Stoichiometric Calibration Method Research Articles

Impact of iodinated oil in proton therapy on relative stopping power of liver post-cTACE

Objective. Conventional transarterial chemoembolization (cTACE) is a common treatment for hepatocellular carcinoma (HCC), often with unsatisfactory local controls. Combining cTACE with radiotherapy shows a promise for unresectable large HCC, with proton therapy preserving healthy liver tissue. However, the proton therapy benefits are subject to the accuracy of tissue relative stopping power (RSP) prediction. The RSP values are typically derived from computed tomography (CT) images using stoichiometric calibration. Lipiodol deposition significantly increases CT numbers in liver regions of post-cTACE. Hence, it is necessary to evaluate the accuracy of RSP in liver regions of post-cTACE. Approach. Liver, water, and iodinated oil samples were prepared. Some liver samples contained iodinated oil. The water equivalent path length (WEPL) of sample was measured through the pullbacks of spread-out Bragg peak (SOBP) depth-dose profiles scanned in a water tank with and without sample in the beam path. Measured RSP values were compared to estimated RSP values derived from the CT number based on the stoichiometric calibration method. Main results. The measured RSP of water was 0.991, confirming measurement system calibration. After removing the RSP contribution from container walls, the pure iodinated oil and liver samples had RSP values of 1.12 and 1.06, while the liver samples mixed with varying oil volumes (5 ml, 10 ml, 15 ml) showed RSP values of 1.05, 1.05 and 1.06. Using the stoichiometric calibration method, pure iodinated oil and liver samples had RSP values of 2.79 and 1.06. Liver samples mixed with iodinated oil (5 ml, 10 ml, 15 ml) had calculated RSP values of 1.21, 1.34, and 1.46. The RSP discrepancy reached 149.1% for pure iodinated oil. Significance. Iodinated oil notably raises CT numbers in liver tissue. However, there is almost no effect on its RSP value. Proton treatment of post-cTACE HCC patients can therefore be overshooting if no proper measures are taken against this specific effect.

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

BackgroundThe 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.ObjectivesThe 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.MethodsArtificial 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.ResultsFor 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%. ConclusionThe 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 knowledgeDeep learning-based frameworks are proposed to estimate material mass density and RSP from SECT with improved accuracy compared with conventional methods.

Accuracy of CT numbers and electron density calibration for mixtures of materials with low and high-atomic number

Abstract In computed tomography (CT) materials with high-atomic number Z cause image artefacts, thus, errors in CT numbers given in Hounsfield Units (HU). Also, the conventional HU scale (CHU) implemented in CT scanners is truncated, i.e., it does not cover high-Z materials. These restrictions lead to incorrect mapping of CT numbers to electron density, which are used in radiotherapy (RT) treatment planning systems (TPS). Even analytical conversion methods are only permissible for tissue-equivalent materials. In terms of HU-to-density conversion in RT TPS, we investigated the CT numbers of material mixtures up to Z<29 at the CHU and an extended-HU (EHU) scale, respectively, and quantify the systematic errors of image artefacts. In [1] the feasibility of a stoichiometric analytical calibration method were analyzed for metals and adapted for higher accurcy, for energies of 80 kV and 120 kV. In this work, we add results for 100 kV and 140 kV to cover the wide diagnostic range. The CT numbers are effected by physical and machine-based properties and depend strongly on the energy, e.g., for Cu a HU difference of 6 171HU at 80 kV and 140 kV occured. The analytical calibration parameters change with energy by a factor between 2 and 10 depending on the physical process. Although for high- Z materials our calibration procedure remains in conflict with rigorous physics [2], it offers an improved and a practical way to predict electron densities from CT numbers.

