Comprehensive analysis of proton range uncertainties related to patient stopping-power-ratio estimation using the stoichiometric calibration

Proton therapy of lung cancer patients - Treatment strategies and clinical experience from a medical physicist's perspective

PURPOSE

Proton therapy of moving targets is considered a challenge. At Maastro, we started treating lung cancer patients with proton therapy in October 2019. In this work, we summarise the developed treatment strategies and gained clinical experience from a physics point of view.

METHODS

We report on our clinical approaches to treat lung cancer patients with the Mevion Hyperscan S250i proton machine. We classify lung cancer patients as small movers (tumour movement ≤ 5 mm) or large movers (tumour movement > 5 mm). The preferred beam configuration has evolved over the years of clinical treatment, and currently mostly two or three beam directions are used. All patients are treated with robustly optimised plans (5 mm setup and 3% range uncertainty). Small movers are planned based on a clinical target volume (CTV) with a 3 mm isotropic margin expansion to account for motion, while large movers are planned based on an internal target volume (ITV). All patients are treated in free-breathing.

RESULTS

Between October 2019 and December 2023, 379 lung cancer patients have been treated, of which 130 were large movers. The adaptation rate was 28%. The median treatment time has been reduced from 30 to 23 min. The mean dose to the heart, oesophagus, and lungs was on average 4.3, 15.4, and 11.0 Gy, respectively.

CONCLUSIONS

Several treatment planning and workflow improvements have been introduced over the years, resulting in an increase of treatment quality and number of treated patients, as well as reduction of planning and treatment time.

Assessing the robustness of dose distributions in carbon ion prostate radiotherapy using a fast dose evaluation system

PURPOSE

We developed a software program for swiftly calculating dose distributions for carbon ion beams. This study aims to evaluate the accuracy of dose calculations using this software and assess the robustness of dose distribution in treating prostate cancer.

METHODS

At the Osaka Heavy Ion Therapy Center, markers are inserted into the prostate gland and used for position verification. To account for geometric changes along the beam path due to marker translation, a beam-specific planning target volume (bsPTV) is set for each beam. To validate the accuracy of the dose calculations using the developed software, dose distributions for prostate and sarcoma cases were calculated and compared with the treatment planning system. To assess the robustness of the dose distribution, position verification data from 346 cases were utilized to reproduce dose distributions for three matching methods: bone matching, widely adopted in most particle therapy centers; marker translation, which involves direct translation to markers without bone matching; and marker translation after bone matching. The coverage of the target (D99 of clinical target volume (CTV)) was assessed to evaluate the robustness of the dose distribution. Additionally, statistical analyses were conducted for the dose distributions of each matching method.

RESULTS

The dose calculation for a single condition can be completed very quickly. Statistical analysis revealed significant differences among dose distributions considering the three matching methods. When irradiation was performed with bone matching only, the D99 was reduced by more than 10% in approximately 7.5% of cases, making it as the poorest among the three matching methods. However, there was no significant reduction in target coverage with the other two methods.

CONCLUSION

We have demonstrated the accuracy of the developed software for rapidly calculating dose distributions for carbon ion beams and confirmed the robustness of the dose distributions based on the bsPTV.

Dual virtual non-contrast imaging: a Bayesian quantitative approach to determine radiotherapy quantities from contrast-enhanced DECT images

Objective.Contrast agents in computed tomography (CT) scans can compromise the accuracy of dose calculations in radiation therapy planning, especially for particle therapy. This often requires an additional non-contrast CT scan, increasing radiation exposure and introducing potential registration errors. Our goal is to resolve these issues by accurately estimating radiotherapy parameters from dual virtual non-contrast (dual-VNC) images generated by contrast-enhanced dual-energy CT (DECT) scans, while accounting for noise and variability in tissue composition.Approach.A new Bayesian model is introduced to estimate dual-VNC Hounsfield units from contrast-enhanced DECT data. The model defines a prior distribution that describes tissue variations in terms of elemental compositions and mass densities. Multiple reference tissues are used to estimate variations across human tissues. A likelihood distribution is also defined to model the noise contained in CT data. The model is thoroughly validated in a simulated environment including 12 virtual patients under low and high iodine uptake scenarios, while incorporating noise and beam hardening effects. The eigentissue decomposition technique is used to derive elemental compositions and parameters critical for radiotherapy from the dual-VNC images, such as electron density (ρe), particle stopping power (SPR), and photon energy absorption coefficient (EAC).Main results.The proposed method yields accurate voxelwise estimations forρe, SPR, and EAC, with root mean square errors of 3.09%, 3.14%, and 1.34% for highly-enhanced tissues, compared to 5.93%, 6.39%, and 17.11% when the presence of contrast agent is ignored. It also demonstrates robustness to systematic shifts in tissue composition and bandwidth variations in the prior distribution, resulting in overall uncertainties down to 1.13%, 1.33%, and 0.86% forρe, SPR, and EAC in soft tissues; 1.17%, 1.32%, and 1.34% in enhanced soft tissues; and 4.34%, 4.00%, and 2.50% in bones.Significance.The proposed method accurately derives radiotherapy parameters from contrast-enhanced DECT data and demonstrates robustness against systematic errors in reference data, highlighting its potential for clinical use.

Clinical advantages of incorporating predicted weekly anatomy in IMPT optimization with reduced setup error

BACKGROUND

In head and neck (H&N) cancer treatment, a conventional setup error (SE) of 3mm is often used in robust optimization (cRO3mm). However, cRO3mm may lead to excessive radiation doses to organs at risk (OARs) and does not purposefully compensate for interfractional anatomy variations.

PURPOSE

This study introduces a method using predicted images from an anatomical model and a reduced 1mm SE uncertainty for robust optimization (aRO1mm), aiming to decrease the dose to OARs without affecting the coverage of the clinical target volume (CTV).

METHODS

This retrospective study involved 10 nasopharynx radiotherapy patients. Validation CT scans (vCT) from treatment weeks 1 to 6 were analyzed. A predictive anatomical model, designed to capture the average anatomical changes over time, provided predicted CT images for weeks 1, 3, and 5. We compared three optimization scenarios: (1) aRO1mm, using three predicted images with 1mm setup shift and 3% range uncertainty, (2) cRO3mm, with a robust 3mm setup shift and 3% range uncertainty, and (3) cRO1mm, a robust 1mm setup shift and 3% range uncertainty. The accumulated dose to CTVs and serial organs was evaluated under these uncertainties, while parallel OARs were assessed using the accumulated nominal dose (without errors).

RESULTS

The accumulated volume receiving 94% of the prescribed dose (V94) for CTVs in cRO3mm exceeded 98%, meeting the clinical goal. For high-risk CTV, the minimum V94 was 96.44% in aRO1mm and 94.05% in cRO1mm. For low-risk CTV, these values were 97.68% in aRO1mm and 97.15% in cRO1mm. When comparing aRO1mm to cRO3mm on OARs, aRO1mm reduced normal tissue complication probability (NTCP) for grade ≥ $\ge$ 2 xerostomia and dysphagia by averages of 3.67% and 1.54%, respectively.

CONCLUSION

aRO1mm lowers the radiation dose to OARs compared to the traditional approach, while maintaining adequate dose coverage on the target area. This method offers an improved strategy for managing uncertainties in radiation therapy planning for H&N cancer, enhancing treatment effectiveness.

Deep learning estimation of proton stopping power with photon-counting computed tomography: a virtual study

Purpose

Proton radiation therapy may achieve precise dose delivery to the tumor while sparing non-cancerous surrounding tissue, owing to the distinct Bragg peaks of protons. Aligning the high-dose region with the tumor requires accurate estimates of the proton stopping power ratio (SPR) of patient tissues, commonly derived from computed tomography (CT) image data. Photon-counting detectors for CT have demonstrated advantages over their energy-integrating counterparts, such as improved quantitative imaging, higher spatial resolution, and filtering of electronic noise. We assessed the potential of photon-counting computed tomography (PCCT) for improving SPR estimation by training a deep neural network on a domain transform from PCCT images to SPR maps.

Approach

The XCAT phantom was used to simulate PCCT images of the head with CatSim, as well as to compute corresponding ground truth SPR maps. The tube current was set to 260 mA, tube voltage to 120 kV, and number of view angles to 4000. The CT images and SPR maps were used as input and labels for training a U-Net.

Results

Prediction of SPR with the network yielded average root mean square errors (RMSE) of 0.26% to 0.41%, which was an improvement on the RMSE for methods based on physical modeling developed for single-energy CT at 0.40% to 1.30% and dual-energy CT at 0.41% to 3.00%, performed on the simulated PCCT data.

Conclusions

These early results show promise for using a combination of PCCT and deep learning for estimating SPR, which in extension demonstrates potential for reducing the beam range uncertainty in proton therapy.

Robust Optimization for Spot-Scanning Proton Therapy based on Dose-Linear-Energy-Transfer Volume Constraints

PURPOSE

Historically, spot-scanning proton therapy (SSPT) treatment planning uses dose-volume constraints and linear-energy-transfer (LET) volume constraints separately to balance tumor control and organs-at-risk (OARs) protection. We propose a novel dose-LET-volume constraint (DLVC)-based robust optimization (DLVCRO) method for SSPT in treating prostate cancer to obtain a desirable joint dose and LET distribution to minimize adverse events.

