainty from 3.5% to 2.0% resulted in a median decrease of 4 cm3 for the brain, 17 cm3 for head-and-neck, and 27 cm3 for breast cancer patients.

Conclusions

Reducing range uncertainty in robust optimisation showed a reduction in dose to OARs. The clinical relevance depends on the affected organs and the clinical dose constraints.

Conversion of single-energy CT to parametric maps of dual-energy CT using convolutional neural network

OBJECTIVES

We propose a deep learning (DL) multitask learning framework using convolutional neural network for a direct conversion of single-energy CT (SECT) to 3 different parametric maps of dual-energy CT (DECT): virtual-monochromatic image (VMI), effective atomic number (EAN), and relative electron density (RED).

METHODS

We propose VMI-Net for conversion of SECT to 70, 120, and 200 keV VMIs. In addition, EAN-Net and RED-Net were also developed to convert SECT to EAN and RED. We trained and validated our model using 67 patients collected between 2019 and 2020. Single-layer CT images with 120 kVp acquired by the DECT (IQon spectral CT; Philips Healthcare, Amsterdam, Netherlands) were used as input, while the VMIs, EAN, and RED acquired by the same device were used as target. The performance of the DL framework was evaluated by absolute difference (AD) and relative difference (RD).

RESULTS

The VMI-Net converted 120 kVp SECT to the VMIs with AD of 9.02 Hounsfield Unit, and RD of 0.41% compared to the ground truth VMIs. The ADs of the converted EAN and RED were 0.29 and 0.96, respectively, while the RDs were 1.99% and 0.50% for the converted EAN and RED, respectively.

CONCLUSIONS

SECT images were directly converted to the 3 parametric maps of DECT (ie, VMIs, EAN, and RED). By using this model, one can generate the parametric information from SECT images without DECT device. Our model can help investigate the parametric information from SECT retrospectively.

ADVANCES IN KNOWLEDGE

DL framework enables converting SECT to various high-quality parametric maps of DECT.

First clinical experience following the consensus guide for calibrating a proton stopping power ratio curve in a new proton centre

BACKGROUND AND PURPOSE

This work introduces the first assessment of CT calibration following the ESTRO's consensus guidelines and validating the HLUT through the irradiation of biological material.

METHODS

Two electron density phantoms were scanned with two CT scanners using two CT scan energies. The stopping power ratio (SPR) and mass density (MD) HLUTs for different CT scan energies were derived using Schneider's and ESTRO's methods. The comparison metric in this work is based on the Water-Equivalent Thickness (WET) difference between the treatment planning system and biological irradiation measurement. The SPR HLUTs were compared between the two calibration methods. To assess the accuracy of using MD HLUT for dose calculation in the treatment planning system, MD vs SPR HLUT was compared. Lastly, the feasibility of using a single SPR HLUT to replace two different energy CT scans was explored.

RESULTS

The results show a WET difference of less than 3.5% except for the result in the Bone region between Schneider's and ESTRO's methods. Comparing MD and SPR HLUT, the results from MD HLUT show less than a 3.5% difference except for the Bone region. However, the SPR HLUT shows a lower mean absolute percentage difference as compared to MD HLUT between the measured and calculated WET difference. Lastly, it is possible to use a single SPR HLUT for two different CT scan energies since both WET differences are within 3.5%.

CONCLUSION

This is the first report on calibrating an HLUT following the ESTRO's guidelines. While our result shows incremental improvement in range uncertainty using the ESTRO's guideline, the prescriptional approach of the guideline does promote harmonization of CT calibration protocols between different centres.

Estimation of Proton Stopping Power Ratio and Mean Excitation Energy Using Electron Density and Its Applications via Machine Learning Approach

Purpose

The purpose of this study was to develop a simple flexible method for accurate estimation of stopping power ratio (SPR) and mean excitation energy (I) using relative electron density (ρ e).

Materials and Methods

The model was formulated using empirical relationships between SPR, mean excitation energy I, and relative electron density. Some examples were implemented, and a comparison was carried out using other existing methods. The needed coefficients in the model were estimated using optimization tools. Basis vector method (BVM) and Hunemohr and Saito (H-S) method were applied to estimate the ρ e used in the application section. 80 kVp and 150 kVpSn were used as low and high energy, respectively, for the implementation of dual-energy methods.

Results

All the examples of the proposed method considered have modeling error that is ≤0.32% and testing root mean square error (RMSE) ≤0.92% for SPR with a mean error close to 0.00%. The method was able to achieve modeling RMSE of 2.12% for mean excitation energy with room for improvement. Similar or better results were achieved in application to BVM.

Conclusion

The method showed robustness in application by achieving lower testing error than other presented methods in most cases. It achieved accurate estimation which can be improved using the machine learning algorithm since it is flexible to implement in terms of the function (model) degree and tissue classification.

Impact of and interplay between proton arc therapy and range uncertainties in proton therapy for head-and-neck cancer

Objective. Proton therapy reduces the integral dose to the patient compared to conventional photon treatments. However,in vivoproton range uncertainties remain a considerable hurdle. Range uncertainty reduction benefits depend on clinical practices. During intensity-modulated proton therapy (IMPT), the target is irradiated from only a few directions, but proton arc therapy (PAT), for which the target is irradiated from dozens of angles, may see clinical implementation by the time considerable range uncertainty reductions are achieved. It is therefore crucial to determine the impact of PAT on range uncertainty reduction benefits.Approach. For twenty head-and-neck cancer patients, four different treatment plans were created: an IMPT and a PAT treatment plan assuming current clinical range uncertainties of 3.5% (IMPT3.5%and PAT3.5%), and an IMPT and a PAT treatment plan assuming that range uncertainties can be reduced to 1% (IMPT1%and PAT1%). Plans were evaluated with respect to target coverage and organ-at-risk doses as well as normal tissue complication probabilities (NTCPs) for parotid glands (endpoint: parotid gland flow <25%) and larynx (endpoint: larynx edema).Main results. Implementation of PAT (IMPT3.5%-PAT3.5%) reduced mean NTCPs in the nominal and worst-case scenario by 3.2 percentage points (pp) and 4.2 pp, respectively. Reducing range uncertainties from 3.5% to 1% during use of IMPT (IMPT3.5%-IMPT1%) reduced evaluated NTCPs by 0.9 pp and 2.0 pp. Benefits of range uncertainty reductions subsequently to PAT implementation (PAT3.5%-PAT1%) were 0.2 pp and 1.0 pp, with considerably higher benefits in bilateral compared to unilateral cases.Significance. The mean clinical benefit of implementing PAT was more than twice as high as the benefit of a 3.5%-1% range uncertainty reduction. Range uncertainty reductions are expected to remain beneficial even after PAT implementation, especially in cases with target positions allowing for full leveraging of the higher number of gantry angles during PAT.

Prediction of proton stopping power ratios using dual-energy CT basis material decomposition

BACKGROUND

Proton radiotherapy treatment plans are currently restricted by the range uncertainties originating from the stopping power ratio (SPR) prediction based on single-energy computed tomography (SECT). Various studies have shown that multi-energy CT (MECT) can reduce the range uncertainties due to medical implant materials and age-related variations in tissue composition. None of these has directly applied the basis material density (MD) images produced by projection-based MECT systems for SPR prediction.

PURPOSE

To present and evaluate a novel proton SPR prediction method based on MD images from dual-energy CT (DECT), which could reduce the range uncertainties currently associated with proton radiotherapy.

METHODS

A theoretical basis material decomposition into water and iodine material densities was performed for various pediatric and adult human reference tissues, as well as other non-tissue materials, by minimizing the root-mean-square relative attenuation error in the energy interval from 40 to 140 keV. A model (here called MD-SPR) mapping predicted MDs to theoretically calculated reference SPRs was created with locally weighted scatterplot smoothing (LOWESS) data-fitting. The goodness of fit of the MD-SPR model was evaluated for the included reference tissues. MD images of two electron density phantoms, combined to form a head- and an abdomen-sized phantom setup, were acquired with a clinical projection-based fast-kV switching DECT scanner. The MD images were compared to the theoretically predicted MDs of the tissue surrogates and other non-tissue materials in the phantoms, as well as used for input to the MD-SPR model for generation of SPR images. The SPR images were subsequently compared to theoretical reference SPRs of the materials in the phantoms, as well as to SPR images from a commercial algorithm (DirectSPR, Siemens Healthineers, Forchheim, Germany) using image-based consecutive scan DECT for the same phantom setups.

RESULTS

The predicted SPRs of the tissue surrogates were similar for MD-SPR and DirectSPR, where the adipose and bone tissue surrogates were within 1% difference to the reference SPRs, while other non-adipose soft tissue surrogates (breast, brain, liver, muscle) were all underestimated by between -0.7% and -1.8%. The SPRs of the non-tissue materials (polymethyl methacrylate (PMMA), polyether ether ketone (PEEK), graphite and Teflon) were within 2.8% for MD-SPR images, compared to 6.8% for DirectSPR.

CONCLUSIONS

The MD-SPR model performed similar compared to other published methods for the human reference tissues. The SPR prediction for tissue surrogates was similar to DirectSPR and showed potential to improve SPR prediction for non-tissue materials.

Enhancement of Stopping Power Ratio (SPR) Estimation Accuracy through Image-Domain Dual-Energy Computer Tomography for Pencil Beam Scanning System: A Simulation Study

Our study aims to quantify the impact of spectral separation on achieved theoretical prediction accuracy of proton-stopping power when the volume discrepancy between calibration phantom and scanned object is observed. Such discrepancy can be commonly seen in our CSI pediatric patients. One of the representative image-domain DECT models is employed on a virtual phantom to derive electron density and effective atomic number for a total of 34 ICRU standard human tissues. The spectral pairs used in this study are 90 kVp/140 kVp, without and with 0.1 mm to 0.5 mm additional tin filter. The two DECT images are reconstructed via a conventional filtered back projection algorithm (FBP) on simulated noiseless projection data. The best-predicted accuracy occurs at a spectral pair of 90 kVp/140 kVp with a 0.3 mm tin filter, and the root-mean-squared average error is 0.12% for tissue substitutes. The results reveal that the selected image-domain model is sensitive to spectral pair deviation when there is a discrepancy between calibration and scanning conditions. This study suggests that an optimization process may be needed for clinically available DECT scanners to yield the best proton-stopping power estimation.