Error on the stopping power ratio of ERKODENT's mouthpiece for head and neck carbon ion radiotherapy treatment

The errors on the stopping power ratio (SPR) of mouthpiece samples from ERKODENT were evaluated. Erkoflex and Erkoloc-pro from ERKODENT and samples that combined Erkoflex and Erkoloc-pro were computed tomography (CT)-scanned using head and neck (HN) protocol at the East Japan Heavy Ion Center (EJHIC), and the values were averaged to obtain the CT number. The integral depth dose of the Bragg curve with and without these samples was measured for 292.1, 180.9, and 118.8 MeV/u of the carbon-ion pencil beam using an ionization chamber with concentric electrodes at the horizontal port of the EJHIC. The average value of the water equivalent length (WEL) of each sample was obtained from the difference between the range of the Bragg curve and the thickness of the sample. To calculate the difference between the theoretical and measured values, the theoretical CT number and SPR value of the sample were calculated using the stoichiometric calibration method. Compared with the Hounsfield unit (HU)-SPR calibration curve used at the EJHIC, the SPR error on each measured and theoretical value was calculated. The WEL value of the mouthpiece sample had an error of approximately 3.5% in the HU-SPR calibration curve. From this error, it was evaluated that for a mouthpiece with a thickness of 10mm, a beam range error of approximately 0.4mm can occur, and for a mouthpiece with a thickness of 30mm, a beam range error of approximately 1mm can occur. For a beam passing through the mouthpiece in HN treatment, it would be practical to consider a mouthpiece margin of 1mm to avoid beam range errors if ions pass through the mouthpiece.

Identification of Common Minerals Using Stoichiometric Calibration Method for Dual‐Energy CT

AbstractMedical X‐ray computed tomography (CT) can be used to rapidly and non‐destructively characterize structure and density variations of geological specimens. More information about the nature of samples (electron density and elemental composition) can be retrieved using multi‐spectral approaches. This paper explores one of them, a stoichiometric calibration method for dual‐energy imaging, to identify the most common minerals. A set of 18 calibrating materials was selected to cover a range of variability in effective atomic number (Zeff) and electron density (ρe) encountered in geological specimens. The validation of this calibration was performed analyzing 23 common minerals by mapping their respective Zeff and ρe in order to identify the one with the closest properties. This study shows that the stoichiometric method correctly identifies the most important and common minerals (quartz, calcite, dolomite) that are usually not distinguishable using a single energy imaging method, although all the 23 studied minerals were not correctly determined. We show that this method previously elaborated for medical purposes is also efficient in earth science.

Electron density and effective atomic number estimation in a maximum a posteriori framework for dual-energy computed tomography.

The stoichiometric calibration method for dual-energy CT (DECT) proposed by Bourque et al. (Phys Med Biol. 59:2059; 2014), which provides estimators of the electron density and the effective atomic number, is adapted to a maximum a posteriori (MAP) framework to increase the model's robustness to noise and biases in CT data, specifically for human tissues. Robust physical parameter estimation from noisy DECT scans is required to maximize the precision of quantities used for radiotherapy treatment planning such as the proton stopping power (SPR). Estimation of electron density and effective atomic number is performed by constraining their variation to the natural range of values expected for human tissues, while maximizing attenuation data fidelity. The MAP framework is first compared against the original method using theoretical CT numbers with Gaussian noise. The quantitative accuracy of the MAP framework is then validated experimentally on the Gammex 467 phantom. Then, using two clinical datasets, the advantages of the approach are experimentally evaluated, qualitatively, and quantitatively. The theoretical study shows that the root-mean-square error on the electron density, the effective atomic number and the SPR are, respectively, reduced from 2.3 to 1.5, 5.7 to 3.2 and 2.8 to 1.7% with the adapted framework, when analyzing soft tissues and bone together. The experimental validation study shows that the standard deviation in Gammex inserts can be reduced, on average, by factors of 1.4 (electron density), 2.7 (effective atomic number), and 1.9 (SPR), while the quantitative accuracy of the three physical parameters is preserved, on average. Evaluation on clinical datasets show apparent noise reduction in maps of all estimated physical quantities, and suggests that the MAP framework has increased robustness to beam hardening and photon starvation artifacts. Mean values for the electron density, the effective atomic number, and the SPR averaged in four uniform regions of interest (brain, muscle, adipose, and cranium), respectively, differ by 0.7, 1.8, and 0.9% between both frameworks. The standard deviation in the same regions of interest is also reduced, on average, by factors of 1.8, 6.6, and 3.2 with the MAP framework. Differences in mean value and standard deviations are statistically significant. Theoretical and experimental results suggest that the MAP framework produces more accurate and precise estimates of the electron density and SPR. Thus, the present approach limits the propagation of noise in DECT attenuation data to radiotherapy-related parameters maps such as the SPR and the electron density. Using a MAP framework with DECT for radiotherapy treatment planning can help maximizing the precision of dose calculation. The method also provides more precise estimates of the effective atomic number. The MAP methodology is presented in a general way such that it can be adapted to any DECT image-based tissue characterization method.

