Proton Stopping Power Research Articles

Calculation of the Relative Stopping Power of Protons and Alpha Particles In Water

n this research we studied and calculatedtherelative stopping power(RSP) of protons and alpha particles in water with energy (35-150)MeV by calculating the range of these particles in water and air, where the range of protons in water calculated by usePSTARprogram and ASTARprogram to calculate the range of alpha particles in water for the same range of energy .The range of protons in air calculate by semi empirical equation while by using Bragg and Kleeman ruleincalculation the range of alpha particle in air.

Electronic stopping power of protons and alpha particles in nickel

The electronic stopping power of nickel for protons and alpha particles at velocities below and around the Fermi velocity has been obtained to high accuracy using time-dependent density functional theory. For the wide range of projectile velocities considered, we observed different regimes of electronic stopping due to the alternative participation of $s$- and $d$-band electrons. Despite the sharp discontinuity in the electronic density of states near the Fermi energy characteristic of the nickel band structure, we do not find an anomalous nonlinear electronic stopping power limit as a function of velocity. However, we find a crossover region above $v=0.15\phantom{\rule{0.28em}{0ex}}\mathrm{a}.\mathrm{u}.$ both for protons and alpha particles, related to the increase in participating host electrons and, in the case of alpha particles, to an increase of the charge state. We compare our calculated results with widely available experimental data and analyze the low velocity limits in the context of Lindhard's linear response theory and previous nonlinear density functional calculations. The comparison shows good accord with the lowest velocity experiments available. This may indicate that the adiabatic local density approximation is already a good theory to calculate electronic stopping power in materials at low velocity.

Experimental implementation of a joint statistical image reconstruction method for proton stopping power mapping from dual-energy CT data.

To experimentally commission a dual-energy CT (DECT) joint statistical image reconstruction (JSIR) method, which is built on a linear basis vector model (BVM) of material characterization, for proton stopping power ratio (SPR) estimation. The JSIR-BVM method builds on the relationship between the energy-dependent photon attenuation coefficients and the proton stopping power via a pair of BVM component weights. The two BVM component images are simultaneously reconstructed from the acquired DECT sinograms and then used to predict the electron density and mean excitation energy (I-value), which are required by the Bethe equationfor SPR computation. A post-reconstruction image-based DECT method, which utilizes the two separate CT images reconstructed via the scanner's software, was implemented for comparison. The DECT measurement data were acquired on a Philips Brilliance scanner at 90 and 140kVp for two phantoms of different sizes. Each phantom contains 12 different soft and bony tissue surrogates with known compositions. The SPR estimation results were compared to the reference values computed from the known compositions. The difference of the computed water equivalent path lengths (WEPL) across the phantoms between the two methods was also compared. The overall root-mean-square (RMS) of SPR estimation error of the JSIR-BVM method are 0.33% and 0.37% for the head- and body-sized phantoms, respectively, and all SPR estimates of the test samples are within 0.7% of the reference ground truth. The image-based method achieves overall RMS errors of 2.35% and 2.50% for the head- and body-sized phantoms, respectively. The JSIR-BVM method also reduces the pixel-wise random variation by 4-fold to 6-fold within homogeneous regions compared to the image-based method. The average differences between the JSIR-BVM method and the image-based method are 0.54% and 1.02% for the head- and body-sized phantoms, respectively. By taking advantage of an accurate polychromatic CT data model and a model-based DECT statistical reconstruction algorithm, the JSIR-BVM method accounts for both systematic bias and random noise in the acquired DECT measurement data. Therefore, the JSIR-BVM method achieves good accuracy and precision on proton SPR estimation for various tissue surrogates and object sizes. In contrast, the experimentally achievable accuracy of the image-based method may be limited by the uncertainties in the image formation process. The result suggests that the JSIR-BVM method has the potential for more accurate SPR prediction compared to post-reconstruction image-based methods in clinical settings.