METHODS AND MATERIALS

DLVCRO treats DLVC as soft constraints that control the shapes of the dose-LET volume histogram (DLVH) curves. It minimizes the overlap of high LET and high dose in OARs and redistributes high LET from OARs to targets in a user-defined way. Ten patients with prostate cancer were included in this retrospective study. Rectum and bladder were considered as OARs. DLVCRO was compared with the conventional robust optimization (RO) method. Plan robustness was quantified using the worst-case analysis method. Besides the dose-volume histogram indices, the analogous LET-volume histogram, extrabiological dose (the product of per voxel dose and LET) volume histogram (xBDVH) indices characterizing the joint dose/LET distributions and DLVH indices were also used. The Wilcoxon signed-rank test was performed to measure statistical significance.

RESULTS

In the nominal scenario, DLVCRO significantly improved joint distribution of dose and LET to protect OARs compared with RO. The physical dose distributions in targets and OARs are comparable. In the worst-case scenario, DLVCRO markedly enhanced OAR protection (more robust) while maintaining almost the same plan robustness in target dose coverage and homogeneity.

CONCLUSIONS

DLVCRO upgrades 2D DVH-based to 3D DLVH-based treatment planning to adjust dose/LET distributions simultaneously and robustly. DLVCRO is potentially a powerful tool to improve patient outcomes in SSPT.

Assessment of Photon-Counting Computed Tomography for Quantitative Imaging in Radiation Therapy

PURPOSE

To test a first-generation clinical photon-counting computed tomography (PCCT) scanner's capabilities to characterize materials in an anthropomorphic head phantom for radiation therapy purposes.

METHODS AND MATERIALS

A CIRS 731-HN head-and-neck phantom (CIRS/SunNuclear) was scanned on a NAEOTOM Alpha PCCT and a SOMATOM Definition AS+ with single-energy and dual-energy CT techniques (SECT and DECT, respectively), both scanners manufactured by Siemens (Siemens Healthineers). A method was developed to derive relative electron density (RED) and effective atomic number (EAN) from linear attenuation coefficients (LACs) of virtual mono-energetic images and applied for the PCCT and DECT data. For DECT, Siemens' syngo.via "Rho/Z"-algorithm was also used. Proton stopping-power ratios (SPRs) were calculated based on RED/EAN with the Bethe equation. For SECT, a stoichiometric calibration to SPR was used. Nine materials in the phantom were segmented, excluding border pixels. Distributions and root-mean-square deviations within the material regions were evaluated for LAC, RED/EAN, and SPR, respectively. Two example ray projections were also examined for LAC, SPR, and water-equivalent thickness, for illustrations of a more treatment-like scenario.

RESULTS

There was a tendency toward narrower distributions for PCCT compared with both DECT methods for the investigated quantities, observed across all materials for RED only. Likewise the scored root-mean-square deviations showed overall superiority for PCCT with a few exceptions: for water-like materials, EAN and SPR were comparable between the modalities; for titanium, the RED and SPR estimates were inferior for PCCT. The PCCT data gave the smallest deviations from theoretic along the more complex example ray profile, whereas the more standard projection showed similar results between the modalities.

CONCLUSIONS

This study shows promising results for tissue characterization in a human-like geometry for radiation therapy purposes using PCCT. The significance of improvements for clinical practice remains to be demonstrated.

A simulation framework for preclinical proton irradiation workflow

Objective.The integration of proton beamlines with x-ray imaging/irradiation platforms has opened up possibilities for image-guided Bragg peak irradiations in small animals. Such irradiations allow selective targeting of normal tissue substructures and tumours. However, their small size and location pose challenges in designing experiments. This work presents a simulation framework useful for optimizing beamlines, imaging protocols, and design of animal experiments. The usage of the framework is demonstrated, mainly focusing on the imaging part.Approach.The fastCAT toolkit was modified with Monte Carlo (MC)-calculated primary and scatter data of a small animal imager for the simulation of micro-CT scans. The simulated CT of a mini-calibration phantom from fastCAT was validated against a full MC TOPAS CT simulation. A realistic beam model of a preclinical proton facility was obtained from beam transport simulations to create irradiation plans in matRad. Simulated CT images of a digital mouse phantom were generated using single-energy CT (SECT) and dual-energy CT (DECT) protocols and their accuracy in proton stopping power ratio (SPR) estimation and their impact on calculated proton dose distributions in a mouse were evaluated.Main results.The CT numbers from fastCAT agree within 11 HU with TOPAS except for materials at the centre of the phantom. Discrepancies for central inserts are caused by beam hardening issues. The root mean square deviation in the SPR for the best SECT (90 kV/Cu) and DECT (50 kV/Al-90 kV/Al) protocols are 3.7% and 1.0%, respectively. Dose distributions calculated for SECT and DECT datasets revealed range shifts <0.1 mm, gamma pass rates (3%/0.1 mm) greater than 99%, and no substantial dosimetric differences for all structures. The outcomes suggest that SECT is sufficient for proton treatment planning in animals.Significance.The framework is a useful tool for the development of an optimized experimental configuration without using animals and beam time.

Multi-stage image registration based on list-mode proton radiographies for small animal proton irradiation: A simulation study

We present a multi-stage and multi-resolution deformable image registration framework for image-guidance at a small animal proton irradiation platform. The framework is based on list-mode proton radiographies acquired at different angles, which are used to deform a 3D treatment planning CT relying on normalized mutual information (NMI) or root mean square error (RMSE) in the projection domain. We utilized a mouse X-ray micro-CT expressed in relative stopping power (RSP), and obtained Monte Carlo simulations of proton images in list-mode for three different treatment sites (brain, head and neck, lung). Rigid transformations and controlled artificial deformation were applied to mimic position misalignments, weight loss and breathing changes. Results were evaluated based on the residual RMSE of RSP in the image domain including the comparison of extracted local features, i.e. between the reference micro-CT and the one transformed taking into account the calculated deformation. The residual RMSE of the RSP showed that the accuracy of the registration framework is promising for compensating rigid (>97% accuracy) and non-rigid (∼95% accuracy) transformations with respect to a conventional 3D-3D registration. Results showed that the registration accuracy is degraded when considering the realistic detector performance and NMI as a metric, whereas the RMSE in projection domain is rather insensitive. This work demonstrates the pre-clinical feasibility of the registration framework on different treatment sites and its use for small animal imaging with a realistic detector. Further computational optimization of the framework is required to enable the use of this tool for online estimation of the deformation.

Use of Virtual CT and On-Treatment MRI to Reduce Radiation Dose and Anesthesia Exposure Associated With the Adaptive Workflow in Pediatric Patients Treated With Intensity Modulated Proton Therapy

Purpose

The purpose of this study was to determine whether virtual computed tomography (vCT) derived from daily cone beam computed tomography (CBCT), or on-treatment magnetic resonance imaging (MRItx) can replace quality assurance computed tomography (qCT) in our clinical workflow to minimize imaging dose and potentially anesthesia exposure in patients requiring plan adaptation.

Methods and Materials

Pediatric patients (age <24 years) treated from 2020 to 2023 with intensity modulated proton therapy with at least 1 qCT during proton therapy were eligible. For cases that required plan adaptation, the dose was recalculated on vCT and compared with same-day qCT as well as the original planning computed tomography (pCT). Anatomic changes triggering plan adaptation were grouped into categories. Two pediatric radiation oncologists verified whether these changes could be detected using CBCT, qCT, and/or MRItx. A new adaptive imaging workflow was proposed to limit imaging dose and anesthesia exposure.

Results

One hundred sixty-eight pediatric patients were treated from 2020 to 2023. Across all patients, there were 517 qCT scans and 61 MRItx acquired. The median number of qCT scans per patient was 3 (range, 1-5). The treatment plans for 20 patients (12%) were adapted. In all patients requiring plan adaptation, there was a correlation between dose differences in target coverage and maximum body dose when comparing vCT with pCT and qCT with pCT (n = 20, r2 = 0.79, P < .01, and r2 = 0.32 P = .01, respectively). The most common reason for adaptation was tissue change (eg, inflammation, changes in abdominal gas, or diaphragmatic variability) in the beam path (10/20) and changes in tumor volume (6/20). All cases of weight change, tissue change in beam path, and unreproducible setup could be detected on CBCT. All cases of change in tumor volume within the brain were detected on MRItx. Replacing the qCT with the vCT was associated with an estimated median reduction of imaging dose by 50% and anesthesia exposure by 1.5 hours.

Conclusions

vCT derived from daily CBCT only or MRItx can safely replace qCT for monitoring dosimetric changes to trigger a new pCT in our clinical workflow. This change would potentially reduce imaging dose and anesthesia exposure.