Optimal energy selection for proton stopping-power-ratio estimation using dual-energy CT-based monoenergetic imaging

The dual-energy computed tomography (DECT)-based approach holds promise in reducing the overall uncertainty in proton stopping-power-ratio (SPR) estimation, but cannot be easily implemented with most commercial proton treatment planning systems (TPS). In this study, we revisited the idea of coupling the stoichiometric calibration method with virtual monoenergetic CT datasets (MonoCT) generated by modern DECT scanners, because of its readiness for implementation with the existing TPS. Our objective was to determine the optimal energy of the MonoCT dataset for stoichiometric calibration and estimate the overall uncertainty in SPR estimation at the optimal energy. We performed stoichiometric calibration for MonoCT datasets across the energy range available on a Siemens Force DECT scanner and a Philips IQon DECT scanner in a 10 keV step. We estimated the uncertainties of different sources (imaging, modeling, and inherent uncertainties) for different tissue types (lung, soft, and bone tissues) associated with each energy; these were then combined into a single composite uncertainty for three tumor sites (head-and-neck (HN), lung, and prostate). The optimal energy was eventually selected based on the composite range uncertainty, which turned out to be 160 keV for both DECT scanners. At 160 keV, the total uncertainties (2σ) in SPR estimation were determined to be 3.2%–4.5%, 0.9%, and 1.4%–1.6% for lung, soft, and bony tissues, respectively. These results were comparable to the corresponding values estimated for the DECT approach evaluated in our previous study: 3.8%, 1.2% and 2.0%, for lung, soft, and bony tissues, respectively. The composite range uncertainties (2σ) were estimated as 1.5%, 1.7%, and 1.5% for prostate, lung, and HN, respectively. Our results demonstrated the potential of MonoCT images for reducing proton SPR uncertainty. Further clinical studies are needed to compare this approach with the DECT approach directly on real patient cases.

Characterization of electron density of the real tissues for radiotherapy planning using dual energy algorithm and stoichiometric calibration method

