Proton therapy range uncertainty reduction using vendor-agnostic tissue characterization on a virtual photon-counting CT head scan.

The purpose of this study was to analyze factors affecting proton stopping-power-ratio (SPR) estimations and range uncertainties in proton therapy planning using the standard stoichiometric calibration. The SPR uncertainties were grouped into five categories according to their origins and then estimated based on previously published reports or measurements. For the first time, the impact of tissue composition variations on SPR estimation was assessed and the uncertainty estimates of each category were determined for low-density (lung), soft, and high-density (bone) tissues. A composite, 95th percentile water-equivalent-thickness uncertainty was calculated from multiple beam directions in 15 patients with various types of cancer undergoing proton therapy. The SPR uncertainties (1σ) were quite different (ranging from 1.6% to 5.0%) in different tissue groups, although the final combined uncertainty (95th percentile) for different treatment sites was fairly consistent at 3.0–3.4%, primarily because soft tissue is the dominant tissue type in the human body. The dominant contributing factor for uncertainties in soft tissues was the degeneracy of Hounsfield numbers in the presence of tissue composition variations. To reduce the overall uncertainties in SPR estimation, the use of dual-energy computed tomography is suggested. The values recommended in this study based on typical treatment sites and a small group of patients roughly agree with the commonly referenced value (3.5%) used for margin design. By using tissue-specific range uncertainties, one could estimate the beam-specific range margin by accounting for different types and amounts of tissues along a beam, which may allow for customization of range uncertainty for each beam direction.

Purpose: Range uncertainty from X-ray CT number conversion to stopping power ratio (SPR) is one of the key factors limiting the potential of proton therapy. The large margins required for deep seated tumors degrade the organ sparing achievable with the technology. Of interest is the application of dual energy CT (DECT) to SPR estimation. In this planning study proton range differences between SECT and DECT have been quantified for brain cases. Methods: A last generation dual source DECT scanner was used to acquire SECT (150 kVp with Sn filtration) and DECT (additionally 90 kVp) scans of phantoms and 5 head trauma patients, acting as surrogate cancer patients. Phantom materials were characterized in terms of SPR in a particle beam to obtain reference values. IMPT treatment plans were generated on the basis of SECT and DECT SPR images for hypothetical brain tumors using a short and a long beam path. Range differences between SECT and DECT from plan recalculations were evaluated in beam-eye-view (BEV) by comparing the 80% isodose. Results: For the 18 phantom materials the SECT RMS SPR errors were 2.6% compared to 1.1% for DECT. Group median relative range differences between SECT and DECT plans were −1.0% for the short beam path over the 5 patients investigated in this study. For the long beam path the median difference was −1.4%. These relative range differences corresponded to −0.5 mm and −1.4 mm shifts respectively. Conclusion: This is the first study performing proton therapy treatment planning on DECT patient images. Important range differences of more than 1 mm were observed between SECT and DECT treatment plans, and DECT SPR accuracy was found superior on the basis of phantom measurements. While the patients investigated in this study did not have brain tumors, the findings we observed should apply to cancer patients. Deutsche Forschungsgemeinschaft (MAP); Bundesministerium fur Bildung und Forschung (01IB13001)

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. 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. 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 140keV. 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. 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. 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.

Purpose: To analyze the uncertainties in proton stopping-power-ratios (SPRs) calculation using a kV-MV DECT and evaluate the potential for margin reduction for proton therapyMethods: Sources of uncertainties in SPR estimation were broken into five categories: CT imaging uncertainties, CT modeling error, uncertainties in the mean-excitation-energy, SPR variation with proton energy and uncertainties due to variations in human tissue compositions. Additionally, lung, soft and bone tissues were analyzed separately because their uncertainties are too different to be considered as the same tissue type. The uncertainty of 1-standard-deviation (1-SD) was determined for each category and each tissue type. To derive a realistic composite uncertainty, tissue proportions of each tissue type were measured in three treatment sites: prostate, lung and head-and-neck, with 5 proton patient cases for each site. The beam angles and treatment target in the original treatment plan were used. Ray tracing technique was used to determine the tissue proportion from the skin surface to the distal surface of the target volume. The range uncertainty was determined for each ray line, and the 95th-percentile uncertainty was determined for the population of the ray lines belonging to the same tumor site. Results: The total uncertainty in SPR estimation (1-SD) was estimated to be 3.8%, 0.8% and 1.3% for lung, soft and bone tissues, respectively. The combined range uncertainty estimates (95th-percentile) for prostate, lung and head-and-neck treatment sites were 1.6%, 2.1% and 1.6%, respectively. Conclusions: The current practice uses a 3.5% range uncertainty for SPR estimation for all treatment sites. We found it is important to separate tissue types in order to better estimate the site-specific range uncertainties. The use of kV-MV DECT can reduce current margin by half for prostate and head-and-neck sites (1.6% vs. 3.5%) but can only reduce the uncertainty by 40% for lung cancer site (2.1% vs. 3.5%). Research partially sponsored by Varian Medical Systems.

