MO-FG-CAMPUS-JeP1-04: Evaluating DECT Vs SECT Range Differences in Proton Therapy Using Clinical Data

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.

PurposeTo investigate and quantify the potential benefits associated with the use of stopping-power-ratio (SPR) images created from dual-energy computed tomography (DECT) images for proton dose calculation in a clinical proton treatment planning system (TPS).Materials and MethodsThe DECT and single-energy computed tomography (SECT) scans obtained for 26 plastic tissue surrogate plugs were placed individually in a tissue-equivalent plastic phantom. Relative-electron density (ρe) and effective atomic number (Zeff) images were reconstructed from the DECT scans and used to create an SPR image set for each plug. Next, the SPR for each plug was measured in a clinical proton beam for comparison of the calculated values in the SPR images. The SPR images and SECTs were then imported into a clinical TPS, and treatment plans were developed consisting of a single field delivering a 10 × 10 × 10-cm3 spread-out Bragg peak to a clinical target volume that contained the plugs. To verify the accuracy of the TPS dose calculated from the SPR images and SECTs, treatment plans were delivered to the phantom containing each plug, and comparisons of point-dose measurements and 2-dimensional γ-analysis were performed.ResultsFor all 26 plugs considered in this study, SPR values for each plug from the SPR images were within 2% agreement with measurements. Additionally, treatment plans developed with the SPR images agreed with the measured point dose to within 2%, whereas a 3% agreement was observed for SECT-based plans. γ-Index pass rates were > 90% for all SECT plans and > 97% for all SPR image–based plans.ConclusionTreatment plans created in a TPS with SPR images obtained from DECT scans are accurate to within guidelines set for validation of clinical treatment plans at our center. The calculated doses from the SPR image–based treatment plans showed better agreement to measured doses than identical plans created with standard SECT scans.

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.

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.

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.

Dual energy CT (DECT) has been shown to estimate stopping power ratio (SPR) map with a higher accuracy than conventional single energy CT (SECT) by obtaining the energy dependence of photon interactions. This work presents a learning-based method to synthesize DECT images from SECT image for proton radiotherapy. The proposed method uses a residual attention generative adversarial network. Residual blocks with attention gates were used to force the model to focus on the difference between DECT images and SECT images. To evaluate the accuracy of the method, we retrospectively investigated 70 head-and-neck cancer patients whose DECT and SECT scans were acquired simultaneously. The model was trained to generate both a high and low energy DECT image based on a SECT image. The generated synthetic low and high DECT images were evaluated against the true DECT images using leave-one-out cross-validation. To evaluate our method in the context of a practical application, we generated SPR maps from synthetic DECT (sDECT) using a dual-energy based stoichiometric method and compared the SPR maps to those generated from DECT. A dosimetric comparison for dose obtained from DECT was performed against that derived from sDECT. The mean of mean absolute error, peak signal-to-noise ratio and normalized cross-correlation for the synthetic high and low energy CT images was 36.9 HU, 29.3 dB, 0.96 and 35.8 HU, 29.2 dB, and 0.96, respectively. The corresponding SPR maps generated from synthetic DECT showed an average normalized mean square deviation of about 1% with reduced noise level and artifacts than those from original DECT. Dose-volume histogram (DVH) metrics for the clinical target volume agree within 1% between the DECT and sDECT calculated dose. Our method synthesized accurate DECT images and showed a potential feasibility for proton SPR map generation. This study investigated a learning-based method to synthesize DECT images from SECT image for proton radiotherapy.