Introduction: Characterization of the relative electron density (𝜌e) of the body tissues is routinely provided by scanning the commercial phantom like RMI 467 at 120 kilovoltage (kVp) in radiotherapy planning. Recent studies showed that the calibrating Hounsfield Unit– 𝜌e curve could be obtained linearly using dual energy computed tomography (DECT) algorithm in commercial phantom like RMI 467. The aim of this study was to produce a more accurate calibrating HU–𝜌e curve by constructing an in- house phantom and applying dual energy algorithm. Materials and Methods: An in-house water filled phantom (33cm diameter) was made including ten water solutions plus composite cork as tissue substitute materials (TSM), and scanned at four kVps by multi-detector four slice CT. The dual energy algorithm was applied to two combination scans (80-140 and 100-140 kVp) and the linear HU–𝜌e curves were produced. The stoichiometric method reproduced the HUs of 31 real body tissues, that their compositions are available in ICRU-46 report. The HU–𝜌e curves for both kVp combination scans were produced. The t-test and compare means were performed between the mean and standard deviation of the relative and absolute differences (%) of the 𝜌e of 31 ICRU real tissues calculated for 120 kVp and both kVp combination scans in the current and previous studies, respectively Results: Applying an energy subtraction algorithm mitigated the 𝜌e calculation error of real tissues. The mean and standard deviation of the relative difference between the 𝜌e of 31 ICRU tissues (–0.23±1.89) were statistically significant compared with the mean and standard deviation of the 30 ICRU tissues (0.80±1.58), which extracted from the RMI 467 phantom at 120 kVp in previous study (p<0.024). The mean and standard deviation of absolute differences of the calculated 𝜌e of the 31 ICRU tissues at 100-140 kVp combination scans (0.14±0.11) compared to previous study using 100-140kVp scan (0.30±0.40) in a second generation dual source CT, were statistically significant (P<0.035). Conclusion: The stoichiometric calibration method and closeness of the 𝜌e of 11 TSMs can result in statistically significant smaller discrepancies in calculating the 𝜌e of real tissues at 120 kVp, compared to the previous studies with RMI 467. The stoichiometric fitting parameters were highly affected by beam hardening artifacts and image noise, especially at 80 kVp. Applying the energy subtraction algorithm can offer further error mitigation in 𝜌e calculation of real tissues by spectral separation and reduction of beam hardening artifacts and noise in two kVp combination scans compared to previous studies. Therefore, a dual energy algorithm in combination with stoichiometry can be used to decrease errors in calculation of the 𝜌e of real tissues, and could be used for radiotherapy planning and material differentiation in clinical practice.

Revisiting the single-energy CT calibration for proton therapy treatment planning: a critical look at the stoichiometric method

Despite extensive research in dual-energy computed tomography (CT), single-energy CT (SECT) is still the standard imaging modality in proton therapy treatment planning and, in this context, the stoichiometric calibration method is considered to be the most accurate to establish a relationship between CT numbers and proton stopping power. This work revisits the SECT calibration for proton therapy treatment planning, with special emphasis on the stoichiometric method. Three different sets of tissue-substitutes of known elemental composition (Gammex, CIRS and Catphan) were scanned with the same scanning protocol. A stoichiometric fit was performed for each set of tissue-substitutes. Based on that, the CT number, relative electron density and relative proton stopping power were calculated for ICRU 46 biological tissues and the different sets of tissue-substitutes. Despite common belief, it was found that the stoichiometric fit depends on the elemental composition of the tissue-substitutes used in the calibration, leading to differences in relative stopping power up to 3.5% for cortical bone. In addition, according to Rutherford et al (1976 Neuroradiology 11 15–21) parametrization of the atomic cross-section, CT numbers of Gammex tissue-substitutes and ICRU 46 biological tissues were found to be similar within the whole energy range relevant to computed tomography. Consequently, it was found that, for Gammex tissue-substitutes, the CT calibration curve resulting from the stoichiometric method agrees with that obtained by simple interpolation of experimental data. In conclusion, the stoichiometric method for SECT calibration seems to depend on the tissue-substitutes used for calibration—which could be regarded as an additional source of uncertainty in proton range for bone tissues. Furthermore, Gammex tissue-substitutes appear to be a good representative of biological tissues within the energy range relevant to computed tomography—making the stoichiometric method unnecessary.