The purpose of this study is to adapt a Bayesian dual-virtual non-contrast (VNC) method by integrating prior anatomical knowledge from AI-based multi-organ segmentation and to generalize it for spectral photon-counting CT (PCCT) with an arbitrary number of energychannels. A previously proposed Bayesian VNC method is reformulated for any number of energies and adapted for integration with AI segmentation. TotalSegmentator, an open-access whole-body AI segmentation model, is used to provide spatial priors. The method is applied to simulated contrast-enhanced dual-energy CT (DECT) and PCCT datasets from eight virtual patients, with and without AI segmentation. Key radiotherapy-relevant parameters such as electron density ( ) and proton stopping power ratio (SPR) are estimated and compared to ground truth values. Additional results are obtained for non-contrast scans by setting contrast agent uptake tozero. AI-based segmentation improved the accuracy of parameter estimation for both DECT and PCCT, with a more pronounced effect for PCCT. The combination of high spectral resolution and anatomical priors led to reduced RMS errors in SPR and . Mean absolute water-equivalent path length (WEPL) errors confirmed the superiority of segmentation-assisted PCCT over othermethods. This proof of concept demonstrates a flexible, AI-assisted Bayesian framework for extracting quantitative information from contrast-enhanced spectral CT. By integrating AI segmentation and generalizing to PCCT, the method shows improved tissue characterization, suggesting the value of AI in extracting quantitative information beyond DECT. Further validation on clinical datasets isneeded. Quantitative VNC methods offer the potential to extract radiotherapy-related parameters from contrast-enhanced spectral CT without the need for additional non-contrast imaging. However, the inherently ill-posed nature of tissue characterization from limited spectral data remains a major limitation, which requires advancedtechniques.

Purpose:Dual energy CT can predict stopping power ratios (SPR) for ion therapy treatment planning. Several approaches have been proposed recently, however accuracy and practicability in a clinical workflow are unaddressed. The aim of this work is to provide a fair comparison of available approaches in a human‐like phantom to find the optimal method for tissue characterization in a clinical situation.Methods:The SPR determination accuracy is investigated using simulated DECT images. A virtual human‐like phantom is created containing 14 different standard human tissues. SECT (120 kV) and DECT images (100 kV and 140 kV Sn) are simulated using the software ImaSim. The single energy CT (SECT) stoichiometric calibration method and four recently published calibration‐based DECT methods are implemented and used to predict the SPRs from simulated images. The difference between SPR predictions and theoretical SPR are compared pixelwize. Mean, standard deviation and skewness of the SPR difference distributions are used as measures for bias, dispersion and symmetry.Results:The average SPR differences and standard deviations are (0.22 ± 1.27)% for SECT, and A) (−0.26 ± 1.30)%, B) (0.08 ± 1.12)%, C) (0.06 ± 1.15)% and D) (−0.05 ± 1.05)% for the four DECT methods. While SPR prediction using SECT is showing a systematic error on SPR, the DECT methods B, C and D are unbiased. The skewness of the SECT distribution is 0.57%, and A) −0.19%, B) −0.56%, C) −0.29% and D) −0.07% for DECT methods respectively.Conclusion:The here presented DECT methods B, C and D outperform the commonly used SECT stoichiometric calibration. These methods predict SPR accurately without a bias and within ± 1.2% (68th percentile). This indicates that DECT potentially improves accuracy of range predictions in proton therapy. A validation of these findings using clinical CT images of real tissues is necessary.