First experimental verification of prompt gamma imaging with carbon ion irradiation

Prompt Gamma Imaging (PGI) is a promising technique for range verification in Particle Therapy. This technique was already tested in clinical environment with a knife-edge-collimator camera for proton treatments but remains relatively unexplored for Carbon Ion Radiation Therapy (CIRT). Previous FLUKA simulations suggested that PG profile shifts could be detected in CIRT with a precision of ∼ 4 mm ([Formula: see text]) for a particle statistic equal to [Formula: see text] C-ions using a 10 × 10 cm2 camera. An experimental campaign was carried out at CNAO (Pavia, Italy) to verify these results, using a knife-edge-collimator camera prototype based on a 5 × 5 cm2 pixelated LYSO crystal. PG profiles were measured irradiating a plastic phantom with a C-ion pencil beam at clinical energies and intensities, also moving the detector to extend the FOV to 13 × 5 cm2. The prototype detected Bragg-peak shifts with ∼ 4 mm precision for a statistic of [Formula: see text] C-ions ([Formula: see text] for the extended FOV), slightly larger than expected. Nevertheless, the detector demonstrated significant potential for verifying the precision in dose delivery following a treatment fraction, which remains fundamental in the clinical environment. For the first time to our knowledge, range verification based on PGI was applied to a C-ion beam at clinical energy and intensities.

First test beam of the DMAPS-based proton tracker at the pMBRT facility at the Curie Institute

Objective.Proton radiotherapy's efficacy relies on an accurate relative stopping power (RSP) map of the patient to optimise the treatment plan and minimize uncertainties. Currently, a conversion of a Hounsfield Units map obtained by a common x-ray computed tomography (CT) is used to compute the RSP. This conversion is one of the main limiting factors for proton radiotherapy. To bypass this conversion a direct RSP map could be obtained by performing a proton CT (pCT). The focal point of this article is to present a proof of concept of the potential of fast pixel technologies for proton tracking at clinical facilities.Approach.A two-layer tracker based on the TJ-Monopix1, a depleted monolithic active pixel sensor (DMAPS) chip initially designed for the ATLAS, was tested at the proton minibeam radiotherapy beamline at the Curie Institute. The chips were subjected to 100 MeV protons passing through the single slit collimator (0.4×20mm2) with fluxes up to1.3×107p s-1 cm-2. The performance of the proton tracker was evaluated with GEANT4 simulations.Main results.The beam profile and dispersion in air were characterized by an opening of 0.031 mm cm-1, and aσx=0.172mm at the position of the slit. The results of the proton tracking show how the TJ-Monopix1 chip can effectively track protons in a clinical environment, achieving a tracking purity close to 70%, and a position resolution below 0.5 mm; confirming the chip's ability to handle high proton fluxes with a competitive performance.Significance.This work suggests that DMAPS technologies can be a cost-effective alternative for proton imaging. Additionally, the study identifies areas where further optimization of chip design is required to fully leverage these technologies for clinical ion imaging applications.

Exploring dual energy CT synthesis in CBCT-based adaptive radiotherapy and proton therapy: application of denoising diffusion probabilistic models

Background.Adaptive radiotherapy (ART) requires precise tissue characterization to optimize treatment plans and enhance the efficacy of radiation delivery while minimizing exposure to organs at risk. Traditional imaging techniques such as cone beam computed tomography (CBCT) used in ART settings often lack the resolution and detail necessary for accurate dosimetry, especially in proton therapy.Purpose.This study aims to enhance ART by introducing an innovative approach that synthesizes dual-energy computed tomography (DECT) images from CBCT scans using a novel 3D conditional denoising diffusion probabilistic model (DDPM) multi-decoder. This method seeks to improve dose calculations in ART planning, enhancing tissue characterization.Methods.We utilized a paired CBCT-DECT dataset from 54 head and neck cancer patients to train and validate our DDPM model. The model employs a multi-decoder Swin-UNET architecture that synthesizes high-resolution DECT images by progressively reducing noise and artifacts in CBCT scans through a controlled diffusion process.Results.The proposed method demonstrated superior performance in synthesizing DECT images (High DECT MAE 39.582 ± 0.855 and Low DECT MAE 48.540± 1.833) with significantly enhanced signal-to-noise ratio and reduced artifacts compared to traditional GAN-based methods. It showed marked improvements in tissue characterization and anatomical structure similarity, critical for precise proton and radiation therapy planning.Conclusions.This research has opened a new avenue in CBCT-CT synthesis for ART/APT by generating DECT images using an enhanced DDPM approach. The demonstrated similarity between the synthesized DECT images and ground truth images suggests that these synthetic volumes can be used for accurate dose calculations, leading to better adaptation in treatment planning.

Machine Learning Approach and Model for Predicting Proton Stopping Power Ratio and Other Parameters Using Computed Tomography Images

Purpose

The purpose of this study was to accurately estimate proton stopping power ratio (SPR), relative electron density ρ e, effective atomic number (Z eff), and mean excitation energy (I) using one simple robust model and design a machine learning algorithm that will lead to automation.

Methods

Empirical relationships between computed tomography (CT) number and SPR, ρ e (Z eff) and I were used to formulate a model that predicts all the four parameters using linear attenuation coefficients which can be converted to CT numbers. The results of these models were compared with the results of other existing models. Thirty-three ICRU human tissues were used as modeling data and 12 Gammex inserts as testing data for the machine learning algorithm designed. More ways of tissue classification were introduced to improve accuracy. In the examples, the dual energy methods were implemented using 80 kVp and 150 kVP/Sn.

Results

The proposed method gave modeling root mean square error (RMSE) near 1% at maximum for the case of SPR and ρ e for both single and dual-energy CT approaches considered with modeling RMSE of 0.32% for ρ e and 0.38% for SPR as modeling RMSE with room for improvement (this can be done by adjusting the model number of terms as well as the parameters). The method was able to achieve modeling RMSE of 1.11% for I and 1.66% for Z ef f. The mean error for all the estimated quantities was near 0.00%. In most cases, the proposed method has lower testing RMSE and mean error compare to the other methods presented in the study.

Conclusion

The proposed method proves to be more flexible and robust among all presented methods since it has lower testing error in most cases and can be improved based on data using the machine learning algorithm. The algorithm can also improve estimation by adjusting the model as well as aid in automation and it's easy to implement.

Deep learning-based statistical robustness evaluation of intensity-modulated proton therapy for head and neck cancer

Objective. Previous methods for robustness evaluation rely on dose calculation for a number of uncertainty scenarios, which either fails to provide statistical meaning when the number is too small (e.g., ∼8) or becomes unfeasible in daily clinical practice when the number is sufficiently large (e.g., >100). Our proposed deep learning (DL)-based method addressed this issue by avoiding the intermediate dose calculation step and instead directly predicting the percentile dose distribution from the nominal dose distribution using a DL model. In this study, we sought to validate this DL-based statistical robustness evaluation method for efficient and accurate robustness quantification in head and neck (H&N) intensity-modulated proton therapy with diverse beam configurations and multifield optimization.Approach. A dense, dilated 3D U-net was trained to predict the 5th and 95th percentile dose distributions of uncertainty scenarios using the nominal dose and planning CT images. The data set comprised proton therapy plans for 582 H&N cancer patients. Ground truth percentile values were estimated for each patient through 600 dose recalculations, representing randomly sampled uncertainty scenarios. The comprehensive comparisons of different models were conducted for H&N cancer patients, considering those with and without a beam mask and diverse beam configurations, including varying beam angles, couch angles, and beam numbers. The performance of our model trained based on a mixture of patients with H&N and prostate cancer was also assessed in contrast with models trained based on data specific for patients with cancer at either site.Results. The DL-based model's predictions of percentile dose distributions exhibited excellent agreement with the ground truth dose distributions. The average gamma index with 2 mm/2%, consistently exceeded 97% for both 5th and 95th percentile dose volumes. Mean dose-volume histogram error analysis revealed that predictions from the combined training set yielded mean errors and standard deviations that were generally similar to those in the specific patient training data sets.Significance. Our proposed DL-based method for evaluation of the robustness of proton therapy plans provides precise, rapid predictions of percentile dose for a given confidence level regardless of the beam arrangement and cancer site. This versatility positions our model as a valuable tool for evaluating the robustness of proton therapy across various cancer sites.

A direct comparison of multi-energy x-ray and proton CT for imaging and relative stopping power estimation of plastic andex-vivophantoms

Objective.Proton therapy administers a highly conformal dose to the tumour region, necessitating accurate prediction of the patient's 3D map of proton relative stopping power (RSP) compared to water. This remains challenging due to inaccuracies inherent in single-energy computed tomography (SECT) calibration. Recent advancements in spectral x-ray CT (xCT) and proton CT (pCT) have shown improved RSP estimation compared to traditional SECT methods. This study aims to provide the first comparison of the imaging and RSP estimation performance among dual-energy CT (DECT) and photon-counting CT (PCCT) scanners, and a pCT system prototype.Approach.Two phantoms were scanned with the three systems for their performance characterisation: a plastic phantom, filled with water and containing four plastic inserts and a wood insert, and a heterogeneous biological phantom, containing a formalin-stabilised bovine specimen. RSP maps were generated by converting CT numbers to RSP using a calibration based on low- and high-energy xCT images, while pCT utilised a distance-driven filtered back projection algorithm for RSP reconstruction. Spatial resolution, noise, and RSP accuracy were compared across the resulting images.Main results.All three systems exhibited similar spatial resolution of around 0.54 lp/mm for the plastic phantom. The PCCT images were less noisy than the DECT images at the same dose level. The lowest mean absolute percentage error (MAPE) of RSP,(0.28±0.07)%, was obtained with the pCT system, compared to MAPE values of(0.51±0.08)%and(0.80±0.08)%for the DECT- and PCCT-based methods, respectively. For the biological phantom, the xCT-based methods resulted in higher RSP values in most of the voxels compared to pCT.Significance.The pCT system yielded the most accurate estimation of RSP values for the plastic materials, and was thus used to benchmark the xCT calibration performance on the biological phantom. This study underlined the potential benefits and constraints of utilising such a novelex-vivophantom for inter-centre surveys in future.