311. Preliminary results of the production of Ga-68 with biomedical cyclotron by liquid target

Purpose The aim of this work was to evaluate the feasibility of production of 68Ga via the 68Zn(p,n)68Ga reaction by proton irradiation of enriched zinc nitrate solution using a biomedical cyclotron, an alternative method to the current production through the 68Ge/68Ga generators. Methods The target material was prepared by dissolving metallic 68Zn (enrichment of 99.26%) in HNO3 6 N and then diluting it with HNO3 0.2 N to obtain a 1.7 M solution of 68Zn(NO3)2 [1] . Using the cross-section data (EXFOR database) and the proton stopping power (SRIM software), the 68Ga saturation yield and its impurities were estimated. Irradiation tests were performed using the GE PETtrace cyclotron, located at our Institution. A liquid target was used, with a 25 μm foil of Havar® and a 250 μm foil of Niobium, employed to obtain proper selection of entrance beam energy, to degrade the energy of the beam to 12 MeV in order to minimize the production of 67Ga through the 68Zn(p,2n)67Ga reaction. The modification of the target also allowed us to improve resistance of the foil to acidity of the target material. Irradiations were carried out for 32 ± 1 min at 47 ± 2 μA. The product’s activity was measured using an activity meter calibrated with a standard of 68Ge traceable to NIST, while radionuclidic purity was evaluated by using gamma-ray spectrometry with an HPGe detector. Results The gamma-spectra of the irradiated samples showed the presence of different isotopes of Ga, Cu and Co, as predicted by the theoretical estimates. Radionuclidic purity is within the limits of EU Pharmacopeia up to 3 h after end of bombardment. Separation and recovery up to a yield of 90 ± 9% was obtained. Conclusions In summary, although optimizations and further advances are necessary, the results obtained are very promising and demonstrate the feasibility to produce sufficient activity of 68Ga in cyclotron as a competitive alternative to the 68Ge/68Ga generators.

Shell Correction in Stopping Power for Protons with Different Ionization Degrees in Al2O3 and ZrO2

The shell correction and stopping power of Aluminum oxide Al2O3 and Zirconium oxide ZrO2 for protons over the 10KeV to 10000KeV energy range is studied using an improved Drude dielectric function. In present work the effects of charge fraction on the stopping power and shell correction has been taken in the consideration and investigated. The main point is to get more information about the energy loss function ELF of compounds Al2O3and ZrO2, which is important in studying the stopping power of protons. The shell correction is one of the corrected term to Bethe formula give more attention to compounds is converging at high energy E ≥ 700keV and increasing at incident proton energy 0 ≤ E(keV) ≤ 700 Good agreement is achieved with previous work.

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.

Stopping power of protons in transition metals of the groups V and VI

We present theoretical results for the stopping power of protons in V, Nb, Cr and Mo with the aim of discuss the present knowledge of energy loss in these transitions metals with 3 and 4 electrons in the outer d-subshell. The theoretical description spans over a wide energy region, which covers from impact velocities below the Fermi velocity, to high but not relativistic ones, covering the sensitive region of the stopping maximum. We use two complementary formalisms, a binary non-perturbative model recently developed by our group, and with very good results for proton and antiproton impact at low impact velocities. The other one is the known dielectric formalism, perturbative, so valid for impact velocities quite above the Fermi velocity, but including the binary and collective excitations of the valence electrons. We obtain a good description of the experimental data in the whole energy range. The importance of plasmon excitation at energies around the maximum is clear. We call the attention on the need of new data in Nb and Mo targets around the stopping maximum, and the lack of stopping values below 30 keV in the four targets.

Theoretical and experimental analysis of photon counting detector CT for proton stopping power prediction.

Photon counting detectors (PCDs) are being introduced in advanced x-ray computed tomography (CT) scanners. From a single PCD-CT acquisition, multiple images can be reconstructed, each based on only a part of the original x-ray spectrum. In this study, we investigated whether PCD-CT can be used to estimate stopping power ratios (SPRs) for proton therapy treatment planning, both by comparing to other SPR methods proposed for single energy CT (SECT) and dual energy CT (DECT) as well as to experimental measurements. A previously developed DECT-based SPR estimation method was adapted to PCD-CT data, by adjusting the estimation equations to allow for more energy spectra. The method was calibrated directly on noisy data to increase the robustness toward image noise. The new PCD SPR estimation method was tested in theoretical calculations as well as in an experimental setup, using both four and two energy bin PCD-CT images, and through comparison to two other SPR methods proposed for SECT and DECT. These two methods were also evaluated on PCD-CT images, full spectrum (one-bin) or two-bin images, respectively. In a theoretical framework, we evaluated the effect of patient-specific tissue variations (density and elemental composition) and image noise on the SPR accuracy; the latter effect was assessed by applying three different noise levels (low, medium, and high noise). SPR estimates derived using real PCD-CT images were compared to experimentally measured SPRs in nine organic tissue samples, including fat, muscle, and bone tissues. For the theoretical calculations, the root-mean-square error (RMSE) of the SPR estimation was 0.1% for the new PCD method using both two and four energy bins, compared to 0.2% and 0.7% for the DECT- and SECT-based method, respectively. The PCD method was found to be very robust toward CT image noise, with a RMSE of 2.7% when high noise was added to the CT numbers. Introducing tissue variations, the RMSE only increased to 0.5%; even when adding high image noise to the changed tissues, the RMSE stayed within 3.1%. In the experimental measurements, the RMSE over the nine tissue samples was 0.8% when using two energy bins, and 1.0% for the four-bin images. In all tested cases, the new PCD method produced similar or better results than the SECT- and DECT-based methods, showing an overall improvement of the SPR accuracy. This study thus demonstrated that PCD-CT scans will be a qualified candidate for SPR estimations.