Analysis of the bias induced by voxel and unstructured mesh Monte Carlo models for the MCNP6 code in orthovoltage applications

The purpose of this work was to assess a proof of concept for a novel method for predicting proton stopping power ratios (SPRs) based on a pair of dual-energy CT generated virtual monoenergetic (VM) images. A rapid kV-switching dual-energy CT scanner was used to acquire Gemstone Spectral Imaging (GSI) and 120kV conventional single-energy CT (SECT) image data of the CIRS 062M phantom. The proposed method was applied to every possible pairing of VM images between 40 and 140keV to find the optimal energy pairs for SPR prediction in lung tissue, soft tissue, and bone. The predicted SPRs were compared against SPRs predicted from the SECT data using the conventional SECT-based method. The impact of different scan and reconstruction parameters was also investigated. The SPR residual root mean square errors (RMSE) yielded by the optimal pairs were 7.2% for lung tissue, 0.4% for soft tissue, and 0.8% for bone. While no direct comparison could be made to other DECT-based methods for SPR prediction, as these methods could not be directly implemented on a fast kV-switching system, the SPR RMSEs for soft tissue and bone in Table 4 are comparable to RMSEs reported in the literature. For the conventional SECT-based method, the SPR RMSEs were 5.9% for lung tissue, 0.9% for soft tissue, and 5.1% for bone. The proposed method is a valid alternative to, and has the potential to improve upon, the conventional SECT-based method for predicting SPRs. The formalism used in the method is applied directly, with no approximations made on our part, and requires neither prior knowledge of the spectra nor calibration with a phantom. This work presents a way of optimizing the proposed method for a specific scanner by determining the optimal energy pairs to use as input and demonstrates the method's robustness to different levels of ASiR-V, reconstruction kernels, and dose levels.

The main objective of this study is to propose an alternative parameterization for the empirical relation between mean excitation energies (I-value) and effective atomic numbers (Zeff ) of human tissues, and to present a simplified formulation (which we called DEEDZ-SPR) for deriving the stopping power ratio (SPR) from dual-energy (DE) CT data via electron density (ρe ) and Zeff calibration. We performed a numerical analysis of this DEEDZ-SPR method for the human-body-equivalent tissues of ICRU Report 46, as objects of interest with unknown SPR and ρe . The attenuation coefficients of these materials were calculated using the XCOM photon cross-sections database. We also applied the DEEDZ-SPR conversion to experimental DECT data available in the literature, which was measured for the tissue-characterization phantom using a dual-source CT scanner at 80kV and 140kV/Sn. It was found that the DEEDZ-SPR conversion enables the calculation of SPR simply by means of the weighted subtraction of an electron-density image and a low- or high-kV CT image. The simulated SPRs were in excellent agreement with the reference values over the SPR range from 0.258 (lung) to 3.638 (bone mineral-hydroxyapatite). The relative deviations from the reference SPR were within ±0.6% for all ICRU-46 human tissues, except for the thyroid that presented a -1.1% deviation. The overall root-mean-square error was 0.21%. Application to experimental DECT data confirmed this agreement within the experimental accuracy, which demonstrates the practical feasibility of the method. The DEEDZ-SPR conversion method could facilitate the construction of SPR images as accurately as a recent DECT-based calibration procedure of SPR parameterization based directly on the CT numbers in a DECT data set.