Multimodal imaging-based material mass density estimation for proton therapy using supervised deep learning

OBJECTIVE

Mapping CT number to material property dominates the proton range uncertainty. This work aims to develop a physics-constrained deep learning-based multimodal imaging (PDMI) framework to integrate physics, deep learning, MRI, and advanced dual-energy CT (DECT) to derive accurate patient mass density maps.

METHODS

Seven tissue substitute MRI phantoms were used for validation including adipose, brain, muscle, liver, skin, spongiosa, 45% hydroxyapatite (HA) bone. MRI images were acquired using T1 weighted Dixon and T2 weighted short tau inversion recovery sequences. Training inputs are from MRI and twin-beam dual-energy images acquired at 120 kVp with gold/tin filters. The feasibility investigation included an empirical model and four residual networks (ResNet) derived from different training inputs and strategies by PDMI framework. PRN-MR-DE and RN-MR-DE denote ResNet (RN) trained with and without a physics constraint (P) using MRI (MR) and DECT (DE) images. PRN-DE stands for RN trained with a physics constraint using only DE images. A retrospective study using institutional patient data was also conducted to investigate the feasibility of the proposed framework.

RESULTS

For the tissue surrogate study, PRN-MR-DE, PRN-DE, and RN-MR-DE result in mean mass density errors: -0.72%/2.62%/-3.58% for adipose; -0.03%/-0.61%/-0.18% for muscle; -0.58%/-1.36%/-4.86% for 45% HA bone. The retrospective patient study indicated that PRN-MR-DE predicted the densities of soft tissue and bone within expected intervals based on the literature survey, while PRN-DE generated large density deviations.

CONCLUSION

The proposed PDMI framework can generate accurate mass density maps using MRI and DECT images. The supervised learning can further enhance model efficacy, making PRN-MR-DE outperform RN-MR-DE. The patient investigation also shows that the framework can potentially improve proton range uncertainty with accurate patient mass density maps.

ADVANCES IN KNOWLEDGE

PDMI framework is proposed for the first time to inform deep learning models by physics insights and leverage the information from MRI to derive accurate mass density maps.

Pediatric Applications of Dual-Energy Computed Tomography

There is renewed interest in novel pediatric dual-energy computed tomography (DECT) applications that can image awake patients faster and at low radiation doses. DECT enables the simultaneous acquisition of 2 data sets at different energy levels, allowing for better material characterization and unique image reconstructions that enhance image analysis and provide quantitative and qualitative information about tissue composition. Pediatric DECT reduces radiation doses further while accelerating image acquisition and improving motion robustness. Current applications include the improved evaluation of congenital and acquired cardiovascular anomalies, lung perfusion and ventilation, renal stone composition, tumor extension and treatment response, and gastrointestinal diseases.

The influence of dual-energy computed tomography image noise in proton therapy treatment planning

Background and purpose

In proton therapy, a 3.5% margin is often used to account for proton range uncertainties, of which computed tomography (CT) image noise is assumed to contribute 0.5%. This work evaluates the noise-sensitivity of three dual-energy computed tomography (DECT)-based methods for mapping proton stopping power relative to water (SPR): Näsmark & Andersson (N&A), Landry-Saito (L-S), and the commercial application DirectSPR.

Methods and materials

DECT image data of a CIRS-062M phantom was acquired with CT scanners from two different vendors. Acquisitions were repeated 30 times to account for intra- and inter-scan variations. SPR maps were generated with the three DECT-based methods and range simulated in a commercial treatment planning system.

Results

Noise in input data was amplified in L-S SPR maps, kept level with DirectSPR, while N&A compressed noise overall but displayed sensitivity to the choice of input data, potentially leading to increased noise levels. In our simulations, only N&A improved upon the assumed 0.5% noise contribution to range uncertainty on one scanner. On the other scanner, uncertainties exceeded 0.5% for all three methods. Mitigation of this issue was demonstrated by using a method employing virtual mono-energetic images as input. Increasing imaging radiation dose, as expected, alleviates the problem, while applying noise reduction only helped to a lesser extent.

Conclusions

While range uncertainty due to noise is small compared to other contributions, it becomes more important as we move towards smaller treatment margins and the noise-sensitivity of SPR mapping methods should be carefully estimated and considered before clinical implementation.

Validation of an MR-based multimodal method for molecular composition and proton stopping power ratio determination using ex vivo animal tissues and tissue-mimicking phantoms

Objective. Range uncertainty in proton therapy is an important factor limiting clinical effectiveness. Magnetic resonance imaging (MRI) can measure voxel-wise molecular composition and, when combined with kilovoltage CT (kVCT), accurately determine mean ionization potential (Im), electron density, and stopping power ratio (SPR). We aimed to develop a novel MR-based multimodal method to accurately determine SPR and molecular compositions. This method was evaluated in tissue-mimicking andex vivoporcine phantoms, and in a brain radiotherapy patient.Approach. Four tissue-mimicking phantoms with known compositions, two porcine tissue phantoms, and a brain cancer patient were imaged with kVCT and MRI. Three imaging-based values were determined: SPRCM(CT-based Multimodal), SPRMM(MR-based Multimodal), and SPRstoich(stoichiometric calibration). MRI was used to determine two tissue-specific quantities of the Bethe Bloch equation (Im, electron density) to compute SPRCMand SPRMM. Imaging-based SPRs were compared to measurements for phantoms in a proton beam using a multilayer ionization chamber (SPRMLIC).Main results. Root mean square errors relative to SPRMLICwere 0.0104(0.86%), 0.0046(0.45%), and 0.0142(1.31%) for SPRCM, SPRMM, and SPRstoich, respectively. The largest errors were in bony phantoms, while soft tissue and porcine tissue phantoms had <1% errors across all SPR values. Relative to known physical molecular compositions, imaging-determined compositions differed by approximately ≤10%. In the brain case, the largest differences between SPRstoichand SPRMMwere in bone and high lipids/fat tissue. The magnitudes and trends of these differences matched phantom results.Significance. Our MR-based multimodal method determined molecular compositions and SPR in various tissue-mimicking phantoms with high accuracy, as confirmed with proton beam measurements. This method also revealed significant SPR differences compared to stoichiometric kVCT-only calculation in a clinical case, with the largest differences in bone. These findings support that including MRI in proton therapy treatment planning can improve the accuracy of calculated SPR values and reduce range uncertainties.

Emerging technologies for cancer therapy using accelerated particles

Cancer therapy with accelerated charged particles is one of the most valuable biomedical applications of nuclear physics. The technology has vastly evolved in the past 50 years, the number of clinical centers is exponentially growing, and recent clinical results support the physics and radiobiology rationale that particles should be less toxic and more effective than conventional X-rays for many cancer patients. Charged particles are also the most mature technology for clinical translation of ultra-high dose rate (FLASH) radiotherapy. However, the fraction of patients treated with accelerated particles is still very small and the therapy is only applied to a few solid cancer indications. The growth of particle therapy strongly depends on technological innovations aiming to make the therapy cheaper, more conformal and faster. The most promising solutions to reach these goals are superconductive magnets to build compact accelerators; gantryless beam delivery; online image-guidance and adaptive therapy with the support of machine learning algorithms; and high-intensity accelerators coupled to online imaging. Large international collaborations are needed to hasten the clinical translation of the research results.

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.

Dual-energy CT-based stopping power prediction for dental materials in particle therapy

Radiotherapy with protons or light ions can offer accurate and precise treatment delivery. Accurate knowledge of the stopping power ratio (SPR) distribution of the tissues in the patient is crucial for improving dose prediction in patients during planning. However, materials of uncertain stoichiometric composition such as dental implant and restoration materials can substantially impair particle therapy treatment planning due to related SPR prediction uncertainties. This study investigated the impact of using dual-energy computed tomography (DECT) imaging for characterizing and compensating for commonly used dental implant and restoration materials during particle therapy treatment planning. Radiological material parameters of ten common dental materials were determined using two different DECT techniques: sequential acquisition CT (SACT) and dual-layer spectral CT (DLCT). DECT-based direct SPR predictions of dental materials via spectral image data were compared to conventional single-energy CT (SECT)-based SPR predictions obtained via indirect CT-number-to-SPR conversion. DECT techniques were found overall to reduce uncertainty in SPR predictions in dental implant and restoration materials compared to SECT, although DECT methods showed limitations for materials containing elements of a high atomic number. To assess the influence on treatment planning, an anthropomorphic head phantom with a removable tooth containing lithium disilicate as a dental material was used. The results indicated that both DECT techniques predicted similar ranges for beams unobstructed by dental material in the head phantom. When ion beams passed through the lithium disilicate restoration, DLCT-based SPR predictions using a projection-based method showed better agreement with measured reference SPR values (range deviation: 0.2 mm) compared to SECT-based predictions. DECT-based SPR prediction may improve the management of certain non-tissue dental implant and restoration materials and subsequently increase dose prediction accuracy.

Tissue-specific range uncertainty estimation in proton therapy

Background and Purpose

Proton therapy is sensitive to range uncertainties, which typically are accounted for by margins or robust optimization, based on tissue-independent uncertainties. However, range uncertainties have been shown to depend on the specific tissues traversed. The aim of this study was to investigate the differences between range margins based on stopping power ratio (SPR) uncertainties which were tissue-specific (applied voxel-wise) or fixed (tissue-independent or composite).

Materials and Methods

Uncertainties originating from imaging, computed tomography (CT) number estimation, and SPR estimation were calculated for low-, medium-, and high-density tissues to quantify the tissue-specific SPR uncertainties. Four clinical treatment plans (four different tumor sites) were created and recomputed after applying either tissue-specific or fixed SPR uncertainties. Plans with tissue-specific and fixed uncertainties were compared, based on dose-volume-histogram parameters for both targets and organs-at-risk.