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

The purpose of this work is to evaluate the impact of single-, dual- and multi-energy CT (SECT, DECT and MECT) on proton range uncertainties in a patient like geometry and a full Monte Carlo environment. A virtual patient is generated from a real patient pelvis CT scan, where known mass densities and elemental compositions are overwritten in each voxel. Simulated CT images for SECT, DECT and MECT are generated for two limiting cases: (1) theoretical and idealistic CT numbers only affected by Gaussian noise (case A, the best scenario) and (2) reconstructed polyenergetic sinograms containing beam hardening, projection-based Poisson noise, and reconstruction artifacts (case B, the worst scenario). Conversion of the simulated SECT images into Monte Carlo inputs is done following the stoichiometric calibration method. For DECT and MECT, the Bayesian eigentissue decomposition method of Lalonde (2017 Med. Phys. 44 5293–302) is used. Pencil beams from seven different angles around the virtual patient are simulated using TOPAS to assess the performance of each method. Percentage depth doses curves (PDD) are compared to ground truth in order to determine the accuracy of range prediction of each imaging modality. For the idealistic images of case A, MECT and DECT slightly outperforms SECT. Root mean square (RMS) errors or 0.78 mm, 0.49 mm and 0.42 mm on R80 mm, are observed for SECT, DECT and MECT respectively. In case B, PDD calculated in the MECT derived Monte Carlo inputs generally shows the best agreement with ground truth in both shape and position, with RMS errors of 2.03 mm, 1.38 mm and 0.86 mm for SECT, DECT and MECT respectively. Overall, the Bayesian eigentissue decomposition used with DECT systematically predicts proton ranges more accurately than the gold standard SECT-based approach. When CT numbers are severely affected by imaging artifacts, MECT with four energy bins becomes more reliable than both DECT and SECT.

Simultaneous characterization of electron density and effective atomic number for radiotherapy planning using stoichiometric calibration method and dual energy algorithms.

Relative electron densities of body tissues (ρe) for radiotherapy treatment planning are normally obtained by CT scanning of tissue substitute materials (TSMs) and producing a Hounsfield Unit-ρe calibration curve. Aiming for more accurate, simultaneous characterization of ρe and effective atomic number (Zeff) of real tissues, an in-house phantom (including 10 water solutions plus composite cork as TSMs) was constructed and scanned at 4kVps. Dual-energy algorithms were applied to 80-140 and 100-140kVp combination scans, for better differentiation of tissues with same attenuation coefficient at 120kVp but different ρe and Zeff. Stoichiometric calibration and closeness of the ρe of the 11 TSMs to real tissues (≤ 0.5%) resulted in smaller ρe calculation discrepancies, compared to studies with commercial phantoms (p < 0.024). Applying an energy subtraction algorithm further mitigated errors by spectral separation and reduction of beam hardening artifacts and noise, reducing the mean and standard deviation of the absolute difference of ρe at 80-140kVp (p < 0.003) and 100-140 kVp (p < 0.0001) scans, compared to 120 kVp scan, respectively. Moreover, a parametrization algorithm decreased the Zeff discrepancy from real tissues at 80-140 kVp scans; for thyroid, the residual error was ≤ 0.18 units of Zeff (vs. 0.2 with the Gammex 467 phantom from a previous study). These results further suggest that a dual-energy algorithm in combination with stoichiometry can decrease errors in calculation of the ρe of real tissues to ameliorate inhomogeneity for dose calculation in radiotherapy treatment planning, especially when the energy spectrum of the X-ray tube of the CT machine is not available.

Ex vivo validation of a stoichiometric dual energy CT proton stopping power ratio calibration

A major source of uncertainty in proton therapy is the conversion of Hounsfield unit (HU) to proton stopping power ratio relative to water (SPR). In this study, we measured and quantified the accuracy of a stoichiometric dual energy CT (DECT) SPR calibration.We applied a stoichiometric DECT calibration method to derive the SPR using CT images acquired sequentially at and . The dual energy index was derived based on the HUs of the paired spectral images and used to calculate the effective atomic number (Zeff), relative electron density (), and SPRs of phantom and biological materials. Two methods were used to verify the derived SPRs. The first method measured the sample’s water equivalent thicknesses to deduce the SPRs using a multi-layer ion chamber (MLIC) device. The second method utilized Gafchromic EBT3 film to directly compare relative ranges between sample and water after proton pencil beam irradiation. Ex vivo validation was performed using five different types of frozen animal tissues with the MLIC and three types of fresh animal tissues using film. In addition, the residual ranges recorded on the film were used to compare with those from the treatment planning system using both DECT and SECT derived SPRs.Bland–Altman analysis indicates that the differences between DECT and SPR measurement of tissue surrogates, frozen and fresh animal tissues has a mean of 0.07% and standard deviation of 0.58% compared to 0.55% and 1.94% respectively for single energy CT (SECT) and SPR measurement.Our ex vivo study indicates that the stoichiometric DECT SPR calibration method has the potential to be more accurate than SECT calibration under ideal conditions although beam hardening effects and other image artifacts may increase this uncertainty.