The accuracy of proton dose calculation is dependent on the ability to correctly characterize patient tissues with medical imaging. The most common method is to correlate computed tomography (CT) numbers obtained via single-energy CT (SECT) with proton stopping power ratio (SPR). CT numbers, however, cannot discriminate between a change in mass density and change in chemical composition of patient tissues. This limitation can have consequences on SPR calibration accuracy. Dual-energy CT (DECT) is receiving increasing interest as an alternative imaging modality for proton therapy treatment planning due to its ability to discriminate between changes in patient density and chemical composition. In the current work we use a phantom of known composition to demonstrate the dosimetric advantages of proton therapy treatment planning with DECT over SECT. A phantom of known composition was scanned with a clinical SECT radiotherapy CT-simulator. The phantom was rescanned at a lower X-ray tube potential to generate a complimentary DECT image set. A set of reference materials similar in composition to the phantom was used to perform a stoichiometric calibration of SECT CT number to proton SPRs. The same set of reference materials was used to perform a DECT stoichiometric calibration based on effective atomic number. The known composition of the phantom was used to assess the accuracy of SPR calibration with SECT and DECT. Intensity modulated proton therapy (IMPT) treatment plans were generated with the SECT and DECT image sets to assess the dosimetric effect of the imaging modality. Isodose difference maps and root mean square (RMS) error calculations were used to assess dose calculation accuracy. SPR calculation accuracy was found to be superior, on average, with DECT relative to SECT. Maximum errors of 12.8% and 2.2% were found for SECT and DECT, respectively. Qualitative examination of dose difference maps clearly showed the dosimetric advantages of DECT imaging, compared to SECT imaging for IMPT dose calculation for the case investigated. Quantitatively, the maximum dose calculation error in the SECT plan was 7.8%, compared to a value of 1.4% in the DECT plan. When considering the high dose target region, the root mean square (RMS) error in dose calculation was 2.1% and 0.4% for SECT and DECT, respectively. DECT-based proton treatment planning in a commercial treatment planning system was successfully demonstrated for the first time. DECT is an attractive imaging modality for proton therapy treatment planning owing to its ability to characterize density and chemical composition of patient tissues. SECT and DECT scans of a phantom of known composition have been used to demonstrate the dosimetric advantages obtainable in proton therapy treatment planning with DECT over the current approach based on SECT.

In charged-particle therapy treatment planning, the volumetric distribution of stopping power ratios (SPRs) of body tissues relative to water is used for patient dose calculation. The distribution is conventionally obtained from computed tomography (CT) images of a patient using predetermined conversion functions from the CT numbers to the SPRs. One of the biggest uncertainty sources of patient SPR estimation is insufficient correction of beam hardening arising from the mismatch between the size of the patient cross section and the calibration phantom for producing the conversion functions. The uncertainty would be minimized by selecting a suitable size for the cylindrical water calibration phantom, referred to as an ‘effective size’ of the patient cross section, Leffective. We investigated the Leffective for pelvis, abdomen, thorax, and head and neck regions by simulating an ideal CT system using volumetric models of the reference male and female phantoms. The Leffective values were 23.3, 20.3, 22.7 and 18.8 cm for the pelvis, abdomen, thorax, and head and neck regions, respectively, and the Leffective for whole body was 21.0 cm. Using the conversion function for a 21.0-cm-diameter cylindrical water phantom, we could reduce the root mean square deviation of the SPRs and their mean deviation to ≤0.011 and ≤0.001, respectively, in the whole body. Accordingly, for simplicity, the effective size of 21.0 cm can be used for the whole body, irrespective of body-part regions for treatment planning in clinical practice.

Magnetic fields are known to alter radiation dose deposition. Before patients receive treatment using an MRI-linear accelerator (MRI-Linac), preclinical studies are needed to understand the biological consequences of magnetic-field-induced dose effects. In the present study, the authors sought to identify a beam energy and magnetic field strength combination suitable for preclinical murine experiments. Magnetic field dose effects were simulated in a mouse lung phantom using various beam energies (225 kVp, 350 kVp, 662 keV [Cs-137], 2 MV, and 1.25 MeV [Co-60]) and magnetic field strengths (0.75, 1.5, and 3 T). The resulting dose distributions were compared with those in a simulated human lung phantom irradiated with a 6 or 8 MV beam and orthogonal 1.5 T magnetic field. In the human lung phantom, the authors observed a dose increase of 45% and 54% at the soft-tissue-to-lung interface and a dose decrease of 41% and 48% at the lung-to-soft-tissue interface for the 6 and 8 MV beams, respectively. In the mouse simulations, the magnetic fields had no measurable effect on the 225 or 350 kVp dose distribution. The dose increases with the Cs-137 beam for the 0.75, 1.5, and 3 T magnetic fields were 9%, 29%, and 42%, respectively. The dose decreases were 9%, 21%, and 37%. For the 2 MV beam, the dose increases were 16%, 33%, and 31% and the dose decreases were 9%, 19%, and 30%. For the Co-60 beam, the dose increases were 19%, 54%, and 44%, and the dose decreases were 19%, 42%, and 40%. The magnetic field dose effects in the mouse phantom using a Cs-137, 3 T combination or a Co-60, 1.5 or 3 T combination most closely resemble those in simulated human treatments with a 6 MV, 1.5 T MRI-Linac. The effects with a Co-60, 1.5 T combination most closely resemble those in simulated human treatments with an 8 MV, 1.5 T MRI-Linac.