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

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

I007] Treatment planning in proton therapy

Purpose To present the most important physics aspects of proton therapy (PT) treatment planning for scanning beams and compare them with photon-based radiotherapy. Methods The most important differences of protontherapy treatment planning with pencil beam scanning (PBS) with respect to what is considered now the norm in radiotherapy with photons (XRT) are the following: 1) The dosimetric inaccuracies in the map of proton stopping power (PSP) estimated via CT imaging are larger than similar error in estimating electron density for photons. This is the reason behind the development of more refined methods (e.g. dual energy CT or proton CT) and the current impossibility of MR-based protontherapy; 2) The pristine beam is in principle simpler to model than in photons, as no beam modifiers are used to shape the beam, with one significant exception: the preabsorber (aka “range shifter”); 3) Up until recently, dose calculation was performed with pencil beam-based algorithms. Now a transition is happening towards Monte Carlo-based algorithms, however for reasons that are different than in XRT; 4) In XRT geometrical uncertainties are handled during planning with ad hoc volume such as the planning target volume (PTV). In PT this approach is hardly satisfactory, albeit still common, and most advanced planning solutions are in fact based on PTV-less so called “robust optimization”, where the effect of range and setup uncertainties is essentially part of the cost function. This leaves open the issue of dose reporting, which is now approached in different ways at different PT centers; 5) PT treatment planning is performed assuming a constant 1.1 relative biological effectiveness (RBE) of protons with respect to XRT. This is an approximation, and the need of variable RBE in proton planning is one of the “hot topic” at the moment; 6) Adaptive therapy, and as a consequence adaptive treatment planning, is an area of development that has to be explored further. PT may be a field where the concept of “online adaptive” may have a clinical impact, but the times are still premature for a large scale implementation of these techniques.

OC-0086: Validation of proton stopping power ratio estimation based on dual energy CT using organic tissues

OC-0086: Validation of proton stopping power ratio estimation based on dual energy CT using organic tissues

PV-0138: A multi-modal MRI and CT imaging method for the accurate calculation of proton stopping power ratios

PV-0138: A multi-modal MRI and CT imaging method for the accurate calculation of proton stopping power ratios

EP-1747: Characterizing proton stopping power ratio uncertainties in a CT/MRI tissue classification model

EP-1747: Characterizing proton stopping power ratio uncertainties in a CT/MRI tissue classification model

Investigations of radiation shielding using Monte Carlo method and elastic properties of PbO-SiO2-B2O3-Na2O glasses

Several physical parameters such as the packing density (PD), oxygen molar volume (OMV), oxygen packing density (OPD) and the elastic moduli of the quaternary glass system xPbO-(30-x)SiO2-46.67B2O3-23.33Na2O (x = 0, 5, 10 and 15 mol%) have been evaluated. The elastic moduli were computed according to Makishima-Mackenzie model and Rocherulle model. The values of these moduli have been compared to their experimental values. Moreover, different shielding parameters such as mass attenuation coefficients (MAC), half value layer (HVL), mean free path (MFP), effective atomic numbers (EAN), effective electron densities (EED) and buildup factors have been evaluated using the WinXcom program in the energy range 0.015–15 MeV for the quaternary studied glass system. The MAC values have been compared with MCNPX (version 2.6.0) Monte Carlo code. Besides, mass stopping power (MSP) for proton, alpha and electron as well as the removal cross section for fast neutron (∑R) have been calculated. The results observed that the composition has the highest value of PbO (15 mol %) showed excellent nuclear radiation shielding and elastic properties.