Results

The total SPR uncertainties were 7.0% for low-, 1.0% for medium-, and 1.3% for high-density tissues. Differences between the proton plans with tissue-specific and fixed uncertainties were mainly found in the vicinity of the target. Composite uncertainties were found to capture the tissue-specific uncertainties more accurately than the tissue-independent uncertainties.

Conclusion

Different SPR uncertainties were found for low-, medium-, and high-density tissues indicating that range margins based on tissue-specific uncertainties may be more exact than the standard approach of using tissue-independent uncertainties. Differences between applying tissue-specific and fixed uncertainties were found, however, a fixed uncertainty might still be sufficient, but with a magnitude that depends on the body region.

Stopping-power ratio estimation for proton radiotherapy using dual-energy computed tomography and prior-image constrained denoising

BACKGROUND

Dual-energy computed tomography (DECT) is a promising technique for estimating stopping-power ratio (SPR) for proton therapy planning. It is known, however, that deriving electron density (ED) and effective atomic number (EAN) from DECT data can cause noise amplification in the resulting SPR images. This can negate the benefits of DECT.

PURPOSE

This work introduces a new algorithm for estimating SPR from DECT with noise suppression, using a pair of CT scans with spectral separation. The method is demonstrated using phantom measurements.

MATERIALS AND METHODS

An iterative algorithm is presented, reconstructing ED and EAN with noise suppression, based on Prior Image Constrained Denoising (PIC-D). The algorithm is tested using a Siemens Definition AS+ CT scanner (Siemens Healthcare, Forchheim, Germany). Three phantoms are investigated: a calibration phantom (CIRS 062M), a QA phantom (CATPHAN 700), and an anthropomorphic head phantom (CIRS 731-HN). A task-transfer function (TTF) and the noise power spectrum are derived from SPR images of the QA phantom for the evaluation of image quality. Comparisons of accuracy and noise for ED, EAN, and SPR are made for various versions of the algorithm in comparison to a solution based on Siemens syngo.via Rho/Z software and the current clinical standard of a single-energy CT stoichiometric calibration. A gamma analysis is also applied to the SPR images of the head phantom and water-equivalent distance (WED) is evaluated in a treatment planning system for a proton treatment field.

RESULTS

The algorithm is effective at suppressing noise in both ED and EAN and hence also SPR. The noise is tunable to a level equivalent to or lower than that of the syngo.via Rho/Z software. The spatial resolution (10% and 50% frequencies in the TTF) does not degrade even for the highest noise suppression investigated, although the average spatial frequency of noise does decrease. The PIC-D algorithm showed better accuracy than syngo.via Rho/Z for low density materials. In the calibration phantom, it was superior even when excluding lung substitutes, with root-mean-square deviations for ED and EAN less than 0.3% and 2%, respectively, compared to 0.5% and 3%. In the head phantom, however, the SPR accuracy of the PIC-D algorithm was comparable (excluding sinus tissue) to that derived from syngo.via Rho/Z: less than 1% error for soft tissue, brain, and trabecular bone substitutes and 5-7% for cortical bone, with the larger error for the latter likely related to the phantom geometry. Gamma evaluation showed that PIC-D can suppress noise in a patient-like geometry without introducing substantial errors in SPR. The absolute pass rates were almost identical for PIC-D and syngo.via Rho/Z. In the WED evaluations no general differences were shown.

CONCLUSIONS

The PIC-D DECT algorithm provides scanner-specific calibration and tunable noise suppression. It is vendor agnostic and applicable to any pair of CT scans with spectral separation. Improved accuracy to current methods was not clearly demonstrated for the complex geometry of a head phantom, but the suppression of noise without spatial resolution degradation and the possibility of incorporating constraints on image properties, suggests the usefulness of the approach.

Dual- and multi-energy CT for particle stopping-power estimation: current state, challenges and potential

Range uncertainty has been a key factor preventing particle radiotherapy from reaching its full physical potential. One of the main contributing sources is the uncertainty in estimating particle stopping power (ρ_s) within patients. Currently, theρ_sdistribution in a patient is derived from a single-energy CT (SECT) scan acquired for treatment planning by converting CT number expressed in Hounsfield units (HU) of each voxel toρ_susing a Hounsfield look-up table (HLUT), also known as the CT calibration curve. HU andρ_sshare a linear relationship with electron density but differ in their additional dependence on elemental composition through different physical properties, i.e. effective atomic number and mean excitation energy, respectively. Because of that, the HLUT approach is particularly sensitive to differences in elemental composition between real human tissues and tissue surrogates as well as tissue variations within and among individual patients. The use of dual-energy CT (DECT) forρ_sprediction has been shown to be effective in reducing the uncertainty inρ_sestimation compared to SECT. The acquisition of CT data over different x-ray spectra yields additional information on the material elemental composition. Recently, multi-energy CT (MECT) has been explored to deduct material-specific information with higher dimensionality, which has the potential to further improve the accuracy ofρ_sestimation. Even though various DECT and MECT methods have been proposed and evaluated over the years, these approaches are still only scarcely implemented in routine clinical practice. In this topical review, we aim at accelerating this translation process by providing: (1) a comprehensive review of the existing DECT/MECT methods forρ_sestimation with their respective strengths and weaknesses; (2) a general review of uncertainties associated with DECT/MECT methods; (3) a general review of different aspects related to clinical implementation of DECT/MECT methods; (4) other potential advanced DECT/MECT applications beyondρ_sestimation.

A comparative study of machine-learning approaches in proton radiography using energy-resolved dose function

PURPOSE

The feasibility of machine learning (ML) techniques and their performance compared to the conventional χ²-minimization technique in the context of the proton energy-resolved dose imaging method are presented.

MATERIALS AND METHOD

Various geometries resembling a wedge and varying gradients are simulated in GATE to obtain energy-resolved dose functions (ERDF) from proton beams of different energies. These ERDFs are used to predict the WEPL using a conventional technique and other ML-based methods. The results are compared to gain an understanding of the performance of ML models in proton radiography.

RESULTS

The results obtained from the χ²-minimization technique indicate that it is robust and more reliable compared to the ML-based techniques. It is also observed that the ML-based techniques did not mitigate the effect of range-mixing but seem to be more affected by it compared to the χ²-minimization technique. Substantial data reduction was required in order to make the results of ML-based methods comparable to that of χ²-minimization. We also note that such data reduction might not be possible in a clinical setting. The only advantage in using the ML-based technique is the computational time required to generate a WEPL map which, in our case study, is 10-30 times shorter than the time required for the conventional χ²-minimization technique.

CONCLUSIONS

The first results from this preliminary study indicate that the ML techniques failed to be on par with the conventional χ²-minimization technique in terms of the achievable accuracy in the predictions of WEPL and in the mitigation of range-mixing effects in the WEPL image. Modern strategies like the GAN-based models may be suitable for such applications.

Spectral CT in Oncology

BACKGROUND

Spectral CT is gaining increasing clinical importance with multiple potential applications, including oncological imaging. Spectral CT-specific image data offers multiple advantages over conventional CT image data through various post-processing algorithms, which will be highlighted in the following review.

METHODOLOGY

The purpose of this review article is to provide an overview of potential useful oncologic applications of spectral CT and to highlight specific spectral CT pitfalls. The technical background, clinical advantages of primary and follow-up spectral CT exams in oncology, and the application of appropriate spectral tools will be highlighted.

RESULTS/CONCLUSIONS

Spectral CT imaging offers multiple advantages over conventional CT imaging, particularly in the field of oncology. The combination of virtual native and low monoenergetic images leads to improved detection and characterization of oncologic lesions. Iodine-map images may provide a potential imaging biomarker for assessing treatment response.

KEY POINTS

· The most important spectral CT reconstructions for oncology imaging are virtual unenhanced, iodine map, and virtual monochromatic reconstructions.. · The combination of virtual unenhanced and low monoenergetic reconstructions leads to better detection and characterization of the vascularization of solid tumors.. · Iodine maps can be a surrogate parameter for tumor perfusion and potentially used as a therapy monitoring parameter.. · For radiotherapy planning, the relative electron density and the effective atomic number of a tissue can be calculated..

CITATION FORMAT

· Sauerbeck J, Adam G, Meyer M. Onkologische Bildgebung mittels Spektral-CT. Fortschr Röntgenstr 2023; 195: 21 - 29.

Spectral CT imaging: Technical principles of dual-energy CT and multi-energy photon-counting CT

Spectral computed tomography (CT) imaging encompasses a unique generation of CT systems based on a simple principle that makes use of the energy-dependent information present in CT images. Over the past two decades this principle has been expanded with the introduction of dual-energy CT systems. The first generation of spectral CT systems, represented either by dual-source or dual-layer technology, opened up a new imaging approach in the radiology community with their ability to overcome the limitations of tissue characterization encountered with conventional CT. Its expansion worldwide can also be considered as an important leverage for the recent groundbreaking technology based on a new chain of detection available on photon counting CT systems, which holds great promise for extending CT towards multi-energy CT imaging. The purpose of this article was to detail the basic principles and techniques of spectral CT with a particular emphasis on the newest technical developments of dual-energy and multi-energy CT systems.

Impact of beam-hardening corrections on proton relative stopping power estimates from single- and dual-energy CT

A major contributing factor to proton range uncertainty is the conversion of computed tomography (CT) Hounsfield units (HU) to proton relative stopping power (RSP). This uncertainty is heightened in the presence of X-ray beam-hardening artifact (BHA), which has two manifestations: cupping and streaking, especially in and near bone tissue. This uncertainty can affect the accuracy of proton RSP calculation for treatment planning in proton radiotherapy. Dual-energy CT (DECT) and iterative beam-hardening correction (iBHC) both show promise in mitigating CT BHA. This present work attempts to analyze the relative robustness of iBHC and DECT techniques on both manifestations of BHA. The stoichiometric method for HU to RSP conversion was used for single-energy CT (SECT) and DECT-based monochromatic techniques using a tissue substitute phantom. Cupping BHA was simulated by measuring the HU of a bone substitute plug in wax/3D-printed phantoms of increasing size. Streaking BHA was simulated by placing a solid water plug between two bone plugs in a wax phantom. Finally, the effect of varying calibration phantom size on RSP was calculated in an anthropomorphic head phantom. The RSP decreased -0.002 cm^-1 as phantom size increased for SECT but remained largely constant when iBHC applied or with DECT techniques. The RSP varied a maximum of 2.60% in the presence of streaking BHA in SECT but was reduced to 1.40% with iBHC. For DECT techniques, the maximum difference was 2.40%, reduced to 0.6% with iBHC. Comparing calibration phantoms of 20- and 33-cm diameter, maximum voxel differences of 5 mm in the water-equivalent thickness were observed in the skull but reduced to 1.3 mm with iBHC. The DECT techniques excelled in mitigating cupping BHA, but streaking BHA still could be observed. The use of iBHC reduced RSP variation with BHA in both SECT and DECT techniques.