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 purpose of this study was to determine the current practice patterns of utilization of dual-energy computed tomography (DECT) in thoracic imaging. In this IRB-approved study, the URL link for an anonymous 26-question survey was sent by email to all the members of the Society of Thoracic Radiology (STR). Survey questions focused on the practice type, case volume, DECT scanner availability, common indications, image types, and perceived utility of DECT. Study data were collected and managed using SurveyMonkey tools and analyzed with χ tests. The survey response rate was 11% (104/962). DECT was available in 75% of respondents' institutions, with 90% of these having 1 to 5 DECT scanners (P<0.001). Seventy percent performed 1 to 500 DECT chest CTs per month (P<0.001). Dual-source was the most common DECT scanner (81%) (P=0.239). DECT was opted depending on the clinical indication in 89% (P=0.433). The technologist reconstructs the additional DECT images in 75% of instances (P<0.001). Acute pulmonary embolism (PE) was the most common indication of DECT (53%) (P=0.006), while chronic PE was considered the most valuable use of DECT (33%) (P<0.001). Iodine map was the most commonly used DECT image (53%) (P<0.001) followed by low-energy virtual monoenergetic image (VMI) (29%). For VMI, 50 keV was the most commonly used energy level for improving vascular contrast (43%) (P=0.048), whereas 120 keV was the most commonly used energy in VMI for decreasing artifacts (25%) (P=0.027). There is wide variability on the utilization of DECT in thoracic imaging. PE is perceived to be the most valuable utility of DECT, and iodine map is considered the most valuable DECT image.

Dual-energy CT represents a great leap in CT technology, with a potential for improvement of the diagnostic performance of CT in ocncologic imaging. A dual-energy CT scan entails splitting the radiation dose between two simultaneous CT acquisitions with different energies. Multiple additional series of images may be generated from a dual-energy CT scan providing new and important imaging data. Applications of dual-energy CT in head and neck tumor imaging are based on either image blending or material characterization. Image blending applications are used to improve image quality, increase lesion detectability, and decrease dental artifacts. Applications based on material characterization (iodine mapping) enable the quantitative calculation of the degree of lesion enhancement and may help in lesion characterization.

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.

Metal artefacts pose a common problem in single energy computed tomography (SECT) images used for radiotherapy. Virtual monoenergetic (VME) images constructed with dual energy computed tomography (DECT) scans can be used to reduce beam hardening artefacts. Dual energy metal artefact reduction is compared and combined with iterative metal artefact reduction (iMAR) to determine optimal imaging strategies for patients with metal prostheses. SECT and DECT scans were performed on a Siemens Somatom AS-64 Slice CT scanner. Images were acquired of a modified CIRS pelvis phantom with 6, 12, 20mm diameter stainless steel rods and VME images reconstructed at 100, 120, 140 and 190keV. These were post-reconstructed with and without the iMAR algorithm. Artefact reduction was measured using: (1) the change in Hounsfield Unit (HU) with and without metal artefact reduction (MAR) for 4 regions of interest; (2) the total number of artefact pixels, defined as pixels with a difference (between images with metal rod and without) exceeding a threshold; (3) the difference in the mean pixel intensity of the artefact pixels. DECT, SECT + iMAR and DECT + iMAR were compared. Both SECT + iMAR and DECT + iMAR offer successful MAR for phantom simulating unilateral hip prosthesis. DECT gives minimal artefact reduction over iMAR alone. Quantitative metrics are advantageous for MAR analysis but have limitations that leave room for metric development.

Photon-counting CT (PCCT) is the latest technology enabling imaging with reduced noise and inherent spectral separation, with the potential to directly calculate a more accurate tissue stopping power from spectral data. This potential benefit is difficult to quantify in practice and is currently evaluated mainly in phantoms with simplified geometries that only approximate real patient anatomy. In this work, we proposed virtual imaging simulators as an alternative approach to experimental validation of beam range uncertainty in complex patient geometry using a computational model of a human head and a CT system. In addition, we validate the accuracy of stopping power ratio (SPR) calculations on a model of a PCCT scanner using a conventional stoichiometric calibration approach and a prototype software TissueXplorer. A validated CT simulator (DukeSim) was used to generate PCCT projections of a computational head phantom, which were reconstructed with an open-source toolbox (ASTRA). The dose of 2 Gy was delivered through protons in a single fraction to target two different cases of nasal and brain tumors. The ground-truth treatment plan was made directly on the computational phantom using clinical treatment planning software (RayStation). This plan was then recalculated on the corresponding CT images for which SPR values were estimated using both the conventional method and the prototype software TissueXplorer. The resulting dose distributions were subsequently compared against the ground-truth plan to quantify dose differences arising from SPR estimation. The mean percentage difference in estimating the SPR with TissueXplorer in all head tissues inside the scanned volume was 0.28%. SPRs obtained with this method showed smaller dose distribution differences from the ground truth plan than the conventional stoichiometric calibration method on the computational head phantom. Virtual imaging offers an alternative approach to validation of the SPR prediction from CT imaging, as well as its effect on the dose distribution and thus downstream clinical outcomes. According to this simulation study, software solutions that utilize spectral information hold promise for more accurate prediction of the SPR than the conventional stoichiometricapproach.