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.

Material elemental decomposition in dual and multi-energy CT via a sparsity-dictionary approach for proton stopping power ratio calculation.

Accurate calculation of proton stopping power ratio (SPR) relative to water is crucial to proton therapy treatment planning, since SPR affects prediction of beam range. Current standard practice derives SPR using a single CT scan. Recent studies showed that dual-energy CT (DECT) offers advantages to accurately determine SPR. One method to further improve accuracy is to incorporate prior knowledge on human tissue composition through a dictionary approach. In addition, it is also suggested that using CT images with multiple (more than two) energy channels, i.e., multi-energy CT (MECT), can further improve accuracy. In this paper, we proposed a sparse dictionary-based method to convert CT numbers of DECT or MECT to elemental composition (EC) and relative electron density (rED) for SPR computation. A dictionary was constructed to include materials generated based on human tissues of known compositions. For a voxel with CT numbers of different energy channels, its EC and rED are determined subject to a constraint that the resulting EC is a linear non-negative combination of only a few tissues in the dictionary. We formulated this as a non-convex optimization problem. A novel algorithm was designed to solve the problem. The proposed method has a unified structure to handle both DECT and MECT with different number of channels. We tested our method in both simulation and experimental studies. Average errors of SPR in experimental studies were 0.70% in DECT, 0.53% in MECT with three energy channels, and 0.45% in MECT with four channels. We also studied the impact of parameter values and established appropriate parameter values for our method. The proposed method can accurately calculate SPR using DECT and MECT. The results suggest that using more energy channels may improve the SPR estimation accuracy.

Experimental Evaluation of CT Number Changes in 320-Row CBCT Volume Scan for Proton Range Calculation

We investigated the longitudinal positional dependence of CT number in 320-row Cone Beam Computed Tomography (CBCT) volume scan (320-row volume scan) using a simple geometric phantom (SGP) and a chest simulation phantom (CSP) in order to evaluate its effect on proton range calculation. The SGP consisted of lung substitute material (LSM) and a cylindrical phantom (CP) made of high-density polyethylene. The CSP was an anthropomorphic phantom similar to the human chest. The two phantoms were scanned using 320-row volume scan in various longitudinal positions from the central beam axis. In experiments using the SGP, an image blur at the boundary of the two materials became gradually evident when the LSM was placed far away from the beam central axis. The image blur of the phantom was consistent with the gradation in CT number. The maximum difference in CT numbers between the 64-row helical scan and 320-row volume scan at the boundary of the two materials was consistent with approximately 50% of the relative proton stopping power. In contrast, the CT number profile in each longitudinal position was fairly consistent and longitudinal positional dependence rarely occurred in the CSP experiments. Pass lengths of CT beams through areas with widely different electron densities were shorter, and thus did not significantly impact CT numbers. Based on findings from the CSP experiments, we considered 320-row volume scan to be feasible for proton range calculation in clinical settings, although the relatively large longitudinal positional dependence of CT number should be accounted for when doing so.

Validation of proton stopping power ratio estimation based on dual energy CT using fresh tissue samples

Dual energy CT (DECT) has been shown, in theoretical and phantom studies, to improve the stopping power ratio (SPR) determination used for proton treatment planning compared to the use of single energy CT (SECT). However, it has not been shown that this also extends to organic tissues. The purpose of this study was therefore to investigate the accuracy of SPR estimation for fresh pork and beef tissue samples used as surrogates of human tissues. The reference SPRs for fourteen tissue samples, which included fat, muscle and femur bone, were measured using proton pencil beams. The tissue samples were subsequently CT scanned using four different scanners with different dual energy acquisition modes, giving in total six DECT-based SPR estimations for each sample. The SPR was estimated using a proprietary algorithm (syngo.via DE Rho/Z Maps, Siemens Healthcare, Forchheim, Germany) for extracting the electron density and the effective atomic number. SECT images were also acquired and SECT-based SPR estimations were performed using a clinical Hounsfield look-up table. The mean and standard deviation of the SPR over large volume-of-interests were calculated. For the six different DECT acquisition methods, the root-mean-square errors (RMSEs) for the SPR estimates over all tissue samples were between 0.9% and 1.5%. For the SECT-based SPR estimation the RMSE was 2.8%. For one DECT acquisition method, a positive bias was seen in the SPR estimates, having a mean error of 1.3%. The largest errors were found in the very dense cortical bone from a beef femur. This study confirms the advantages of DECT-based SPR estimation although good results were also obtained using SECT for most tissues.