One-step iterative reconstruction approach based on eigentissue decomposition for spectral photon-counting computed tomography

Purpose: We propose a one-step tissue characterization method for spectral photon-counting computed tomography (SPCCT) using eigentissue decomposition (ETD), tailored for highly accurate human tissue characterization in radiotherapy. Methods: The approach combines a Poisson likelihood, a spatial prior, and a quantitative prior constraining eigentissue fractions based on expected values for tabulated tissues. There are two regularization parameters: α for the quantitative prior, and β for the spatial prior. The approach is validated in a realistic simulation environment for SPCCT. The impact of α and β is evaluated on a virtual phantom. The framework is tested on a virtual patient and compared with two sinogram-based two-step methods [using respectively filtered backprojection (FBP) and an iterative method for the second step] and a post-reconstruction approach with the same quantitative prior. All methods use ETD. Results: Optimal performance with respect to bias or RMSE is achieved with different combinations of α and β on the cylindrical phantom. Evaluated in tissues of the virtual patient, the one-step framework outperforms two-step and post-reconstruction approaches to quantify proton-stopping power (SPR). The mean absolute bias on the SPR is 0.6% (two-step FBP), 0.6% (two-step iterative), 0.6% (post-reconstruction), and 0.2% (one-step optimized for low bias). Following the same order, the RMSE on the SPR is 13.3%, 2.5%, 3.2%, and 1.5%. Conclusions: Accurate and precise characterization with ETD can be achieved with noisy SPCCT data without the need to rely on post-reconstruction methods. The one-step framework is more accurate and precise than two-step methods for human tissue characterization.

Range uncertainty reductions in proton therapy may lead to the feasibility of novel beam arrangements which improve organ-at-risk sparing

PURPOSE

In proton therapy, dose distributions are currently often conformed to organs at risk (OARs) using the less sharp dose fall-off at the lateral beam edge to reduce the effects of uncertainties in the in vivo proton range. However, range uncertainty reductions may make greater use of the sharper dose fall-off at the distal beam edge feasible, potentially improving OAR sparing. We quantified the benefits of such novel beam arrangements.

METHODS

For each of 10 brain or skull base cases, five treatment plans robust to 2 mm setup and 0%-4% range uncertainty were created for the traditional clinical beam arrangement and a novel beam arrangement making greater use of the distal beam edge to conform the dose distribution to the brainstem. Metrics including the brainstem normal tissue complication probability (NTCP) with the endpoint of necrosis were determined for all plans and all setup and range uncertainty scenarios.

RESULTS

For the traditional beam arrangement, reducing the range uncertainty from the current level of approximately 4% to a potentially achievable level of 1% reduced the brainstem NTCP by up to 0.9 percentage points in the nominal and up to 1.5 percentage points in the worst-case scenario. Switching to the novel beam arrangement at 1% range uncertainty improved these values by a factor of 2, that is, to 1.8 percentage points and 3.2 percentage points, respectively. The novel beam arrangement achieved a lower brainstem NTCP in all cases starting at a range uncertainty of 2%.

CONCLUSION

The benefits of novel beam arrangements may be of the same magnitude or even exceed the direct benefits of range uncertainty reductions. Indirect effects may therefore contribute markedly to the benefits of reducing proton range uncertainties.

Dual-energy CT based mass density and relative stopping power estimation for proton therapy using physics-informed deep learning

Proton therapy requires accurate dose calculation for treatment planning to ensure the conformal doses are precisely delivered to the targets. The conversion of CT numbers to material properties is a significant source of uncertainty for dose calculation. The aim of this study is to develop a physics-informed deep learning (PIDL) framework to derive accurate mass density and relative stopping power maps from dual-energy computed tomography (DECT) images. The PIDL framework allows deep learning (DL) models to be trained with a physics loss function, which includes a physics model to constrain DL models. Five DL models were implemented including a fully connected neural network (FCNN), dual-FCNN (DFCNN), and three variants of residual networks (ResNet): ResNet-v1 (RN-v1), ResNet-v2 (RN-v2), and dual-ResNet-v2 (DRN-v2). An artificial neural network (ANN) and the five DL models trained with and without physics loss were explored to evaluate the PIDL framework. Two empirical DECT models were implemented to compare with the PIDL method. DL training data were from CIRS electron density phantom 062M (Computerized Imaging Reference Systems, Inc., Norfolk, VA). The performance of DL models was tested by CIRS adult male, adult female, and 5-year-old child anthropomorphic phantoms. For density map inference, the physics-informed RN-v2 was 3.3%, 2.9% and 1.9% more accurate than ANN for the adult male, adult female, and child phantoms. The physics-informed DRN-v2 was 0.7%, 0.6%, and 0.8% more accurate than DRN-v2 without physics training for the three phantoms, respectfully. The results indicated that physics-informed training could reduce uncertainty when ANN/DL models without physics training were insufficient to capture data structures or derived significant errors. DL models could also achieve better image noise control compared to the empirical DECT parametric mapping methods. The proposed PIDL framework can potentially improve proton range uncertainty by offering accurate material properties conversion from DECT.

Improved accuracy of relative electron density and proton stopping power ratio through CycleGAN machine learning

Objective. Kilovoltage computed tomography (kVCT) is the cornerstone of radiotherapy treatment planning for delineating tissues and towards dose calculation. For the former, kVCT provides excellent contrast and signal-to-noise ratio. For the latter, kVCT may have greater uncertainty in determining relative electron density ( ρ e ) and proton stopping power ratio (SPR). Conversely, megavoltage CT (MVCT) may result in superior dose calculation accuracy. The purpose of this work was to convert kVCT HU to MVCT HU using deep learning to obtain higher accuracy ρ e and SPR. Approach. Tissue-mimicking phantoms were created to compare kVCT- and MVCT-determined ρ e and SPR to physical measurements. Using 100 head-and-neck datasets, an unpaired deep learning model was trained to learn the relationship between kVCTs and MVCTs, creating synthetic MVCTs (sMVCTs). Similarity metrics were calculated between kVCTs, sMVCTs, and MVCTs in 20 test datasets. An anthropomorphic head phantom containing bone-mimicking material with known composition was scanned to provide an independent determination of ρ e and SPR accuracy by sMVCT. Main results. In tissue-mimicking bone, ρ e errors were 2.20% versus 0.19% and SPR errors were 4.38% versus 0.22%, for kVCT versus MVCT, respectively. Compared to MVCT, in vivo mean difference (MD) values were 11 and 327 HU for kVCT and 2 and 3 HU for sMVCT in soft tissue and bone, respectively. ρ e MD decreased from 1.3% to 0.35% in soft tissue and 2.9% to 0.13% in bone, for kVCT and sMVCT, respectively. SPR MD decreased from 1.8% to 0.24% in soft tissue and 6.8% to 0.16% in bone, for kVCT and sMVCT, respectively. Relative to physical measurements, ρ e and SPR error in anthropomorphic bone decreased from 7.50% and 7.48% for kVCT to <1% for both MVCT and sMVCT. Significance. Deep learning can be used to map kVCT to sMVCT, suggesting higher accuracy ρ e and SPR is achievable with sMVCT versus kVCT.

Potential of a Second-Generation Dual-Layer Spectral CT for Dose Calculation in Particle Therapy Treatment Planning

In particle therapy treatment planning, dose calculation is conducted using patient-specific maps of tissue ion stopping power ratio (SPR) to predict beam ranges. Improving patient-specific SPR prediction is therefore essential for accurate dose calculation. In this study, we investigated the use of the Spectral CT 7500, a second-generation dual-layer spectral computed tomography (DLCT) system, as an alternative to conventional single-energy CT (SECT) for patient-specific SPR prediction. This dual-energy CT (DECT)-based method allows for the direct prediction of SPR from quantitative measurements of relative electron density and effective atomic number using the Bethe equation, whereas the conventional SECT-based method consists of indirect image data-based prediction through the conversion of calibrated CT numbers to SPR. The performance of the Spectral CT 7500 in particle therapy treatment planning was characterized by conducting a thorough analysis of its SPR prediction accuracy for both tissue-equivalent materials and common non-tissue implant materials. In both instances, DLCT was found to reduce uncertainty in SPR predictions compared to SECT. Mean deviations of 0.7% and 1.6% from measured SPR values were found for DLCT- and SECT-based predictions, respectively, in tissue-equivalent materials. Furthermore, end-to-end analyses of DLCT-based treatment planning were performed for proton, helium, and carbon ion therapies with anthropomorphic head and pelvic phantoms. 3D gamma analysis was performed with ionization chamber array measurements as the reference. DLCT-predicted dose distributions revealed higher passing rates compared to SECT-predicted dose distributions. In the DLCT-based treatment plans, measured distal-edge evaluation layers were within 1 mm of their predicted positions, demonstrating the accuracy of DLCT-based particle range prediction. This study demonstrated that the use of the Spectral CT 7500 in particle therapy treatment planning may lead to better agreement between planned and delivered dose compared to current clinical SECT systems.