Dosimetric impact of dual-energy CT tissue segmentation for low-energy prostate brachytherapy: a Monte Carlo study

The purpose of this study is to evaluate the impact of a novel tissue characterization method using dual-energy over single-energy computed tomography (DECT and SECT) on Monte Carlo (MC) dose calculations for low-dose rate (LDR) prostate brachytherapy performed in a patient like geometry. A virtual patient geometry is created using contours from a real patient pelvis CT scan, where known elemental compositions and varying densities are overwritten in each voxel. A second phantom is made with additional calcifications. Both phantoms are the ground truth with which all results are compared. Simulated CT images are generated from them using attenuation coefficients taken from the XCOM database with a 100 kVp spectrum for SECT and 80 and 140Sn kVp for DECT. Tissue segmentation for Monte Carlo dose calculation is made using a stoichiometric calibration method for the simulated SECT images. For the DECT images, Bayesian eigentissue decomposition is used. A LDR prostate brachytherapy plan is defined with 125I sources and then calculated using the EGSnrc user-code Brachydose for each case. Dose distributions and dose-volume histograms (DVH) are compared to ground truth to assess the accuracy of tissue segmentation. For noiseless images, DECT-based tissue segmentation outperforms the SECT procedure with a root mean square error (RMS) on relative errors on dose distributions respectively of 2.39% versus 7.77%, and provides DVHs closest to the reference DVHs for all tissues. For a medium level of CT noise, Bayesian eigentissue decomposition still performs better on the overall dose calculation as the RMS error is found to be of 7.83% compared to 9.15% for SECT. Both methods give a similar DVH for the prostate while the DECT segmentation remains more accurate for organs at risk and in presence of calcifications, with less than 5% of RMS errors within the calcifications versus up to 154% for SECT. In a patient-like geometry, DECT-based tissue segmentation provides dose distributions with the highest accuracy and the least bias compared to SECT. When imaging noise is considered, benefits of DECT are noticeable if important calcifications are found within the prostate.

Investigation of real tissue water equivalent path lengths using an efficient dose extinction method

For proton therapy, an accurate conversion of CT HU to relative stopping power (RSP) is essential. Validation of the conversion based on real tissue samples is more direct than the current practice solely based on tissue substitutes and can potentially address variations over the population. Based on a novel dose extinction method, we measured water equivalent path lengths (WEPL) on animal tissue samples to evaluate the accuracy of CT HU to RSP conversion and potential variations over a population. A broad proton beam delivered a spread out Bragg peak to the samples sandwiched between a water tank and a 2D ion-chamber detector. WEPLs of the samples were determined from the transmission dose profiles measured as a function of the water level in the tank. Tissue substitute inserts and Lucite blocks with known WEPLs were used to validate the accuracy. A large number of real tissue samples were measured. Variations of WEPL over different batches of tissue samples were also investigated. The measured WEPLs were compared with those computed from CT scans with the Stoichiometric calibration method. WEPLs were determined within ±0.5% percentage deviation (% std/mean) and ±0.5% error for most of the tissue surrogate inserts and the calibration blocks. For biological tissue samples, percentage deviations were within ±0.3%. No considerable difference (<1%) in WEPL was observed for the same type of tissue from different sources. The differences between measured WEPLs and those calculated from CT were within 1%, except for some bony tissues. Depending on the sample size, each dose extinction measurement took around 5 min to produce ~1000 WEPL values to be compared with calculations. This dose extinction system measures WEPL efficiently and accurately, which allows the validation of CT HU to RSP conversions based on the WEPL measured for a large number of samples and real tissues.