Dosimetric impact of stopping power for human bone porosity with dual-energy computed tomography in scanned carbon-ion therapy treatment planning

Few reports have documented how the accuracy of stopping power ratio (SPR) prediction for porous bone tissue affects the dose distribution of scanned carbon-ion beam therapy. The estimated SPR based on single-energy computed tomography (SECT) and dual-energy CT (DECT) were compared for the femur of a Rando phantom which simulates the porosity of human bone, NEOBONE which is the hydroxyapatite synthetic bone substitute, and soft tissue samples. Dose differences between SECT and DECT were evaluated for a scanned carbon-ion therapy treatment plan for the Rando phantom. The difference in the water equivalent length was measured to extract the SPR of the examined samples. The differences for SPR estimated from the DECT-SPR conversion were small with - 1.8% and - 3.3% for the Rando phantom femur and NEOBONE, respectively, whereas the differences for SECT-SPR were between 7.6 and 70.7%, illustrating a 1.5-mm shift of the range and a dose difference of 13.3% at maximum point in the evaluation of the dose distribution. This study demonstrated that the DECT-SPR conversion method better estimated the SPR of the porosity of bone tissues than SECT-SPR followed by the accurate range of the carbon-ion beams on carbon-ion dose calculations.

Investigation of intra-fractionated range guided adaptive proton therapy (RGAPT): II. Range-shift compensated on-line treatment adaptation and verification

We previously proposed range-guided adaptive proton therapy (RGAPT) that uses mid-range treatment beams as probing beams and intra-fractionated range measurements for online adaptation. In this work, we demonstrated experimental verification and reported the dosimetric accuracy for RGAPT. A STEEV phantom was used for the experiments, and a 3 × 3 × 3 cm3cube inside the phantom was assigned to be the treatment target. We simulated three online range shift scenarios: reference, overshoot, and undershoot, by placing upstream Lucite sheets, 4, 0, and 8 that corresponded to changes of 0, 6.8, and -6.8 mm, respectively, in water-equivalent path length. The reference treatment plan was to deliver single-field uniform target doses in pencil beam scanning mode and generated on the Eclipse treatment planning system. Different numbers of mid-range layers, including single, three, and five layers, were selected as probing beams to evaluate beam range (BR) measurement accuracy in positron emission tomography (PET). Online plans were modified to adapt to BR shifts and compensate for probing beam doses. In contrast, non-adaptive plans were also delivered and compared to adaptive plans by film measurements. The mid-range probing beams of three (5.55MU) and five layers (8.71MU) yielded accurate range shift measurements in 60 s of PET acquisition with uncertainty of 0.5 mm while the single-layer probing (1.65MU) was not sufficient for measurements. The adaptive plans achieved an average gamma (2%/2 mm) passing rate of 95%. In contrast, the non-adaptive plans only had an average passing rate of 69%. RGAPT planning and delivery are feasible and verified by the experiments. The probing beam delivery, range measurements, and adaptive planning and delivery added a small increase in treatment delivery workflow time but resulted in substantial dose improvement. The three-layer mid-range probing was most suitable considering the balance of high range measurement accuracy and the low number of probing beam layers.

Impact of proton PBS machine operating parameters on the effectiveness of layer rescanning for interplay effect mitigation in lung SBRT treatment

BACKGROUND

Rescanning is a common technique used in proton pencil beam scanning to mitigate the interplay effect. Advances in machine operating parameters across different generations of particle therapy systems have led to improvements in beam delivery time (BDT). However, the potential impact of these improvements on the effectiveness of rescanning remains an underexplored area in the existing research.

METHODS

We systematically investigated the impact of proton machine operating parameters on the effectiveness of layer rescanning in mitigating interplay effect during lung SBRT treatment, using the CIRS phantom. Focused on the Hitachi synchrotron particle therapy system, we explored machine operating parameters from our institution's current (2015) and upcoming systems (2025A and 2025B). Accumulated dynamic 4D dose were reconstructed to assess the interplay effect and layer rescanning effectiveness.

RESULTS

Achieving target coverage and dose homogeneity within 2% deviation required 6, 6, and 20 times layer rescanning for the 2015, 2025A, and 2025B machine parameters, respectively. Beyond this point, further increasing the number of layer rescanning did not further improve the dose distribution. BDTs without rescanning were 50.4, 24.4, and 11.4 s for 2015, 2025A, and 2025B, respectively. However, after incorporating proper number of layer rescanning (six for 2015 and 2025A, 20 for 2025B), BDTs increased to 67.0, 39.6, and 42.3 s for 2015, 2025A, and 2025B machine parameters. Our data also demonstrated the potential problem of false negative and false positive if the randomness of the respiratory phase at which the beam is initiated is not considered in the evaluation of interplay effect.

CONCLUSION

The effectiveness of layer rescanning for mitigating interplay effect is affected by machine operating parameters. Therefore, past clinical experiences may not be applicable to modern machines.

Proton CT on biological phantoms for x-ray CT calibration in proton treatment planning

Objective.To present and characterize a novel method for x-ray computed tomography (xCT) calibration in proton treatment planning, based on proton CT (pCT) measurements on biological phantoms.Approach.A pCT apparatus was used to perform direct measurements of 3D stopping power relative to water (SPR) maps on stabilized, biological phantoms. Two single-energy xCT calibration curves-i.e. tissue substitutes and stoichiometric-were compared to pCT data. Moreover, a new calibration method based on these data was proposed, and verified against intra- and inter-species variability, dependence on stabilization, beam-hardening conditions, and analysis procedures.Main results.Biological phantoms were verified to be stable in time, with a dependence on temperature conditions, especially in the fat region: (-2.5 0.5) HU °C-1. The pCT measurements were compared with standard xCT calibrations, revealing an average SPR discrepancy within ±1.60% for both fat and muscle regions. In the bone region the xCT calibrations overestimated the pCT-measured SPR of the phantom, with a maximum discrepancy of about +3%. As a result, a new cross-calibration curve was directly extracted from the pCT data. Overall, the SPR uncertainty margin associated with this curve was below 3%; fluctuations in the uncertainty values were observed across the HU range. Cross-calibration curves obtained with phantoms made of different animal species and anatomical parts were reproducible with SPR discrepancies within 3%. Moreover, the stabilization procedure did not affect the resulting curve within a 2.2% SPR deviation. Finally, the cross-calibration curve was affected by the beam-hardening conditions on xCTs, especially in the bone region, while dependencies below 2% resulted from the image registration procedure.Significance.Our results showed that pCT measurements on biological phantoms may provide an accurate method for the verification of current xCT calibrations and may represent a tool for the implementation of a new calibration method for proton treatment planning.

MRI-only based material mass density and relative stopping power estimation via deep learning for proton therapy: a preliminary study

Magnetic Resonance Imaging (MRI) is increasingly being used in treatment planning due to its superior soft tissue contrast, which is useful for tumor and soft tissue delineation compared to computed tomography (CT). However, MRI cannot directly provide mass density or relative stopping power (RSP) maps, which are required for calculating proton radiotherapy doses. Therefore, the integration of artificial intelligence (AI) into MRI-based treatment planning to estimate mass density and RSP directly from MRI has generated significant interest. A deep learning (DL) based framework was developed to establish a voxel-wise correlation between MR images and mass density as well as RSP. To facilitate the study, five tissue substitute phantoms were created, representing different tissues such as skin, muscle, adipose tissue, 45% hydroxyapatite (HA), and spongiosa bone. The composition of these phantoms was based on information from ICRP reports. Additionally, two animal tissue phantoms, simulating pig brain and liver, were prepared for DL training purposes. The phantom study involved the development of two DL models. The first model utilized clinical T1 and T2 MRI scans as input, while the second model incorporated zero echo time (ZTE) MRI scans. In the patient application study, two more DL models were trained: one using T1 and T2 MRI scans as input, and another model incorporating synthetic dual-energy computed tomography (sDECT) images to provide accurate bone tissue information. The DECT empirical model was used as a reference to evaluate the proposed models in both phantom and patient application studies. The DECT empirical model was selected as the reference for evaluating the proposed models in both phantom and patient application studies. In the phantom study, the DL model based on T1, and T2 MRI scans demonstrated higher accuracy in estimating mass density and RSP for skin, muscle, adipose tissue, brain, and liver. The mean absolute percentage errors (MAPE) were 0.42%, 0.14%, 0.19%, 0.78%, and 0.26% for mass density, and 0.30%, 0.11%, 0.16%, 0.61%, and 0.23% for RSP, respectively. The DL model incorporating ZTE MRI further improved the accuracy of mass density and RSP estimation for 45% HA and spongiosa bone, with MAPE values of 0.23% and 0.09% for mass density, and 0.19% and 0.07% for RSP, respectively. These results demonstrate the feasibility of using an MRI-only approach combined with DL methods for mass density and RSP estimation in proton therapy treatment planning. By employing this approach, it is possible to obtain the necessary information for proton radiotherapy directly from MRI scans, eliminating the need for additional imaging modalities.