Proton Therapy for Prostate Cancer: Challenges and Opportunities

Simple Summary

Reported clinical outcomes of proton therapy (PT) for localized prostate cancer are similar to photon-based external beam radiotherapy. Apparently, the dosimetric advantages of PT have yet to be translated to clinical benefits. The suboptimal clinical outcomes of PT might be attributable to inadequate dose prescription, as indicated by the ASCENDE-RT trial. Moreover, uncertainties involved in the treatment planning and delivery processes, as well as technological limitations in PT treatment systems, may lead to discrepancies between planned doses and actual doses delivered to patients. In this article, we reviewed the current status of PT for prostate cancer and discussed different clinical implementations that could potentially improve the clinical outcome of PT for prostate cancer. Various technological advancements under which uncertainties in dose calculations can be minimized, including MRI-guided PT, dual-energy photon-counting CT and high-resolution Monte Carlo-based treatment planning systems, are highlighted.

Abstract

The dosimetric advantages of proton therapy (PT) treatment plans are demonstrably superior to photon-based external beam radiotherapy (EBRT) for localized prostate cancer, but the reported clinical outcomes are similar. This may be due to inadequate dose prescription, especially in high-risk disease, as indicated by the ASCENDE-RT trial. Alternatively, the lack of clinical benefits with PT may be attributable to improper dose delivery, mainly due to geometric and dosimetric uncertainties during treatment planning, as well as delivery procedures that compromise the dose conformity of treatments. Advanced high-precision PT technologies, and treatment planning and beam delivery techniques are being developed to address these uncertainties. For instance, external magnetic resonance imaging (MRI)-guided patient setup rooms are being developed to improve the accuracy of patient positioning for treatment. In-room MRI-guided patient positioning systems are also being investigated to improve the geometric accuracy of PT. Soon, high-dose rate beam delivery systems will shorten beam delivery time to within one breath hold, minimizing the effects of organ motion and patient movements. Dual-energy photon-counting computed tomography and high-resolution Monte Carlo-based treatment planning systems are available to minimize uncertainties in dose planning calculations. Advanced in-room treatment verification tools such as prompt gamma detector systems will be used to verify the depth of PT. Clinical implementation of these new technologies is expected to improve the accuracy and dose conformity of PT in the treatment of localized prostate cancers, and lead to better clinical outcomes. Improvement in dose conformity may also facilitate dose escalation, improving local control and implementation of hypofractionation treatment schemes to improve patient throughput and make PT more cost effective.

Towards sub-percentage uncertainty proton stopping-power mapping via dual-energy CT: direct experimental validation and uncertainty analysis of a statistical iterative image reconstruction method

PURPOSE

To assess the potential of a joint dual-energy CT reconstruction process (Statistical Image Reconstruction method built on a Basis Vector Model (JSIR-BVM)) implemented on a 16-slice commercial CT scanner to measure high spatial-resolution stopping-power ratio (SPR) maps with uncertainties of less than 1%.

METHODS

JSIR-BVM was used to reconstruct images of effective electron density and mean excitation energy from dual-energy CT (DECT) sinograms for ten high-purity samples of known density and atomic composition inserted into head and body phantoms. The measured DECT data consisted of 90 kVp and 140 kVp axial sinograms serially acquired on a Philips Brilliance Big Bore CT scanner without beam-hardening corrections. The corresponding SPRs were subsequently measured directly via ion chamber measurements on a MEVION S250 superconducting synchrocyclotron and evaluated theoretically from the known sample compositions and densities. Deviations of JSIR-BVM SPR values from their theoretically calculated and directly measured ground-truth values were evaluated for our JSIR-BVM method and for our implementation of the Hünemohr-Saito (H-S) DECT image-domain decomposition technique for SPR imaging. A thorough uncertainty analysis was then performed for 5 different scenarios (comparison of JSIR-BVM SPR/SP to International Commission on Radiation Measurements and Units (ICRU) benchmarks; comparison of JSIR-BVM SPR to measured benchmarks; and uncertainties in JSIR-BVM SPR/SP maps for patients of unknown composition) per the Joint Committee for Guides in Metrology (JCGM) and the Guide to expression of Uncertainty in Measurement (GUM), including the impact of uncertainties in measured photon spectra, sample composition and density, photon cross-section and I-value models, and random measurement uncertainty. Estimated SPR uncertainty for three main tissue groups in patients of unknown composition and the weighted proportion of each tissue type for three proton treatment sites were then used to derive a composite range uncertainty for our method.

RESULTS

Mean JSIR-BVM SPR estimates deviated by less than 1% from their theoretical and directly measured ground-truth values for most inserts and phantom geometries except for high density Delrin and Teflon samples with SPR error relative to proton measurements of 1.1% and -1.0% (Head Phantom) and 1.1% and -1.1% (Body Phantom). The overall RMS deviations over all samples were 0.39% and 0.52% (head phantom) and 0.43% and 0.57% (body phantom) relative to theoretical and directly measured ground-truth SPRs, respectively. The corresponding RMS (maximum) errors for the image-domain decomposition method were 2.68% and 2.73% (4.68% and 4.99%) for the head phantom and 0.71% and 0.87% (1.37% and 1.66%) for the body phantom. Compared to H-S SPR maps, JSIR-BVM yielded 30% sharper and two-fold sharper images for soft tissues and bone-like surrogates, respectively, while reducing noise by factors of 6 and 3, respectively. The uncertainty (coverage factor k = 1) of the DECT-to-benchmark values comparison ranged from 0.5% to 1.5% and is dominated by scanning-beam photon-spectra uncertainties. An analysis of the SPR uncertainty for patients of unknown composition showed a JSIR-BVM uncertainty of 0.65%, 1.21%, and 0.77% for soft-, lung-, and bony-tissue groups which led to a composite range uncertainty of 0.6%-0.9%.

CONCLUSIONS

Observed JSIR-BVM SPR estimation errors were all less than 50% of the estimated k = 1 total uncertainty of our benchmarking experiment, demonstrating that JSIR-BVM high spatial-resolution, low-noise SPR mapping is feasible and is robust to variations in the geometry of the scanned object. In contrast, the much larger H-S SPR estimation errors are dominated by imaging noise and residual beam-hardening artifacts. While the uncertainties characteristic of our current JSIR-BVM implementation can be as large as 1.5%, achieving <1% total uncertainty is feasible by improving the accuracy of scanner-specific scatter-profile and photon-spectrum estimates. With its robustness to beam-hardening artifact, image noise and variations in phantom size and geometry, JSIR-BVM has the potential to achieve high spatial-resolution SPR mapping with sub-percentage accuracy and estimated uncertainty in the clinical setting. This article is protected by copyright. All rights reserved.

Dual-layer spectral CT for proton, helium, and carbon ion beam therapy planning of brain tumors

Pretreatment computed tomography (CT) imaging is an essential component of the particle therapy treatment planning chain. Treatment planning and optimization with charged particles require accurate and precise estimations of ion beam range in tissues, characterized by the stopping power ratio (SPR). Reduction of range uncertainties arising from conventional CT-number-to-SPR conversion based on single-energy CT (SECT) imaging is of importance for improving clinical practice. Here, the application of a novel imaging and computational methodology using dual-layer spectral CT (DLCT) was performed toward refining patient-specific SPR estimates. A workflow for DLCT-based treatment planning was devised to evaluate SPR prediction for proton, helium, and carbon ion beam therapy planning in the brain. DLCT- and SECT-based SPR predictions were compared in homogeneous and heterogeneous anatomical regions. This study included eight patients scanned for diagnostic purposes with a DLCT scanner. For each patient, four different treatment plans were created, simulating tumors in different parts of the brain. For homogeneous anatomical regions, mean SPR differences of about 1% between the DLCT- and SECT-based approaches were found. In plans of heterogeneous anatomies, relative (absolute) proton range shifts of 0.6% (0.4 mm) in the mean and up to 4.4% (2.1 mm) at the distal fall-off were observed. In the investigated cohort, 12% of the evaluated organs-at-risk (OARs) presented differences in mean or maximum dose of more than 0.5 Gy (RBE) and up to 6.8 Gy (RBE) over the entire treatment. Range shifts and dose differences in OARs between DLCT and SECT in helium and carbon ion treatment plans were similar to protons. In the majority of investigated cases (75th percentile), SECT- and DLCT-based range estimations were within 0.6 mm. Nonetheless, the magnitude of patient-specific range deviations between SECT and DLCT was clinically relevant in heterogeneous anatomical sites, suggesting further study in larger, more diverse cohorts. Results indicate that patients with brain tumors may benefit from DLCT-based treatment planning.

Experimental comparison of photon versus particle computed tomography to predict tissue relative stopping powers

PURPOSE

Measurements comparing relative stopping power (RSP) accuracy of state-of-the-art systems representing single-energy and dual-energy computed tomography (SECT/DECT) with proton CT (pCT) and helium CT (HeCT) in biological tissue samples.

METHODS

We used 16 porcine and bovine samples of various tissue types and water, covering an RSP range from 0.90 ± 0.06 to 1.78 ± 0.05. Samples were packed and sealed into 3D-printed cylinders ( d = 2  cm, h = 5  cm) and inserted into an in-house designed cylindrical polymethyl methacrylate (PMMA) phantom ( d = 10  cm, h = 10  cm). We scanned the phantom in a commercial SECT and DECT (120 kV; 100  and 140 kV/Sn (tin-filtered)); and acquired pCT and HeCT ( E ∼ 200  MeV/u, 2 ∘ steps, ∼ 6.2 × 10 6 (p)/ ∼ 2.3 × 10 6 (He) particles/projection) with a particle imaging prototype. RSP maps were calculated from SECT/DECT using stoichiometric methods and from pCT/HeCT using the DROP-TVS algorithm. We estimated the average RSP of each tissue per modality in cylindrical volumes of interest and compared it to ground truth RSP taken from peak-detection measurements.

RESULTS

Throughout all samples, we observe the following root-mean-squared RSP prediction errors ± combined uncertainty from reference measurement and imaging: SECT 3.10 ± 2.88%, DECT 0.75 ± 2.80%, pCT 1.19 ± 2.81%, and HeCT 0.78 ± 2.81%. The largest mean errors ± combined uncertainty per modality are SECT 8.22 ± 2.79% in cortical bone, DECT 1.74 ± 2.00% in back fat, pCT 1.80 ± 4.27% in bone marrow, and HeCT 1.37 ± 4.25% in bone marrow. Ring artifacts were observed in both pCT and HeCT reconstructions, imposing a systematic shift to predicted RSPs.