A general method to derive tissue parameters for Monte Carlo dose calculation with multi-energy CT

To develop a general method for human tissue characterization with dual- and multi-energy CT and evaluate its performance in determining elemental compositions and quantities relevant to radiotherapy Monte Carlo dose calculation. Ideal materials to describe human tissue are obtained applying principal component analysis on elemental weight and density data available in literature. The theory is adapted to elemental composition for solving tissue information from CT data. A novel stoichiometric calibration method is integrated to the technique to make it suitable for a clinical environment. The performance of the method is compared with two techniques known in literature using theoretical CT data. In determining elemental weights with dual-energy CT, the method is shown to be systematically superior to the water-lipid-protein material decomposition and comparable to the parameterization technique. In determining proton stopping powers and energy absorption coefficients with dual-energy CT, the method generally shows better accuracy and unbiased results. The generality of the method is demonstrated simulating multi-energy CT data to show the potential to extract more information with multiple energies. The method proposed in this paper shows good performance to determine elemental compositions from dual-energy CT data and physical quantities relevant to radiotherapy dose calculation. The method is particularly suitable for Monte Carlo calculations and shows promise in using more than two energies to characterize human tissue with CT.

TU-AB-BRC-03: Accurate Tissue Characterization for Monte Carlo Dose Calculation Using Dual-and Multi-Energy CT Data

Purpose: To develop a general method for human tissue characterization with dual-and multi-energy CT and evaluate its performance in determining elemental compositions and the associated proton stopping power relative to water (SPR) and photon mass absorption coefficients (EAC). Methods: Principal component analysis is used to extract an optimal basis of virtual materials from a reference dataset of tissues. These principal components (PC) are used to perform two-material decomposition using simulated DECT data. The elemental mass fraction and the electron density in each tissue is retrieved by measuring the fraction of each PC. A stoichiometric calibration method is adapted to the technique to make it suitable for clinical use. The present approach is compared with two others: parametrization and three-material decomposition using the water-lipid-protein (WLP) triplet. Results: Monte Carlo simulations using TOPAS for four reference tissues shows that characterizing them with only two PC is enough to get a submillimetric precision on proton range prediction. Based on the simulated DECT data of 43 references tissues, the proposed method is in agreement with theoretical values of protons SPR and low-kV EAC with a RMS error of 0.11% and 0.35%, respectively. In comparison, parametrization and WLP respectively yield RMS errors of 0.13% and 0.29% on SPR, and 2.72% and 2.19% on EAC. Furthermore, the proposed approach shows potential applications for spectral CT. Using five PC and five energy bins reduces the SPR RMS error to 0.03%. Conclusion: The proposed method shows good performance in determining elemental compositions from DECT data and physical quantities relevant to radiotherapy dose calculation and generally shows better accuracy and unbiased results compared to reference methods. The proposed method is particularly suitable for Monte Carlo calculations and shows promise in using more than two energies to characterize human tissue with CT.

SU‐F‐J‐195: On the Performance of Four Dual Energy CT Formalisms for Extracting Proton Stopping Powers

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.

TU-FG-BRB-01: Dual Energy CT Proton Stopping Power Ratio Calibration and Validation with Animal Tissues