Quality assurance in magnetic resonance (MR)-guided radiotherapy lacks anthropomorphic phantoms that represent tissue-equivalent imaging contrast in both computed tomography (CT) and MR imaging. In this study, we developed phantom materials with individually adjustable CT value as well as T1 - and T2 -relaxation times in MR imaging at three different magnetic field strengths. Additionally, their experimental stopping power ratio (SPR) for carbon ions was compared with predictions based on single- and dual-energy CT. Ni-DTPA doped agarose gels were used for individual adjustment of T1 and T2 at 0.35,1.5 and 3.0 T. The CT value was varied by adding potassium chloride (KCl). By multiple linear regression, equations for the determination of agarose, Ni-DTPA and KCl concentrations for given T1, T2 and CT values were derived and employed to produce nine specific soft tissue samples. Experimental T1, T2 and CT values of these soft tissue samples were compared with predictions and additionally, carbon ion SPR obtained by range measurements were compared with predictions based on single- and dual-energy CT. The measured CT value, T1 and T2 of the produced soft tissue samples agreed very well with predictions based on the derived equations with mean deviations of less than 3.5%. While single-energy CT overestimates the measured SPR of the soft tissue samples, the dual-energy CT-based predictions showed a mean SPR deviation of only 0.2±0.3%. To conclude, anthropomorphic phantom materials with independently adjustable CT values as well as T1 and T2 relaxation times at three different magnetic field strengths were developed. The derived equations describe the material specific relaxation times and the CT value in dependence on agarose, Ni-DTPA and KCl concentrations as well as the chemical composition of the materials based on given T1,T2 and CT value. Dual-energy CT allows accurate prediction of the carbon ion range in these materials.

The measurement of stopping power ratio in oxides by 16O(α, α) 16O reaction for channelling He ions

Purpose: To reduce uncertainties in CT data conversion to stopping power ratio (SPR) and tissue composition when using single energy CT (SECT), this study presents a first approach of exploiting dual energy CT (DECT) data for Monte Carlo (MC) based dose calculation in particle therapy. Methods: DECT image data can be converted into an electron density and effective atomic number image. With both tissue parameters elemental weights of 71 tabulated tissues were predicted by dedicated linear fits for each element. The mass density was derived via a single linear fit of the electron density from adipose on. Dose calculations with the MC system TOPAS were performed using monoenergetic pencil beams (protons and carbons) stopped in 12 selected tissues which showed considerable differences in composition predictions from single and dual energy CT. Ranges were compared with predictions from the novel DECT approach and the standard SECT. Results: Predictions of mass density, carbon and oxygen elemental weights profit highly from the additional DECT information. Maximum differences to the true mass density could be reduced from 3.0% (SECT) to 0.9% (DECT) for soft tissue and from 0.7% (SECT) to 0.1% (DECT) for bone tissue. Furthermore, DECT shows a significant improvement also in common tissues: range uncertainties in cartilage were reduced from 1.7% (SECT) to 0.2% (DECT), in yellow marrow from 1.7% (SECT) to 0.5% (DECT), in liver from 0.9% (SECT) to 0.2% (DECT), in brain cerebroisal fluid from 2.2% (SECT) to 0.0% (DECT) and in femur the predicted range deviates by 0.8% (SECT) to 0.0% (DECT) from the true value. Conclusion: DECT can provide more accurate material compositions and mass density as compared to single energy CT. Therewith, DECT information can significantly reduce range uncertainties in particle therapy and can lead to a reduction in currently applied range margins in proton and carbon ion therapy.