CONCLUSION

Comparing state-of-the-art SECT/DECT technology and a pCT/HeCT prototype, DECT provided the most accurate RSP prediction, closely followed by particle imaging. The novel modalities pCT and HeCT have the potential to further improve on RSP accuracies with work focusing on the origin and correction of ring artifacts. Future work will study accuracy of proton treatment plans using RSP maps from investigated imaging modalities.

A generalized simultaneous algebraic reconstruction technique (GSART) for dual-energy X-ray computed tomography

BACKGROUND

Dual-energy computed tomography (DECT) is a widely used and actively researched imaging modality that can estimate the physical properties of an object more accurately than single-energy CT (SECT). Recently, iterative reconstruction methods called one-step methods have received attention among various approaches since they can resolve the intermingled limitations of the conventional methods. However, the one-step methods typically have expensive computational costs, and their material decomposition performance is largely affected by the accuracy in the spectral coefficients estimation.

OBJECTIVE

In this study, we aim to develop an efficient one-step algorithm that can effectively decompose into the basis material maps and is less sensitive to the accuracy of the spectral coefficients.

METHODS

By use of a new loss function that employs the non-linear forward model and the weighted squared errors, we propose a one-step reconstruction algorithm named generalized simultaneous algebraic reconstruction technique (GSART). The proposed algorithm was compared with the image-domain material decomposition and other existing one-step reconstruction algorithm.

RESULTS

In both simulation and experimental studies, we demonstrated that the proposed algorithm effectively reduced the beam-hardening artifacts thereby increasing the accuracy in the material decomposition.

CONCLUSIONS

The proposed one-step reconstruction for material decomposition in dual-energy CT outperformed the image-domain approach and the existing one-step algorithm. We believe that the proposed method is a practically very useful addition to the material-selective image reconstruction field.

The accuracy of helium ion CT based particle therapy range prediction: an experimental study comparing different particle and x-ray CT modalities

This work provides a quantitative assessment of helium ion CT (HeCT) for particle therapy treatment planning. For the first time, HeCT based range prediction accuracy in a heterogeneous tissue phantom is presented and compared to single-energy x-ray CT (SECT), dual-energy x-ray CT (DECT) and proton CT (pCT). HeCT and pCT scans were acquired using the US pCT collaboration prototype particle CT scanner at the Heidelberg Ion-Beam Therapy Center. SECT and DECT scans were done with a Siemens Somatom Definition Flash and converted to RSP. A Catphan CTP404 module was used to study the RSP accuracy of HeCT. A custom phantom of 20 cm diameter containing several tissue equivalent plastic cubes was used to assess the spatial resolution of HeCT and compare it to DECT. A clinically realistic heterogeneous tissue phantom was constructed using cranial slices from a pig head placed inside a cylindrical phantom (ø150 mm). A proton beam (84.67 mm range) depth-dose measurement was acquired using a stack of GafchromicTM EBT-XD films in a central dosimetry insert in the phantom. CT scans of the phantom were acquired with each modality, and proton depth-dose estimates were simulated based on the reconstructions. The RSP accuracy of HeCT for the plastic phantom was found to be 0.3 ± 0.1%. The spatial resolution for HeCT of the cube phantom was 5.9 ± 0.4 lp cm-1for central, and 7.6 ± 0.8 lp cm-1for peripheral cubes, comparable to DECT spatial resolution (7.7 ± 0.3 lp cm-1and 7.4 ± 0.2 lp cm-1, respectively). For the pig head, HeCT, SECT, DECT and pCT predicted range accuracy was 0.25%, -1.40%, -0.45% and 0.39%, respectively. In this study, HeCT acquired with a prototype system showed potential for particle therapy treatment planning, offering RSP accuracy, spatial resolution, and range prediction accuracy comparable to that achieved with a commercial DECT scanner. Still, technical improvements of HeCT are needed to enable clinical implementation.

Reduction of clinical safety margins in proton therapy enabled by the clinical implementation of dual-energy CT for direct stopping-power prediction

PURPOSE

To quantifiy the range uncertainty in proton treatment planning using dual-energy computed tomography (DECT) for a direct stopping-power prediction (DirectSPR) algorithm and its clinical implementation.

METHODS AND MATERIALS

To assess the overall uncertainty in stopping-power ratio (SPR) prediction of a DirectSPR implementation calibrated for different patient geometries, the influencing factors were categorized in imaging, modeling as well as others. The respective SPR uncertainty was quantified for lung, soft tissue and bone and translated into range uncertainty for several tumor types. The amount of healthy tissue spared was quantified for 250 patients treated with DirectSPR and the dosimetric impact was evaluated exemplarily for a representative brain-tumor patient.

RESULTS

For bone, soft tissue and lung, an SPR uncertainty (1σ) of 1.6%, 1.3% and 1.3% was determined for DirectSPR, respectively. This allowed for a reduction of the clinically applied range uncertainty from currently (3.5% + 2 mm) to (1.7% + 2 mm) for brain-tumor and (2.0% + 2 mm) for prostate-cancer patients. The 150 brain-tumor and 100 prostate-cancer patients treated using DirectSPR benefitted from sparing on average 2.6 mm and 4.4 mm of healthy tissue in beam direction, respectively. In the representative patient case, dose reduction in organs at risk close to the target volume was achieved, with a mean dose reduction of up to 16% in the brainstem. Patient-specific DECT-based treatment planning with reduced safety margins was successfully introduced into clinical routine.

CONCLUSIONS

A substantial increase in range prediction accuracy in clinical proton treatment planning was achieved by patient-specific DECT-based SPR prediction. For the first time, a relevant imaging-based reduction of range prediction uncertainty on a 2% level has been achieved.

Improving radiation physics, tumor visualisation, and treatment quantification in radiotherapy with spectral or dual-energy CT

Over the past decade, spectral or dual‐energy CT has gained relevancy, especially in oncological radiology. Nonetheless, its use in the radiotherapy (RT) clinic remains limited. This review article aims to give an overview of the current state of spectral CT and to explore opportunities for applications in RT.

In this article, three groups of benefits of spectral CT over conventional CT in RT are recognized. Firstly, spectral CT provides more information of physical properties of the body, which can improve dose calculation. Furthermore, it improves the visibility of tumors, for a wide variety of malignancies as well as organs‐at‐risk OARs, which could reduce treatment uncertainty. And finally, spectral CT provides quantitative physiological information, which can be used to personalize and quantify treatment.

A Beam-Specific Optimization Target Volume for Stereotactic Proton Pencil Beam Scanning Therapy for Locally Advanced Pancreatic Cancer

PURPOSE

We investigate two margin-based schemes for optimization target volumes (OTV), both isotropic expansion (2 mm) and beam-specific OTV, to account for uncertainties due to the setup errors and range uncertainties in pancreatic stereotactic pencil beam scanning (PBS) proton therapy. Also, as 2-mm being one of the extreme sizes of margin, we also study whether the plan quality of 2-mm uniform expansion could be comparable to other plan schemes.

METHODS AND MATERIALS

We developed 2 schemes for OTV: (1) a uniform expansion of 2 mm (OTV2mm) for setup uncertainty and (2) a water equivalent thickness-based, beam-specific expansion (OTVWET) on beam direction and 2 mm expansion laterally. Six LAPC patients were planned with a prescribed dose of 33 Gy (RBE) in 5 fractions. Robustness optimization (RO) plans on gross tumor volumes, with setup uncertainties of 2 mm and range uncertainties of 3.5%, were implemented as a benchmark.

RESULTS

All 3 optimization schemes achieved decent target coverage with no significant difference. The OTV2mm plans show superior organ at risk (OAR) sparing, especially for proximal duodenum. However, OTV2mm plans demonstrate severe susceptibility to range and setup uncertainties with a passing rate of 19% of the plans meeting the goal of 95% volume covered by the prescribed dose. The proposed dose spread function analysis shows no significant difference.

CONCLUSIONS

The use of OTVWET mimics a union volume for all scenarios in robust optimization but saves optimization time noticeably. The beam-specific margin can be attractive to online adaptive stereotactic body proton therapy owing to the efficiency of the plan optimization.

Investigate the Dosimetric and Potential Clinical Benefits Utilizing Stereotactic Body Radiation Therapy With Simultaneous Integrated Boost Technique for Locally Advanced Pancreatic Cancer: A Comparison Between Photon and Proton Beam Therapy

Purpose

To investigate the potential clinical benefits of using stereotactic body radiation therapy (SBRT) with simultaneous integrated boost (SIB) technique for locally advanced pancreatic cancer (LAPC) among different treatment modalities and planning strategies, including photon and proton.

Method

A total of 19 patients were retrospectively selected in this study: 13 cases with the tumor located in the head of the pancreas and 6 cases with the tumor in the body of the pancreas. SBRT-SIB plans were generated using volumetric modulated arc therapy (VMAT), two-field Intensity Modulated Proton Therapy (IMPT), and three-field IMPT. The IMPT used the robust optimization parameters of ± 3.5% range and 5-mm setup uncertainties. Root-mean-square deviation dose (RMSD) volume histograms were used to evaluate the target coverage robustness quantitatively. Dosimetric metrics based on the dose-volume histogram (DVH), homogeneity index (HI), and normal tissue complication probability (NTCP) were analyzed to evaluate the potential clinical benefits among different planning groups.

Results

With a similar CTV and SIB coverage, two-field IMPT provided a lower maximum dose for the stomach (median: 18.6GyE, p<0.05) and duodenum (median: 32.62GyE, p<0.05) when the target was located in the head of the pancreas compared to VMAT and three-field IMPT. The risks of gastric bleed (3.42%) and grade ≥ 3 GI toxicity (4.55%) were also decreased. However, for the target in the body of the pancreas, VMAT showed a lower maximum dose for the stomach (median 30.93GyE, p<0.05) and toxicity of gastric bleed (median: 8.67%, p<0.05) compared to two-field IMPT and three-field IMPT, while other maximum doses and NTCPs were similar. The RMSD volume histogram (RVH) analysis shows that three-field IMPT provided better robustness for targets but not for OARs. Instead, three-field IMPT increased the Dmean of organs such as the stomach, duodenum, and intestine.