Experimental validation of two dual-energy CT methods for proton therapy using heterogeneous tissue samples.

The purpose of this work is to evaluate the performance of dual-energy CT (DECT) for determining proton stopping power ratios (SPRs) in an experimental environment and to demonstrate its potential advantages over conventional single-energy CT (SECT) in clinical conditions. Water equivalent range (WER) measurements of 12 tissue-equivalent plastic materials and 12 fresh animal tissue samples are performed in a 195MeV broad proton beam using the dose extinction method. SECT and DECT scans of the samples are performed with a dual-source CT scanner (Siemens SOMATOM Definition Flash). The methods of Schneider etal. (1996), Bourque etal. (2014), and Lalonde etal. (2017) are used to predict proton SPR on SECT and DECT images. From predicted SPR values, the WER of the proton beam through the sample is predicted for SECT and DECT using Monte Carlo simulations and compared to the measured WER. For homogeneous tissue-equivalent plastic materials, results with DECT are consistent with experimental measurements and show a systematic reduction of SPR uncertainty compared to SECT, with root-mean-square errors of 1.59% versus 0.61% for SECT and DECT, respectively. Measurements with heterogeneous animal samples show a clear reduction of the bias on range predictions in the presence of bones, with -0.88% for SECT versus -0.58% and -0.14% for both DECT methods. An uncertainty budgetallows isolating the effect of CT number conversion to SPR and predicts improvements by DECT over SECT consistently with theoretical predictions, with 0.34% and 0.31% for soft tissues and bones in the experimental setup compared to 0.34% and 1.14% with the theoretical method. The present work uses experimental measurements in a realistic clinical environment to show potential benefits of DECT for proton therapy treatment planning. Our results show clear improvements over SECT in tissue-equivalent plastic materials and animal tissues. Further work towards using Monte Carlo simulations for treatment planning with DECT data and a more detailed investigation of the uncertainties on I-value and limitations on the Bragg additivity rule could potentially further enhance the benefits of this imaging technology for proton therapy.

Abstract ID: 187 Validation of dual energy CT atomic composition extraction using proton Monte Carlo

Proton Monte Carlo (MC) simulations model the physics processes between protons and medium and yield more accurate dose calculation compared to proton stopping power based pencil beam convolution algorithm. MC simulation is also essential for prompt gamma and proton-induced isotope production for in vivo proton range detection. It requires the tissue element composition to be determined from patient CT images. We propose a new hierarchical clustering based approach in extracting the atomic composition in human tissues from Dual Energy CT (DECT) scans and a method for validation using MC simulations. 70 published human tissues samples were used for the theoretical study of this approach. The tissues were first clustered by their composition similarities using hierarchical clustering. For each tissue cluster, three base tissues were randomly selected. Other tissues in this cluster were the weighted sum of these base tissues, where the weights were derived by fitting the attenuation coefficients calculated from DECT X-ray spectrums. The derived tissue atomic compositions and effective atomic number were compared with the ground truth. Animal tissues were scanned using a DECT scanner. The tissues were then irradiated using a PBS proton beam. A double Gaussian proton source has been modeled in GEANT4. Using these images, source model and extraction method, Monte Carlo simulations will be run mimicking the experimental setup. The soft tissues and bones were clustered into 8 subcategories, with cophenetic correlation 0.895 and inconsistency coefficient (IC) > 1.15 ( IC max = 1.1547 ). The mean and standard deviations of the difference in the derived atomic compositions and the ground truth for H, C, N, O, Ca, P were 0.05% (±0.07%), 1.56% (±2.02%), 0.61% (±0.55%), 1.81% (±2.37%), 0.03% (±0.07%), 0.13% (±0.16%), respectively. The Z eff from derived elemental composition was within 0.04 from the true values. We have investigated a new approach to extract the atomic composition from DECT scans. The accuracy of the extraction was demonstrated for published tissue samples. This approach will be validated using the DECT scans of animal tissues and GEANT4 source model, by comparing the Zeff derived from this method against measurements from proton beam irradiations.