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.

High-Density Glass Scintillators for Proton Radiography-Relative Luminosity, Proton Response, and Spatial Resolution

Proton radiography is a promising development in proton therapy, and researchers are currently exploring optimal detector materials to construct proton radiography detector arrays. High-density glass scintillators may improve integrating-mode proton radiography detectors by increasing spatial resolution and decreasing detector thickness. We evaluated several new scintillators, activated with europium or terbium, with proton response measurements and Monte Carlo simulations, characterizing relative luminosity, ionization quenching, and proton radiograph spatial resolution. We applied a correction based on Birks's analytical model for ionization quenching. The data demonstrate increased relative luminosity with increased activation element concentration, and higher relative luminosity for samples activated with europium. An increased glass density enables more compact detector geometries and higher spatial resolution. These findings suggest that a tungsten and gadolinium oxide-based glass activated with 4% europium is an ideal scintillator for testing in a full-size proton radiography detector.

PTCOG Gastrointestinal Subcommittee Lower Gastrointestinal Tract Malignancies Consensus Statement

Purpose

Radiotherapy delivery in the definitive management of lower gastrointestinal (LGI) tract malignancies is associated with substantial risk of acute and late gastrointestinal (GI), genitourinary, dermatologic, and hematologic toxicities. Advanced radiation therapy techniques such as proton beam therapy (PBT) offer optimal dosimetric sparing of critical organs at risk, achieving a more favorable therapeutic ratio compared with photon therapy.

Materials and Methods

The international Particle Therapy Cooperative Group GI Subcommittee conducted a systematic literature review, from which consensus recommendations were developed on the application of PBT for LGI malignancies.

Results

Eleven recommendations on clinical indications for which PBT should be considered are presented with supporting literature, and each recommendation was assessed for level of evidence and strength of recommendation. Detailed technical guidelines pertaining to simulation, treatment planning and delivery, and image guidance are also provided.

Conclusion

PBT may be of significant value in select patients with LGI malignancies. Additional clinical data are needed to further elucidate the potential benefits of PBT for patients with anal cancer and rectal cancer.

Integrated-mode proton radiography with 2D lateral projections

Integrated-mode proton radiography leading to water equivalent thickness (WET) maps is an avenue of interest for motion management, patient positioning, andin vivorange verification. Radiographs can be obtained using a pencil beam scanning setup with a large 3D monolithic scintillator coupled with optical cameras. Established reconstruction methods either (1) involve a camera at the distal end of the scintillator, or (2) use a lateral view camera as a range telescope. Both approaches lead to limited image quality. The purpose of this work is to propose a third, novel reconstruction framework that exploits the 2D information provided by two lateral view cameras, to improve image quality achievable using lateral views. The three methods are first compared in a simulated Geant4 Monte Carlo framework using an extended cardiac torso (XCAT) phantom and a slanted edge. The proposed method with 2D lateral views is also compared with the range telescope approach using experimental data acquired with a plastic volumetric scintillator. Scanned phantoms include a Las Vegas (contrast), 9 tissue-substitute inserts (WET accuracy), and a paediatric head phantom. Resolution increases from 0.24 (distal) to 0.33 lp mm-1(proposed method) on the simulated slanted edge phantom, and the mean absolute error on WET maps of the XCAT phantom is reduced from 3.4 to 2.7 mm with the same methods. Experimental data from the proposed 2D lateral views indicate a 36% increase in contrast relative to the range telescope method. High WET accuracy is obtained, with a mean absolute error of 0.4 mm over 9 inserts. Results are presented for various pencil beam spacing ranging from 2 to 6 mm. This work illustrates that high quality proton radiographs can be obtained with clinical beam settings and the proposed reconstruction framework with 2D lateral views, with potential applications in adaptive proton therapy.

Energy painting: helium-beam radiography with thin detectors and multiple beam energies

Objective.Compact ion imaging systems based on thin detectors are a promising prospect for the clinical environment since they are easily integrated into the clinical workflow. Their measurement principle is based on energy deposition instead of the conventionally measured residual energy or range. Therefore, thin detectors are limited in the water-equivalent thickness range they can image with high precision. This article presents ourenergy paintingmethod, which has been developed to render high precision imaging with thin detectors feasible even for objects with larger, clinically relevant water-equivalent thickness (WET) ranges.Approach.A detection system exclusively based on pixelated silicon Timepix detectors was used at the Heidelberg ion-beam therapy center to track single helium ions and measure their energy deposition behind the imaged object. Calibration curves were established for five initial beam energies to relate the measured energy deposition to WET. They were evaluated regarding their accuracy, precision and temporal stability. Furthermore, a 60 mm × 12 mm region of a wedge phantom was imaged quantitatively exploiting the calibrated energies and five different mono-energetic images. These mono-energetic images were combined in a pixel-by-pixel manner by averaging the WET-data weighted according to their single-ion WET precision (SIWP) and the number of contributing ions.Main result.A quantitative helium-beam radiograph of the wedge phantom with an average SIWP of 1.82(5) % over the entire WET interval from 150 mm to 220 mm was obtained. Compared to the previously used methodology, the SIWP improved by a factor of 2.49 ± 0.16. The relative stopping power value of the wedge derived from the energy-painted image matches the result from range pullback measurements with a relative deviation of only 0.4 %.Significance.The proposed method overcomes the insufficient precision for wide WET ranges when employing detection systems with thin detectors. Applying this method is an important prerequisite for imaging of patients. Hence, it advances detection systems based on energy deposition measurements towards clinical implementation.

Technical note: development of a simulation framework, enabling the investigation of locally tuned single energy proton radiography

Range uncertainties remain a limitation for the confined dose distribution that proton therapy can offer. The uncertainty stems from the ambiguity when translating CT Hounsfield Units (HU) into proton stopping powers. Proton Radiography (PR) can be used to verify the proton range. Specifically, PR can be used as a quality-control tool for CBCT-based synthetic CTs. An essential part of the work illustrating the potential of PR has been conducted using multi-layer ionization chamber (MLIC) detectors and mono-energetic PR. Due to the dimensions of commercially available MLICs, clinical adoption is cumbersome. Here, we present a simulation framework exploring locally-tuned single energy (LTSE) proton radiography and corresponding potential compact PR detector designs. Based on a planning CT data set, the presented framework models the water equivalent thickness. Subsequently, it analyses the proton energies required to pass through the geometry within a defined ROI. In the final step, an LTSE PR is simulated using the MCsquare Monte Carlo code. In an anatomical head phantom, we illustrate that LTSE PR allows for a significantly shorter longitudinal dimension of MLICs. We compared PR simulations for two exemplary 30 × 30 mm2proton fields passing the phantom at a 90° angle at an anterior and a posterior location in an iso-centric setup. The longitudinal distance over which all spots per field range out is significantly reduced for LTSE PR compared to mono-energetic PR. In addition, we illustrate the difference in shape of integral depth dose (IDD) when using constrained PR energies. Finally, we demonstrate the accordance of simulated and experimentally acquired IDDs for an LTSE PR acquisition. As the next steps, the framework will be used to investigate the sensitivity of LTSE PR to various sources of errors. Furthermore, we will use the framework to systematically explore the dimensions of an optimized MLIC design for daily clinical use.

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.

Clinical value of dual-energy CT for predicting occult metastasis in central neck lymph nodes of papillary thyroid carcinoma

OBJECTIVES

To predict the probability of occult lymph node metastasis (OLNM) in the central cervical by analyzing the dual-energy computed tomography (DECT) parameters derived from papillary thyroid carcinoma (PTC).

METHODS

Data were retrospectively collected from patients with pathologically confirmed PTC who underwent arterial and venous phases of enhanced DECT with concurrent central neck lymph node dissection (CLND). Three clinical features, three shape-related features, and twenty-six DECT-derived parameters were measured. The univariate and multivariate analyses were applied to select the relevant parameters and develop the nomogram.

RESULTS

A total 140 cases with negative diagnosis of cervical central lymph node metastases by preoperative evaluation were included, among which 88 patients with metastasis (OLNM +) and 52 patients without metastasis (OLNM -) were finally confirmed by pathology. (1) Anteroposterior/transverse diameter ratio (A/T) derived from the PTC focus had significant difference between the OLNM + and OLNM - groups (p < 0.05). (2) In the arterial phase, iodine concentration (ICarterial), normalized iodine concentration (NICarterial), effective atomic number (Zeff-arterial), electron density (EDarterial), and slope of energy curve (karterial) from PTC focus showed significant difference (all p < 0.05) between the two groups. In the venous phase, only the CT value under the 40 keV (HU40keVvenous) had differences (p < 0.05). (3) The nomogram was produced to predict the probability of OLNM, and the AUC, sensitivity, and specificity in the training and test cohort were 0.830, 75.0%, 76.9%, and 0.829, 65.9%, 84.6%, respectively.

CONCLUSIONS

DECT parameters combined with shape-related feature derived from PTC might be used as predictors of OLNM in the central neck.