Purpose: The conversion of Hounsfield Unit (HU) to proton stopping power ratio (SPR) is a main source of uncertainty in proton therapy. In this study, the SPRs of animal tissues were measured and compared with prediction from dual energy CT (DECT) and single energy CT (SECT) calibrations. Methods: A stoichiometric calibration method for DECT was applied to predict the SPR using CT images acquired at 80 kVp and 140 kVp. The dual energy index was derived based on the HUs of the paired spectral images and used to calculate the SPRs of the materials. Tissue surrogates with known chemical compositions were used for calibration, and animal tissues (pig brain, liver, kidney; veal shank, muscle) were used for validation. The materials were irradiated with proton pencil beams, and SPRs were deduced from the residual proton range measured using a multi-layer ion chamber device. In addition, Gafchromic EBT3 films were used to measure the distal dose profiles after irradiation through the tissue samples and compared with those calculated by the treatment planning system using both DECT and SECT predicted SPRs. Results: The differences in SPR between DECT prediction and measurement were −0.31±0.36% for bone, 0.47±0.42% for brain, 0.67±0.15% for liver, 0.51±0.52% for kidney, and −0.96±0.15% for muscle. The corresponding results using SECT were 3.1±0.12%, 1.90±0.45%, −0.66±0.11%, 2.33±0.21%, and −1.70±0.17%. In the film measurements, average distances between film and calculated distal dose profiles were 0.35±0.12 mm for DECT calibration and −1.22±0.12 mm for SECT calibration for a beam with a range of 15.79 cm. Conclusion: Our study indicates that DECT is superior to SECT for proton SPR prediction and has the potential to reduce the range uncertainty to less than 2%. DECT may permit the use of tighter distal and proximal range uncertainty margins for treatment, thereby increasing the precision of proton therapy.

Real patient data based cross verification of kilovoltage and megavoltage CT calibration for proton therapy

PurposeWe propose a methodology to evaluate the stoichiometric calibration method on MVCT against the corresponding kVCT calibration using patient data. MethodsStoichiometric calibrations were conducted for a MVCT and a kVCT scanner, respectively. We retrospectively analyzed kVCT and MVCT images of 21 patients by picking small tissue volumes in kVCT images and performing image registration to locate the tissue volumes in corresponding MVCT images. We computed the difference between the mean proton stopping power derived through kVCT and MVCT calibration, taking into account the uncertainties in calibration, imaging, and image registration. ResultskVCT and MVCT calibration curves were in good agreement for soft tissues such as muscle and brain, but showed statistically significant difference (p < 0.05) in stopping power of adipose (2.4 ± 1.7%) and bony structures such as spongiosa, and cranium (−3.2 ± 1.4 and −3.1 ± 2.1%, respectively). ConclusionThe MVCT calibration might not agree with the corresponding kVCT calibration for some tissues.

SU‐E‐T‐391: Evaluation of Image Parameters Impact On the CT Calibration Curve for Proton Therapy

Purpose:The calibration of the Hounsfield units (HU) to relative proton stopping powers (RSP) is a crucial component in assuring the accurate delivery of proton therapy dose distributions to patients. The purpose of this work is to assess the uncertainty of CT calibration considering the impact of CT slice thickness, position of the plug within the phantom and phantom sizes.Methods:Stoichiometric calibration method was employed to develop the CT calibration curve. Gammex 467 tissue characterization phantom was scanned in Tomotherapy Cheese phantom and Gammex 451 phantom by using a GE CT scanner. Each plug was individually inserted into the same position of inner and outer ring of phantoms at each time, respectively. 1.25 mm and 2.5 mm slice thickness were used. Other parameters were same.Results:HU of selected human tissues were calculated based on fitted coefficient (Kph, Kcoh and KKN), and RSP were calculated according to the Bethe‐Bloch equation. The calibration curve was obtained by fitting cheese phantom data with 1.25 mm thickness. There is no significant difference if the slice thickness, phantom size, position of plug changed in soft tissue. For boney structure, RSP increases up to 1% if the phantom size and the position of plug changed but keep the slice thickness the same. However, if the slice thickness varied from the one in the calibration curve, 0.5%–3% deviation would be expected depending on the plug position. The Inner position shows the obvious deviation (averagely about 2.5%).Conclusion:RSP shows a clinical insignificant deviation in soft tissue region. Special attention may be required when using a different slice thickness from the calibration curve for boney structure. It is clinically practical to address 3% deviation due to different thickness in the definition of clinical margins.