Conclusion

The results indicated that the tumor locations could play a critical role in determining clinical benefits among different treatment modalities. Two-field IMPT could be a better option for LAPC patients whose tumors are located in the head of the pancreas. It provides lower severe toxicity for the stomach and duodenum. Nevertheless, VMAT is preferred for the body with better protection for the possibility of gastric bleed.

Proton range uncertainty reduction benefits for skull base tumors in terms of normal tissue complication probability (NTCP) and healthy tissue doses

PURPOSE

Proton therapy allows for more conformal dose distributions and lower organ at risk and healthy tissue doses than conventional photon-based radiotherapy, but uncertainties in the proton range currently prevent proton therapy from making full use of these advantages. Numerous developments therefore aim to reduce such range uncertainties. In this work, we quantify the benefits of reductions in range uncertainty for treatments of skull base tumors.

METHODS

The study encompassed 10 skull base patients with clival tumors. For every patient, six treatment plans robust to setup errors of 2 mm and range errors from 0% to 5% were created. The determined metrics included the brainstem and optic chiasm normal tissue complication probability (NTCP) with the endpoints of necrosis and blindness, respectively, as well as the healthy tissue volume receiving at least 70% of the prescription dose.

RESULTS

A range uncertainty reduction from the current level of 4% to a potentially achievable level of 1% reduced the probability of brainstem necrosis by up to 1.3 percentage points in the nominal scenario in which neither setup nor range errors occur and by up to 2.9 percentage points in the worst-case scenario. Such a range uncertainty reduction also reduced the optic chiasm NTCP with the endpoint of blindness by up to 0.9 percentage points in the nominal scenario and by up to 2.2 percentage points in the worst-case scenario. The decrease in the healthy tissue volume receiving at least 70% of the prescription dose ranged from -7.8 to 24.1 cc in the nominal scenario and from -3.4 to 38.4 cc in the worst-case scenario.

CONCLUSION

The benefits quantified as part of this study serve as a guideline of the OAR and healthy tissue dose benefits that range monitoring techniques may be able to achieve. Benefits were observed between all levels of range uncertainty. Even smaller range uncertainty reductions may therefore be beneficial.

Assessment of the impact of CT calibration procedures for proton therapy planning on pediatric treatments

PURPOSE

Relative stopping powers (RSPs) for proton therapy are estimated using single-energy computed tomography (SECT), calibrated with standardized tissues of the adult male. It is assumed that those tissues are representative of tissues of all age and sex. Female, male, and pediatric tissues differ from one another in density and composition. In this study, we use tabulated pediatric tissues and computational phantoms to investigate the impact of this assumption on pediatric proton therapy. The potential of dual-energy CT (DECT) to improve the accuracy of these calculations is explored.

METHODS

We study 51 human body tissues, categorized into male/female for the age groups newborn, 1-, 5-, 10-, and 15-year-old children, and adult, with given compositions and densities. CT numbers are simulated and RSPs are estimated using SECT and DECT methods. Estimated tissue RSPs from each method are compared to theoretical RSPs. The dose and range errors of each approach are evaluated on three computational phantoms (Ewing's sarcoma, salivary sarcoma, and glioma) derived from pediatric proton therapy patients.

RESULTS

With SECT, soft tissues have mean estimation errors and standard deviation up to (1.96 ± 4.18)% observed in newborns, compared to (0.20 ± 1.15)% in adult males. Mean estimation errors for bones are up to (-3.35 ± 4.76)% in pediatrics as opposed to (0.10 ± 0.66)% in adult males. With DECT, mean errors reduce to (0.17 ± 0.13)% and (0.23 ± 0.22)% in newborns (soft tissues/bones). With SECT, dose errors in a Ewing's sarcoma phantom are exceeding 5 Gy (10% of prescribed dose) at the distal end of the treatment field, with volumes of dose errors >5 Gy ofV diff > 5 = 4630.7  mm3 . Similar observations are made in the head and neck phantoms, with overdoses to healthy tissue exceeding 2 Gy (4%). A systematic Bragg peak shift resulting in either over- or underdosage of healthy tissues and target volumes depending on the crossed tissues RSP prediction errors is observed. Water equivalent range errors of single beams are between -1.53 and 5.50 mm (min, max) (Ewing's sarcoma phantom), -0.78 and 3.62 mm (salivary sarcoma phantom), and -0.43 and 1.41 mm (glioma phantom). DECT can reduce dose errors to <1 Gy and range errors to <1 mm.

CONCLUSION

Single-energy computed tomography estimates RSPs for pediatric tissues with systematic shifts. DECT improves the accuracy of RSPs and dose distributions in pediatric tissues compared to the SECT calibration curve based on adult male tissues.

Experimental assessment of inter-centre variation in stopping-power and range prediction in particle therapy

PURPOSE

Experimental assessment of inter-centre variation and absolute accuracy of stopping-power-ratio (SPR) prediction within 17 particle therapy centres of the European Particle Therapy Network.

MATERIAL AND METHODS

A head and body phantom with seventeen tissue-equivalent materials were scanned consecutively at the participating centres using their individual clinical CT scan protocol and translated into SPR with their in-house CT-number-to-SPR conversion. Inter-centre variation and absolute accuracy in SPR prediction were quantified for three tissue groups: lung, soft tissues and bones. The integral effect on range prediction for typical clinical beams traversing different tissues was determined for representative beam paths for the treatment of primary brain tumours as well as lung and prostate cancer.

RESULTS

An inter-centre variation in SPR prediction (2σ) of 8.7%, 6.3% and 1.5% relative to water was determined for bone, lung and soft-tissue surrogates in the head setup, respectively. Slightly smaller variations were observed in the body phantom (6.2%, 3.1%, 1.3%). This translated into inter-centre variation of integral range prediction (2σ) of 2.9%, 2.6% and 1.3% for typical beam paths of prostate-, lung- and primary brain-tumour treatments, respectively. The absolute error in range exceeded 2% in every fourth participating centre. The consideration of beam hardening and the execution of an independent HLUT validation had a positive effect, on average.

CONCLUSION

The large inter-centre variations in SPR and range prediction justify the currently clinically used margins accounting for range uncertainty, which are of the same magnitude as the inter-centre variation. This study underlines the necessity of higher standardisation in CT-number-to-SPR conversion.

Accuracy of proton stopping power estimation of silicone breast implants with single and dual-energy CT calibration techniques

A major contributing factor to proton range uncertainty is the conversion of computed tomography (CT) Hounsfield Units (HU) to proton relative stopping power (RSP). This uncertainty is elevated with implanted devices, such as silicone breast implants when computed with single energy CT (SECT). In recent years, manufacturers have introduced implants with variations in gel cohesivity. Deriving the RSP for these implants from dual-energy CT (DECT) can result in a marked reduction of the error associated with SECT. In this study, we investigate the validity of DECT calibration of HU to RSP on silicone breast implants of varying cohesivity levels. A DECT capable scanner was calibrated using the stoichiometric method of Bourque et al for SECT and DECT using a tissue substitute phantom. Three silicone breast implants of increasing gel cohesivity were measured in a proton beam of clinical energy to determine ground-truth RSP and water equivalent thickness (WET). These were compared to SECT-derived RSP at three CT spectrum energies and DECT with two energy pairs (80/140 kVp and 100/140 kVp) as obtained from scans with and without an anthropomorphic phantom. The RSP derived from parameters estimates from CT vendor-specific software (syngo.via) was compared. The WET estimates from SECT deviated from MLIC ground truth approximately +11%-19%, which would result in overpenetration if used clinically. Both the Bourque calibration and syngo.via WET estimates from DECT yielded error ≤0.5% from ground truth; no significant difference was found between models of varying gel cohesivity levels. WET estimates without the anthropomorphic phantom were significantly different than ground truth for the Bourque calibration. From these results, gel cohesivity had no effect on proton RSP. User-generated DECT calibration can yield comparably accurate RSP estimates for silicone breast implants to vendor software methods. However, care must be taken to account for beam hardening effects.

Proton stopping power prediction based on dual-energy CT-generated virtual monoenergetic images

PURPOSE

The purpose of this work was to assess a proof of concept for a novel method for predicting proton stopping power ratios (SPRs) based on a pair of dual-energy CT generated virtual monoenergetic (VM) images.

MATERIALS AND METHODS

A rapid kV-switching dual-energy CT scanner was used to acquire Gemstone Spectral Imaging (GSI) and 120 kV conventional single-energy CT (SECT) image data of the CIRS 062M phantom. The proposed method was applied to every possible pairing of VM images between 40 and 140 keV to find the optimal energy pairs for SPR prediction in lung tissue, soft tissue, and bone. The predicted SPRs were compared against SPRs predicted from the SECT data using the conventional SECT-based method. The impact of different scan and reconstruction parameters was also investigated.

RESULTS

The SPR residual root mean square errors (RMSE) yielded by the optimal pairs were 7.2% for lung tissue, 0.4% for soft tissue, and 0.8% for bone. While no direct comparison could be made to other DECT-based methods for SPR prediction, as these methods could not be directly implemented on a fast kV-switching system, the SPR RMSEs for soft tissue and bone in Table 4 are comparable to RMSEs reported in the literature. For the conventional SECT-based method, the SPR RMSEs were 5.9% for lung tissue, 0.9% for soft tissue, and 5.1% for bone.

CONCLUSIONS

The proposed method is a valid alternative to, and has the potential to improve upon, the conventional SECT-based method for predicting SPRs. The formalism used in the method is applied directly, with no approximations made on our part, and requires neither prior knowledge of the spectra nor calibration with a phantom. This work presents a way of optimizing the proposed method for a specific scanner by determining the optimal energy pairs to use as input and demonstrates the method's robustness to different levels of ASiR-V, reconstruction kernels, and dose levels.