CLINICAL RELEVANCE STATEMENT

Preoperative imaging evaluation combining shape-related features and dual-energy CT parameters could serve as a reference to discern occult lymph node metastasis in central neck during the surgically planning of papillary thyroid carcinoma.

KEY POINTS

• Papillary thyroid carcinoma (PTC) patients may have occult lymph node metastasis (OLNM) in the central neck, which is extremely difficult to find by preoperative imaging examination. • Dual-energy CT quantitative evaluation has higher accuracy than conventional CT and can predicting OLNM in the central neck of PTC. • Dual-energy CT quantitative parameters and morphology of PTC can serve as a useful tool in predicting OLNM in the central neck, and as a guide for personalized treatment.

Exploration of differentiability in a proton computed tomography simulation framework

Objective.Gradient-based optimization using algorithmic derivatives can be a useful technique to improve engineering designs with respect to a computer-implemented objective function. Likewise, uncertainty quantification through computer simulations can be carried out by means of derivatives of the computer simulation. However, the effectiveness of these techniques depends on how 'well-linearizable' the software is. In this study, we assess how promising derivative information of a typical proton computed tomography (pCT) scan computer simulation is for the aforementioned applications.Approach.This study is mainly based on numerical experiments, in which we repeatedly evaluate three representative computational steps with perturbed input values. We support our observations with a review of the algorithmic steps and arithmetic operations performed by the software, using debugging techniques.Main results.The model-based iterative reconstruction (MBIR) subprocedure (at the end of the software pipeline) and the Monte Carlo (MC) simulation (at the beginning) were piecewise differentiable. However, the observed high density and magnitude of jumps was likely to preclude most meaningful uses of the derivatives. Jumps in the MBIR function arose from the discrete computation of the set of voxels intersected by a proton path, and could be reduced in magnitude by a 'fuzzy voxels' approach. The investigated jumps in the MC function arose from local changes in the control flow that affected the amount of consumed random numbers. The tracking algorithm solves an inherently non-differentiable problem.Significance.Besides the technical challenges of merely applying AD to existing software projects, the MC and MBIR codes must be adapted to compute smoother functions. For the MBIR code, we presented one possible approach for this while for the MC code, this will be subject to further research. For the tracking subprocedure, further research on surrogate models is necessary.

Single energy CT-based mass density and relative stopping power estimation for proton therapy using deep learning method

Background

The number of patients undergoing proton therapy has increased in recent years. Current treatment planning systems (TPS) calculate dose maps using three-dimensional (3D) maps of relative stopping power (RSP) and mass density. The patient-specific maps of RSP and mass density were obtained by translating the CT number (HU) acquired using single-energy computed tomography (SECT) with appropriate conversions and coefficients. The proton dose calculation uncertainty of this approach is 2.5%-3.5% plus 1 mm margin. SECT is the major clinical modality for proton therapy treatment planning. It would be intriguing to enhance proton dose calculation accuracy using a deep learning (DL) approach centered on SECT.

Objectives

The purpose of this work is to develop a deep learning method to generate mass density and relative stopping power (RSP) maps based on clinical single-energy CT (SECT) data for proton dose calculation in proton therapy treatment.

Methods

Artificial neural networks (ANN), fully convolutional neural networks (FCNN), and residual neural networks (ResNet) were used to learn the correlation between voxel-specific mass density, RSP, and SECT CT number (HU). A stoichiometric calibration method based on SECT data and an empirical model based on dual-energy CT (DECT) images were chosen as reference models to evaluate the performance of deep learning neural networks. SECT images of a CIRS 062M electron density phantom were used as the training dataset for deep learning models. CIRS anthropomorphic M701 and M702 phantoms were used to test the performance of deep learning models.

Results

For M701, the mean absolute percentage errors (MAPE) of the mass density map by FCNN are 0.39%, 0.92%, 0.68%, 0.92%, and 1.57% on the brain, spinal cord, soft tissue, bone, and lung, respectively, whereas with the SECT stoichiometric method, they are 0.99%, 2.34%, 1.87%, 2.90%, and 12.96%. For RSP maps, the MAPE of FCNN on M701 are 0.85%, 2.32%, 0.75%, 1.22%, and 1.25%, whereas with the SECT reference model, they are 0.95%, 2.61%, 2.08%, 7.74%, and 8.62%.

Conclusion

The results show that deep learning neural networks have the potential to generate accurate voxel-specific material property information, which can be used to improve the accuracy of proton dose calculation.

Advances in knowledge

Deep learning-based frameworks are proposed to estimate material mass density and RSP from SECT with improved accuracy compared with conventional methods.

A spot-specific range uncertainty framework for robust optimization of proton therapy treatments

PURPOSE

An accurate estimation of range uncertainties is essential to exploit the potential of proton therapy. According to Paganetti's study, a value of 2.4% (1.5 standard deviation) is currently recommended for planning robust treatments with Monte Carlo dose engines. This number is based on a dominant contribution from the mean excitation energy of tissues. However, it was recently shown that expressing tissues as a mixture of water and "dry" material in the CT calibration process allowed for a significant reduction of this uncertainty. We thus propose an adapted framework for pencil beam scanning robust optimization. First, we move towards a spot-specific range uncertainty (SSRU) determination. Second, we use the water-based formalism to reduce range uncertainties and, potentially, to spare better the organs at risk.

METHODS

The stoichiometric calibration was adapted to provide a molecular decomposition (including water) of each voxel of the CT. The SSRU calculation was implemented in MCsquare, a fast Monte Carlo dose engine dedicated to proton therapy. For each spot, a ray-tracing method was used to propagate molecular I-values uncertainties and obtain the corresponding effective range uncertainty. These were then combined with other sources of range uncertainties, according to Paganetti's study of 2012. The method was then assessed on three head-and-neck patients. Two plans were optimized for each patient: the first one with the classical 2.4% flat range uncertainty (FRU), the second one with the variable range uncertainty. Both plans were then compared in terms of target coverage and OAR mean dose reduction. Robustness evaluations were also performed, using the SSRU for both plans in order to simulate errors as realistically as possible.

RESULTS

For patient 1, it was found that the median SSRU was 1.04% (1.5 standard deviation), yielding, therefore, a very large reduction from the 2.4% FRU. All three SSRU plans were found to have a very good robustness level at a 90% confidence interval while sparing OAR better than the classical plan. For instance, in nominal cases, average reductions in the mean dose of 15.7, 8.4, and 13.2% were observed in the left parotid, right parotid, and pharyngeal constrictor muscle, respectively. As expected, the classical plans showed a higher but unnecessary level of robustness.

CONCLUSIONS

Promising results of the SSRU framework were observed on three head-and-neck cases, and more patients should now be considered. The method could also benefit to other tumor sites and, in the long run, the variable part of the range uncertainty could be generalized to other sources of uncertainty in order to move towards more and more patient-specific treatments.

Investigation on the proton range uncertainty with spectral CT-based virtual monoenergetic images

OBJECTIVE

The stopping power ratio (SPR) prediction error will contribute to the range uncertainty of proton therapy. Spectral CT is promising in reducing the uncertainty in SPR estimation. The purpose of this research is to determine the optimal energy pairs of SPR prediction for each tissue and to evaluate the dose distribution and range difference between the spectral CT with the optimal energy pairs method and the single energy CT (SECT) method.

METHODS

A new method was proposed based on image segmentation to calculate the proton dose with spectral CT images for the head and body phantom. CT number of each organ region were converted to SPR with the optimal energy pairs of each organ. The CT images were segmented into different organ parts with thresholding method. Virtual monoenergetic (VM) images from 70 keV to 140 keV were investigated to determine the optimal energy pairs for each organ based on Gammex 1467 phantom. The beam data of Shanghai Advanced Proton Therapy facility (SAPT) was employed in matRad (an open-source software for radiation treatment planning) for the dose calculation.

RESULTS

The optimal energy pairs were obtained for each tissue. The dose distribution of two tumor sites (brain and lung) were calculated with the aforementioned optimal energy pairs. The maximum dose deviation between spectral CT and SECT at the target region was 2.57% and 0.84% for the lung tumor and brain tumor respectively. The range difference between spectral and SECT was significant with 1.8411 mm for the lung tumor. γ passing rate was 85.95% and 95.49% for the lung tumor and brain tumor with the criterion 2%/2 mm.

CONCLUSIONS

This work presents a way to determine the optimal energy pairs for each organ and to calculate the dose distribution based on the more accurate SPR prediction.