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.

While dual-energy computed tomography (DECT) images provide clinically useful information than single-energy CT (SECT), SECT remains the most widely used CT system globally, and only a few institutions can use DECT. This study aimed to establish an artificial intelligence (AI)-based image-domain material decomposition technique using multiple keV-output learning of virtual monochromatic images (VMIs) to create DECT-equivalent images from SECT images. This study involved 82 patients with head and neck cancer. Of these, the AI model was built with data from the 67 patients with only DECT scans, while 15 patients with both SECT and DECT scans were used for SECT testing. Our AI model generated VMI50keV and VMI100keV from VMI70keV equivalent to 120-kVp SECT images. We introduced a loss function for material density images (MDIs) in addition to the loss for VMIs. For comparison, we trained the same model with the loss for VMIs only. DECT-equivalent images were generated from SECT images and compared with the true DECT images. The prediction time was 5.4s per patient. The proposed method with the MDI loss function quantitatively provided more accurate DECT-equivalent images than the model trained with the loss for VMIs only. Using real 120-kVp SECT images, the trained model produced precise DECT images of excellent quality. In this study, we developed an AI-based material decomposition approach for head and neck cancer patients by introducing the loss function for MDIs via multiple keV-output learning. Our results suggest the feasibility of AI-based image-domain material decomposition in a conventional SECT system without a DECT scanner.

To compare the radiation exposure from single-energy CT (SECT) against rapid kV-switching dual-energy CT (DECT) imaging in both adults and children when resulting image data offer equivalent lesion identification power. Lesions in an adult and a 10-year-old-child body phantom were imitated using iodine solutions of different concentrations. Phantoms were subjected to several SECT and DECT thoracic and abdominal scans using a rapid kV-switching DECT scanner. The contrast-to-noise ratio (CNR) of each lesion was measured on resulting SECT images and virtual monoenergetic images (VMI) available from DECT. The SECT scans that resulted in CNR values similar to the maximum CNR observed in VMIs derived from corresponding DECT scans were identified. SECT and DECT scans with equivalent lesion-discriminating power were compared regarding the associated radiation dose burden. Doses to the lung, breast, and esophagus from thoracic imaging and doses to the liver, kidneys, and stomach from abdominal imaging were determined through Monte Carlo simulations of SECT and DECT exposures. Compared to SECT imaging of the adult body phantom, organ doses from DECT were found to be 5-11% lower in thoracic imaging and 44-45% lower in abdominal imaging. Compared to SECT imaging of the 10-year-old body phantom, organ doses from DECT were found to be 2.8-3.4 times higher in thoracic imaging and 1.5-1.6 times higher in abdominal imaging. The use of rapid kV-switching DECT instead of SECT imaging may be associated with a similar or lower dose burden in adults but a noticeably higher dose burden in children. Question How does the radiation exposure from single-energy and dual-energy CT imaging compare when both techniques provide equivalent lesion identification power? Findings Rapid kV-switching dual-energy CT compared to single-energy CT may result in a similar or lower radiation dose in adults, but higher radiation dose in children. Clinical relevance Rapid kV-switching dual-energy CT imaging in children should be preferred over single-energy CT imaging only in cases where the additional information provided is crucial for an effective diagnosis.