Dual-Energy Computed Tomography Proton-Dose Calculation with Scripting and Modified Hounsfield Units

Purpose

To describe an implementation of dual-energy computed tomography (DECT) for calculation of proton stopping-power ratios (SPRs) in a commercial treatment-planning system. The process for validation and the workflow for safe deployment of DECT is described, using single-energy computed tomography (SECT) as a safety check for DECT dose calculation.

Materials and Methods

The DECT images were acquired at 80 kVp and 140 kVp and were processed with computed tomography scanner software to derive the electron density and effective atomic number images. Reference SPRs of tissue-equivalent plugs from Gammex (Middleton, Wisconsin) and CIRS (Computerized Imaging Reference Systems, Norfolk, Virginia) electron density phantoms were used for validation and comparison of SECT versus DECT calculated through the Eclipse treatment planning system (Varian Medical Systems, Palo Alto, California) application programming interface scripting tool. An in-house software was also used to create DECT SPR computed tomography images for comparison with the script output. In the workflow, using the Eclipse system application programming interface script, clinical plans were optimized with the SECT image set and then forward-calculated with the DECT SPR for the final dose distribution. In a second workflow, the plans were optimized using DECT SPR with reduced range-uncertainty margins.

Results

For the Gammex phantom, the root mean square error in SPR was 1.08% for DECT versus 2.29% for SECT for 10 tissue-surrogates, excluding the lung. For the CIRS Phantom, the corresponding results were 0.74% and 2.27%. When evaluating the head and neck plan, DECT optimization with 2% range-uncertainty margins achieved a small reduction in organ-at-risk doses compared with that of SECT plans with 3.5% range-uncertainty margins. For the liver case, DECT was used to identify and correct the lipiodol SPR in the SECT plan.

Conclusion

It is feasible to use DECT for proton-dose calculation in a commercial treatment planning system in a safe manner. The range margins can be reduced to 2% in some sites, including the head and neck.

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.

Advances in radiotherapy technology for pediatric cancer patients and roles of medical physicists: COG and SIOP Europe perspectives

Over the last two decades, rapid technological advances have dramatically changed radiation delivery to children with cancer, enabling improved normal-tissue sparing. This article describes recent advances in photon and proton therapy technologies, image-guided patient positioning, motion management, and adaptive therapy that are relevant to pediatric cancer patients. For medical physicists who are at the forefront of realizing the promise of technology, challenges remain with respect to ensuring patient safety as new technologies are implemented with increasing treatment complexity. The contributions of medical physicists to meeting these challenges in daily practice, in the conduct of clinical trials, and in pediatric oncology cooperative groups are highlighted. Representing the perspective of the physics committees of the Children's Oncology Group (COG) and the European Society for Paediatric Oncology (SIOP Europe), this paper provides recommendations regarding the safe delivery of pediatric radiotherapy. Emerging innovations are highlighted to encourage pediatric applications with a view to maximizing the therapeutic ratio.

Dose calculation in proton therapy using a discovery cross-domain generative adversarial network (DiscoGAN)

PURPOSE

Accurate dose calculation is a critical step in proton therapy. A novel machine learning-based approach was proposed to achieve comparable accuracy to that of Monte Carlo simulation while reducing the computational time.

METHODS

Computed tomography-based patient phantoms were used and three treatment sites were selected (thorax, head, and abdomen), comprising different beam pathways and beam energies. The training data were generated using Monte Carlo simulations. A discovery cross-domain generative adversarial network (DiscoGAN) was developed to perform the mapping between two domains: stopping power and dose, with HU values from CT images incorporated as auxiliary features. The accuracy of dose calculation was quantitatively evaluated in terms of mean relative error (MRE) and mean absolute error (MAE). The relationship between the DiscoGAN performance and other factors such as absolute dose, beam energy and location within the beam cross-section (center and off-center lines) was examined.

RESULTS

The DiscoGAN model is found to be effective in dose calculation. For the abdominal case, the MRE is found to 1.47% (mean), 3.30% (maximum) and 0.67% (minimum). For the thoracic case, the MRE is found to ~2.43% (mean), 4.80% (maximum) and 0.71% (minimum). For the head case, the MRE is found to ~2.83% (mean), 4.84% (maximum) and 1.01% (minimum). Comparable accuracy is found in the independent validation dataset (different CT images), achieving a mean MRE of ~1.65% (thorax), 4.02% (head) and 1.64% (abdomen). For the energy span between 80 and 130 MeV, no strong dependency of accuracy on beam energy is found. The results imply that no systematic deviation, either over-dose or under-dose, occurs between the predicted dose and raw dose.

CONCLUSION

The DiscoGAN framework demonstrates great potential as a tool for dose calculation in proton therapy, achieving comparable accuracy yet being more efficient relative to Monte Carlo simulation. Its comparison with the pencil beam algorithm (PBA) will be the next step of our research. If successful, our proposed approach is expected to find its use in more advanced applications such as inverse planning and adaptive proton therapy.

Synthetic dual-energy CT for MRI-only based proton therapy treatment planning using label-GAN

MRI-only treatment planning is highly desirable in the current proton radiation therapy workflow due to its appealing advantages such as bypassing MR-CT co-registration, avoiding x-ray CT exposure dose and reduced medical cost. However, MRI alone cannot provide stopping power ratio (SPR) information for dose calculations. Given that dual energy CT (DECT) can estimate SPR with higher accuracy than conventional single energy CT, we propose a deep learning-based method in this study to generate synthetic DECT (sDECT) from MRI to calculate SPR. Since the contrast difference between high-energy and low-energy CT (LECT) is important, and in order to accurately model this difference, we propose a novel label generative adversarial network-based model which can not only discriminate the realism of sDECT but also differentiate high-energy CT (HECT) and LECT from DECT. A cohort of 57 head-and-neck cancer patients with DECT and MRI pairs were used to validate the performance of the proposed framework. The results of sDECT and its derived SPR maps were compared with clinical DECT and the corresponding SPR, respectively. The mean absolute error for synthetic LECT and HECT were 79.98 ± 18.11 HU and 80.15 ± 16.27 HU, respectively. The corresponding SPR maps generated from sDECT showed a normalized mean absolute error as 5.22% ± 1.23%. By comparing with the traditional Cycle GANs, our proposed method significantly improves the accuracy of sDECT. The results indicate that on our dataset, the sDECT image form MRI is close to planning DECT, and thus shows promising potential for generating SPR maps for proton therapy.

Feasibility of using post-contrast dual-energy CT for pediatric radiation treatment planning and dose calculation

OBJECTIVES

When iodinated contrast is administered during CT simulation, standard practice requires a separate non-contrast CT for dose calculation. The objective of this study is to validate our hypothesis that since iodine affects Hounsfield units (HUs) more than electron density (ED), the information from post-contrast dual-layer CT (DLCT) would be sufficient for accurate dose calculation for both photon and proton therapy.

METHODS AND MATERIALS

10 pediatric patients with abdominal tumors underwent DLCT scans before and after iodinated contrast administration for radiotherapy planning. Dose distributions with these DLCT-based methods were compared to those with conventional calibration-curve methods that map HU images to ED and stopping-power ratio (SPR) images.

RESULTS

For photon plans, conventional and DLCT approaches based on post-contrast scans underestimated the PTV D99 by 0.87 ± 0.70% (p = 0.18) and 0.36 ± 0.31% (p = 0.34), respectively, comparing to their non-contrast optimization plans. Renal iodine concentration was weakly associated with D99 deviation for both conventional (R² = 0.10) and DLCT (R² = 0.02) approaches. For proton plans, the clinical target volume D99 errors were 3.67 ± 2.43% (p = 0.0001) and 0.30 ± 0.25% (p = 0.40) for conventional and DLCT approaches, respectively. The proton beam range changed noticeably with the conventional approach. Renal iodine concentration was highly associated with D99 deviation for the conventional approach (R² = 0.83) but not for DLCT (R² = 0.007).

CONCLUSION

Conventional CT with iodine contrast resulted in a large dosimetric error for proton therapy, compared to true non-contrast plans, but the error was less for photon therapy. These errors can be greatly reduced in the case of the proton plans if DLCT is used, raising the possibility of using only a single post-contrast CT for radiotherapy dose calculation, thus reducing the time and imaging dose required.

ADVANCES IN KNOWLEDGE

This study is the first to compare directly the differences in the calculated dose distributions between pre- and post-contrast CT images generated by single-energy CT and dual-energy CT methods for photon and proton therapy.

Helium radiography with a digital tracking calorimeter-a Monte Carlo study for secondary track rejection

Radiation therapy using protons and heavier ions is a fast-growing therapeutic option for cancer patients. A clinical system for particle imaging in particle therapy would enable online patient position verification, estimation of the dose deposition through range monitoring and a reduction of uncertainties in the calculation of the relative stopping power of the patient. Several prototype imaging modalities offer radiography and computed tomography using protons and heavy ions. A Digital Tracking Calorimeter (DTC), currently under development, has been proposed as one such detector. In the DTC 43 longitudinal layers of laterally stacked ALPIDE CMOS monolithic active pixel sensor chips are able to reconstruct a large number of simultaneously recorded proton tracks. In this study, we explored the capability of the DTC for helium imaging which offers favorable spatial resolution over proton imaging. Helium ions exhibit a larger cross section for inelastic nuclear interactions, increasing the number of produced secondaries in the imaged object and in the detector itself. To that end, a filtering process able to remove a large fraction of the secondaries was identified, and the track reconstruction process was adapted for helium ions. By filtering on the energy loss along the tracks, on the incoming angle and on the particle ranges, 97.5% of the secondaries were removed. After passing through 16 cm water, 50.0% of the primary helium ions survived; after the proposed filtering 42.4% of the primaries remained; finally after subsequent image reconstruction 31% of the primaries remained. Helium track reconstruction leads to more track matching errors compared to protons due to the increased available focus strength of the helium beam. In a head phantom radiograph, the Water Equivalent Path Length error envelope was 1.0 mm for helium and 1.1 mm for protons. This accuracy is expected to be sufficient for helium imaging for pre-treatment verification purposes.