Uncertainty in the range of the protons and C-ions in particle therapy due to a hydration level of a human body model

Cancer treatment with protons and carbon ions relies on the property of the accelerated charged particles to deposit most of their energy in the vicinity of their range (around the Bragg peak). The level of hydration in a cancer patient's body may vary within hours. Some patients may be heavy to moderately dehydrated, and some may be well and even excessively hydrated. In this research, we aim to estimate the uncertainty of the protons and C-ion ranges because of the different hydration levels of the human body. For the study of the impact of body hydration level on the particle's ranges, we have designed a new phantom model - a homogeneous mixture of an Average HUuman BOdy constituting elements (AHUBO) in three states of hydration: normal (n), dehydrated (d), and excessively hydrated (e) by applying corresponding recalibration in the "atomic-stoichiometry model" due to the water sufficiency/deficiency. The purpose of the study is to estimate the shift in the ranges depending on the hydration level, possibly suggest particle beam energy adjustments to overcome the range uncertainties, to deliver the prescribed dose to the tumour while sparing the healthy tissue. Herein we present the results of the FLUKA-Flair simulations of the therapeutic range of energies of protons (50-105 MeV) and C-ions (30-210 MeV) respectively, into an AHUBO head phantom model at three levels of hydration (normal, dehydrated, and excessively hydrated). The range uncertainty was estimated via the shifts of the Bragg-peaks position for the three different hydration levels. The estimations showed that the range uncertainty (ΔR) due to body hydration for the maximum energy in the range for protons at 105 MeV is about 0.04 mm and for C-ions at 190 MeV/u is about 0.06 mm.

A generalizable new figure of merit for dose optimization in dual energy cone beam CT scanning protocols

Objective. This study proposes and evaluates a new figure of merit (FOMn) for dose optimization of Dual-energy cone-beam CT (DE-CBCT) scanning protocols based on size-dependent modeling of radiation dose and multi-scale image quality.Approach. FOMn was defined using Z-score normalization and was proportional to the dose efficiency providing better multi-scale image quality, including comprehensive contrast-to-noise ratio (CCNR) and electron density (CED) for CatPhan604 inserts of various materials. Acrylic annuluses were combined with CatPhan604 to create four phantom sizes (diameters of the long axis are 200 mm, 270 mm, 350 mm, and 380 mm, respectively). DE-CBCT was decomposed using image-domain iterative methods based on Varian kV-CBCT images acquired using 25 protocols (100 kVp and 140 kVp combined with 5 tube currents).Main results. The accuracy of CED was approximately 1% for all protocols, but degraded monotonically with the increased phantom sizes. Combinations of lower voltage + higher current and higher voltage + lower current were optimal protocols balancing CCNR and dose. The most dose-efficient protocols for CED and CCNR were inconsistent, underlining the necessity of including multi-scale image quality in the evaluation and optimization of DE-CBCT. Pediatric and adult anthropomorphic phantom tests confirmed dose-efficiency of FOMn-recommended protocols.Significance. FOMn is a comprehensive metric that collectively evaluates radiation dose and multi-scale image quality for DE-CBCT. The models and data can also serve as lookup tables, suggesting personalized dose-efficient protocols for specific clinical imaging purposes.

Clinical benefit of range uncertainty reduction in proton treatment planning based on dual-energy CT for neuro-oncological patients

OBJECTIVE

Several studies have shown that dual-energy CT (DECT) can lead to improved accuracy for proton range estimation. This study investigated the clinical benefit of reduced range uncertainty, enabled by DECT, in robust optimisation for neuro-oncological patients.

METHODS

DECT scans for 27 neuro-oncological patients were included. Commercial software was applied to create stopping-power ratio (SPR) maps based on the DECT scan. Two plans were robustly optimised on the SPR map, keeping the beam and plan settings identical to the clinical plan. One plan was robustly optimised and evaluated with a range uncertainty of 3% (as used clinically; denoted 3%-plan); the second plan applied a range uncertainty of 2% (2%-plan). Both plans were clinical acceptable and optimal. The dose-volume histogram parameters were compared between the two plans. Two experienced neuro-radiation oncologists determined the relevant dose difference for each organ-at-risk (OAR). Moreover, the OAR toxicity levels were assessed.

RESULTS

For 24 patients, a dose reduction >0.5/1 Gy (relevant dose difference depending on the OAR) was seen in one or more OARs for the 2%-plan; e.g. for brainstem D0.03cc in 10 patients, and hippocampus D40% in 6 patients. Furthermore, 12 patients had a reduction in toxicity level for one or two OARs, showing a clear benefit for the patient.

CONCLUSION

Robust optimisation with reduced range uncertainty allows for reduction of OAR toxicity, providing a rationale for clinical implementation. Based on these results, we have clinically introduced DECT-based proton treatment planning for neuro-oncological patients, accompanied with a reduced range uncertainty of 2%.

ADVANCES IN KNOWLEDGE

This study shows the clinical benefit of range uncertainty reduction from 3% to 2% in robustly optimised proton plans. A dose reduction to one or more OARs was seen for 89% of the patients, and 44% of the patients had an expected toxicity level decrease.

Effect of extended field-of-view approaches on the accuracy of stopping power ratio estimation for single-energy computed tomography simulators

BACKGROUND

Extended field-of-view (eFOV) methods have been proposed to generate larger demonstration FOVs for computed tomography (CT) simulators with a limited scanning FOV (sFOV) size in order to ensure accurate dose calculation and patient collision avoidance. Although the efficacy of these strategies has been evaluated for photon applications, the effect of stopping power ratio (SPR) estimation on proton therapy has not been studied. This study investigated the effect of an eFOV approach on the accuracy of SPR to water estimation in homogeneous and heterogeneous phantoms.

MATERIALS AND METHODS

To simulate patient geometries, tissue-equivalent material (TEM) and customized extension phantoms were used. The TEM phantom supported various rod arrangements through predefined holes. Images were reconstructed to three FOV sizes using a commercial eFOV technique. A single-energy CT stoichiometric method was used to generate Hounsfield unit (HU) to SPR (HU-to-SPR) conversion curves for each FOV. To investigate the effect of rod location in the sFOV and eFOV regions, eight TEM rods were placed at off-center distances in the homogeneous phantom and scanned individually. Similarly, 16 TEM rods were placed in the heterogeneous TEM phantom and scanned simultaneously.

RESULTS

The conversion curves derived from the sFOV and eFOV data were identical. The average SPR differences of soft-tissue, bone, and lung materials for rods placed at various off-center locations were 3.3%, 4.8%, and 39.6%, respectively. In the heterogeneous phantom, the difference was within 1.0% in the absence of extension. However, in the presence of extension, the difference increased to 2.8% for all rods, except for lung materials, whose difference was 4.8%.

CONCLUSIONS

When an eFOV method is used, the SPR variation in phantoms considerably increases for all TEM rods, especially for lung TEM rods. This phenomenon may substantially increase the uncertainty of HU-to-SPR conversion. Therefore, image reconstruction with a standard FOV size is recommended.

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.

Validation of robust radiobiological optimization algorithms based on the mixed beam model for intensity-modulated carbon-ion therapy

Currently, treatment planning systems (TPSs) that can compute the intensities of intensity-modulated carbon-ion therapy (IMCT) using scanned carbon-ion beams are limited. In the present study, the computational efficacy of the newly designed IMCT algorithms was analyzed for the first time based on the mixed beam model with respect to the physical and biological doses; moreover, the validity and effectiveness of the robust radiobiological optimization were verified. A dose calculation engine was independently generated to validate a clinical dose determined in the TPS. A biological assay was performed using the HSGc-C5 cell line to validate the calculated surviving fraction (SF). Both spot control (SC) and voxel-wise worst-case scenario (WC) algorithms were employed for robust radiobiological optimization followed by their application in a Radiation Therapy Oncology Group benchmark phantom under homogeneous and heterogeneous conditions and a clinical case for range and position errors. Importantly, for the first time, both SC and WC algorithms were implemented in the integrated TPS platform that can compute the intensities of IMCT using scanned carbon-ion beams for robust radiobiological optimization. For assessing the robustness, the difference between the maximum and minimum values of a dose-volume histogram index in the examined error scenarios was considered as a robustness index. The relative biological effectiveness (RBE) determined by the independent dose calculation engine exhibited a -0.6% difference compared with the RBE defined by the TPS at the isocenter, whereas the measured and the calculated SF were similar. Regardless of the objects, compared with the conventional IMCT, the robust radiobiological optimization enhanced the sensitivity of the examined error scenarios by up to 19% for the robustness index. The computational efficacy of the novel IMCT algorithms was verified according to the mixed beam model with respect to the physical and biological doses. The robust radiobiological optimizations lowered the impact of range and position uncertainties considerably in the examined scenarios. The robustness of the WC algorithm was more enhanced compared with that of the SC algorithm. Nevertheless, the SC algorithm can be used as an alternative to the WC IMCT algorithm with respect to the computational cost.

The INFN proton computed tomography system for relative stopping power measurements: calibration and verification

Objective. This paper describes the procedure to calibrate the three-dimensional (3D) proton stopping power relative to water (SPR) maps measured by the proton computed tomography (pCT) apparatus of the Istituto Nazionale di Fisica Nucleare (INFN, Italy). Measurements performed on water phantoms are used to validate the method. The calibration allowed for achieving measurement accuracy and reproducibility to levels below 1%.Approach. The INFN pCT system is made of a silicon tracker for proton trajectory determination followed by a YAG:Ce calorimeter for energy measurement. To perform the calibration, the apparatus has been exposed to protons of energies ranging from 83 to 210 MeV. Using the tracker, a position-dependent calibration has been implemented to keep the energy response uniform across the calorimeter. Moreover, correction algorithms have been developed to reconstruct the proton energy when this is shared in more than one crystal and to consider the energy loss in the non-uniform apparatus material. To verify the calibration and its reproducibility, water phantoms have been imaged with the pCT system during two data-taking sessions.Main results. The energy resolution of the pCT calorimeter resulted to beσEE≅0.9%at 196.5 MeV. The average values of the water SPR in fiducial volumes of the control phantoms have been calculated to be 0.995±0.002. The image non-uniformities were below 1%. No appreciable variation of the SPR and uniformity values between the two data-taking sessions could be identified.Significance. This work demonstrates the accuracy and reproducibility of the calibration of the INFN pCT system at a level below 1%. Moreover, the uniformity of the energy response keeps the image artifacts at a low level even in the presence of calorimeter segmentation and tracker material non-uniformities. The implemented calibration technique allows the INFN-pCT system to face applications where the precision of the SPR 3D maps is of paramount importance.

Consensus guide on CT-based prediction of stopping-power ratio using a Hounsfield look-up table for proton therapy

BACKGROUND AND PURPOSE

Studies have shown large variations in stopping-power ratio (SPR) prediction from computed tomography (CT) across European proton centres. To standardise this process, a step-by-step guide on specifying a Hounsfield look-up table (HLUT) is presented here.

MATERIALS AND METHODS

The HLUT specification process is divided into six steps: Phantom setup, CT acquisition, CT number extraction, SPR determination, HLUT specification, and HLUT validation. Appropriate CT phantoms have a head- and body-sized part, with tissue-equivalent inserts in regard to X-ray and proton interactions. CT numbers are extracted from a region-of-interest covering the inner 70% of each insert in-plane and several axial CT slices in scan direction. For optimal HLUT specification, the SPR of phantom inserts is measured in a proton beam and the SPR of tabulated human tissues is computed stoichiometrically at 100 MeV. Including both phantom inserts and tabulated human tissues increases HLUT stability. Piecewise linear regressions are performed between CT numbers and SPRs for four tissue groups (lung, adipose, soft tissue, and bone) and then connected with straight lines. Finally, a thorough but simple validation is performed.

RESULTS

The best practices and individual challenges are explained comprehensively for each step. A well-defined strategy for specifying the connection points between the individual line segments of the HLUT is presented. The guide was tested exemplarily on three CT scanners from different vendors, proving its feasibility.

CONCLUSION

The presented step-by-step guide for CT-based HLUT specification with recommendations and examples can contribute to reduce inter-centre variations in SPR prediction.

Creation, evolution, and future challenges of ion beam therapy from a medical physicist's viewpoint (Part 2). Chapter 2. Biophysical model, treatment planning system and image guided radiotherapy

When an ion beam penetrates deeply into the body, its kinetic energy decreases, and its biological effect increases due to the change of the beam quality. To give a uniform biological effect to the target, it is necessary to reduce the absorbed dose with the depth. A bio-physical model estimating the relationship between ion beam quality and biological effect is necessary to determine the relative biological effectiveness (RBE) of the ion beam that changes with depth. For this reason, Lawrence Berkeley Laboratory, National Institute of Radiological Sciences (NIRS) and GSI have each developed their own model at the starting of the ion beam therapy. Also, NIRS developed a new model at the starting of the scanning irradiation. Although the Local Effect Model (LEM) at the GSI and the modified Microdosimetric Kinetic Model (MKM) at the NIRS, the both are currently used, can similarly predict radiation quality-induced changes in surviving fraction of cultured cell, the clinical RBE-weighted doses for the same absorbed dose are different. This is because the LEM uses X-rays as a reference for clinical RBE, whereas the modified MKM uses carbon ion beam as a reference and multiplies it by a clinical factor of 2.41. Therefore, both are converted through the absorbed dose. In PART 2, I will describe the development of such a bio-physical model, as well as the birth and evolution of a treatment planning system and image guided radiotherapy.

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.

Evaluation of treatment planning system accuracy in estimating the stopping-power ratio of immobilization devices for proton therapy

PURPOSE

To assess treatment planning system (TPS) accuracy in estimating the stopping-power ratio (SPR) of immobilization devices commonly used in proton therapy and to evaluate the dosimetric effect of SPR estimation error for a set of clinical treatment plans.

METHODS

Computed tomography scans of selected clinical immobilization devices were acquired. Then, the water-equivalent thickness (WET) and SPR values of these devices based on the scans were estimated in a commercial TPS. The reference SPR of each device was measured using a multilayer ion chamber (MLIC), and the differences between measured and TPS-estimated SPRs were calculated. These findings were utilized to calculate corrected dose distributions of 15 clinical proton plans for three treatment sites: extremity, abdomen, and head-and-neck. The original and corrected dose distributions were compared using a set of target and organs-at-risk (OARs) dose-volume histogram (DVH) parameters.

RESULTS

On average, the TPS-estimated SPR was 19.5% lower (range, -35.1% to 0.2%) than the MLIC-measured SPR. Due to the relatively low density of most immobilization devices used, the WET error was typically <1 mm, but up to 2.2 mm in certain devices. Overriding the SPR of the immobilization devices to the measured values did not result in significant changes in the DVH metrics of targets and most OARs. However, some critical OARs showed noticeable changes of up to 6.7% in maximum dose.

CONCLUSIONS

The TPS tends to underestimate the SPR of selected proton immobilization devices by an average of about 20%, but this does not induce major WET errors because of the low density of the devices. The dosimetric effect of this SPR error was negligible for most treatment sites, although the maximum dose of a few OARs exhibited noticeable variations.

Stereotactic body proton therapy for early stage non-small cell lung cancer - Technical challenges and solutions: The MD Anderson experience

Our randomized clinical study comparing stereotactic body radiotherapy (SBRT) and stereotactic body proton therapy (SBPT) for early stage non-small cell lung cancer (NSCLC) was closed prematurely owing to poor enrollment, largely because of lack of volumetric imaging and difficulty in obtaining insurance coverage for the SBPT group. In this article, we describe technology improvements in our new proton therapy center, particularly in image guidance with cone beam CT (CBCT) and CT on rail (CTOR), as well as motion management with real-time gated proton therapy (RGPT) and optical surface imaging. In addition, we have a treatment planning system that provides better treatment plan optimization and more accurate dose calculation. We expect to re-start the SBPT program, including for early stage NSCLC as well as for other disease sites soon after starting patient treatment at our new proton therapy center.

Prediction of proton beam range in phantom with metals based on monochromatic energy CT images

The purpose of the study was to evaluate the accuracy of monochromatic energy (MonoE) computed tomography (CT) images reconstructed by spectral CT in predicting the stopping power ratio $( SP{R}_w)$ of materials in the presence of metal. The CIRS062 phantom was scanned three times using spectral CT. In the first scan, a solid water insert was placed at the center of the phantom $(C{T}_{no\ metal})$. In the second scan, the solid water insert was replaced with a titanium alloy femoral head $(C{T}_{metal})$. The metal artifact reduction (MAR) algorithm was used in the last scan $(C{T}_{metal+ MAR})$. The MonoE-CT images of 40 keV and 80 keV were reconstructed. Finally, the single-energy CT method (SECT) and the dual-energy CT method (DECT) were used to calculate the $SP{R}_w$. The mean absolute error (MAE) of the $SP{R}_w$ of the inner layer inserts calculated by the SECT method were 3.19%, 13.88% and 2.71%, corresponding to $C{T}_{no\ metal}$, $C{T}_{metal}$ and $C{T}_{metal+ MAR}$, respectively. For the outer layer inserts, the MAE of $SP{R}_w$ were 3.43%, 5.42% and 2.99%, respectively. Using the DECT method, the MAE of the $SP{R}_w$ of the inner layer inserts was 1.30%, 3.69% and 1.46% and the MAE of the outer layer inserts- was 1.34%, 1.36% and 1.05%. The studies shows that, compared with the SECT method, the accuracy of the DECT method in predicting the $SP{R}_w$ of a material is more robust to the presence of metal. Using the MAR algorithm when performing CT scans can further improve the accuracy of predicting the SPR of materials in the presence of metal.

Assessment of quantitative information for radiation therapy at a first-generation clinical photon-counting computed tomography scanner

As one of the latest developments in X-ray computed tomography (CT), photon-counting technology allows spectral detection, demonstrating considerable advantages as compared to conventional CT. In this study, we investigated the use of a first-generation clinical photon-counting computed tomography (PCCT) scanner and estimated proton relative (to water) stopping power (RSP) of tissue-equivalent materials from virtual monoenergetic reconstructions provided by the scanner. A set of calibration and evaluation tissue-equivalent inserts were scanned at 120 kVp. Maps of relative electron density (RED) and effective atomic number (EAN) were estimated from the reconstructed virtual monoenergetic images (VMI) using an approach previously applied to a spectral CT scanner with dual-layer detector technology, which allows direct calculation of RSP using the Bethe-Bloch formula. The accuracy of RED, EAN, and RSP was evaluated by root-mean-square errors (RMSE) averaged over the phantom inserts. The reference RSP values were obtained experimentally using a water column in an ion beam. For RED and EAN, the reference values were calculated based on the mass density and the chemical composition of the inserts. Different combinations of low- and high-energy VMIs were investigated in this study, ranging from 40 to 190 keV. The overall lowest error was achieved using VMIs at 60 and 180 keV, with an RSP accuracy of 1.27% and 0.71% for the calibration and the evaluation phantom, respectively.