Dual-energy CT Methods Research Articles

Material decomposition with a prototype photon-counting detector CT system: expanding a stoichiometric dual-energy CT method via energy bin optimization and K-edge imaging

Objective. Computed tomography (CT) has advanced since its inception, with breakthroughs such as dual-energy CT (DECT), which extracts additional information by acquiring two sets of data at different energies. As high-flux photon-counting detectors (PCDs) become available, PCD-CT is also becoming a reality. PCD-CT can acquire multi-energy data sets in a single scan by spectrally binning the incident x-ray beam. With this, K-edge imaging becomes possible, allowing high atomic number (high-Z) contrast materials to be distinguished and quantified. In this study, we demonstrated that DECT methods can be converted to PCD-CT systems by extending the method of Bourque et al (2014). We optimized the energy bins of the PCD for this purpose and expanded the capabilities by employing K-edge subtraction imaging to separate a high-atomic number contrast material. Approach. The method decomposes materials into their effective atomic number (Z eff) and electron density relative to water (ρ e ). The model was calibrated and evaluated using tissue-equivalent materials from the RMI Gammex electron density phantom with known ρ e values and elemental compositions. Theoretical Z eff values were found for the appropriate energy ranges using the elemental composition of the materials. Z eff varied slightly with energy but was considered a systematic error. An ex vivo bovine tissue sample was decomposed to evaluate the model further and was injected with gold chloride to demonstrate the separation of a K-edge contrast agent. Main results. The mean root mean squared percent errors on the extracted Z eff and ρ e for PCD-CT were 0.76% and 0.72%, respectively and 1.77% and 1.98% for DECT. The tissue types in the ex vivo bovine tissue sample were also correctly identified after decomposition. Additionally, gold chloride was separated from the ex vivo tissue sample with K-edge imaging. Significance. PCD-CT offers the ability to employ DECT material decomposition methods, along with providing additional capabilities such as K-edge imaging.

Simulation Study: Data-Driven Material Decomposition in Industrial X-ray Computed Tomography

Material-resolving computed tomography is a powerful and well-proven tool for various clinical applications. For industrial scan setups and materials, several problems, such as K-edge absence and beam hardening, prevent the direct transfer of these methods. This work applies dual-energy computed tomography methods for material decomposition to simulated phantoms composed of industry-relevant materials such as magnesium, aluminium and iron, as well as some commonly used alloys like Al–Si and Ti64. Challenges and limitations for multi-material decomposition are discussed in the context of X-ray absorption physics, which provides spectral information that can be ambiguous. A deep learning model, derived from a clinical use case and based on the popular U-Net, was utilised in this study. For various reasons outlined below, the training dataset was simulated, whereby phantom shapes and material properties were sampled arbitrarily. The detector signal is computed by a forward projector followed by Beer–Lambert law integration. Our trained model could predict two-material systems with different elements, achieving a relative error of approximately 1% through simulated data. For the discrimination of the element titanium and its alloy Ti64, which were also simulated, the relative error increased to 5% due to their similar X-ray absorption coefficients. To access authentic CT data, the model underwent testing using a 10c euro coin composed of an alloy known as Nordic gold. The model detected copper as the main constituent correctly, but the relative fraction, which should be 89%, was predicted to be ≈70%.

Dual-energy CT-based stopping power prediction for dental materials in particle therapy.

Radiotherapy with protons or light ions can offer accurate and precise treatment delivery. Accurate knowledge of the stopping power ratio (SPR) distribution of the tissues in the patient is crucial for improving dose prediction in patients during planning. However, materials of uncertain stoichiometric composition such as dental implant and restoration materials can substantially impair particle therapy treatment planning due to related SPR prediction uncertainties. This study investigated the impact of using dual-energy computed tomography (DECT) imaging for characterizing and compensating for commonly used dental implant and restoration materials during particle therapy treatment planning. Radiological material parameters of ten common dental materials were determined using two different DECT techniques: sequential acquisition CT (SACT) and dual-layer spectral CT (DLCT). DECT-based direct SPR predictions of dental materials via spectral image data were compared to conventional single-energy CT (SECT)-based SPR predictions obtained via indirect CT-number-to-SPR conversion. DECT techniques were found overall to reduce uncertainty in SPR predictions in dental implant and restoration materials compared to SECT, although DECT methods showed limitations for materials containing elements of a high atomic number. To assess the influence on treatment planning, an anthropomorphic head phantom with a removable tooth containing lithium disilicate as a dental material was used. The results indicated that both DECT techniques predicted similar ranges for beams unobstructed by dental material in the head phantom. When ion beams passed through the lithium disilicate restoration, DLCT-based SPR predictions using a projection-based method showed better agreement with measured reference SPR values (range deviation: 0.2mm) compared to SECT-based predictions. DECT-based SPR prediction may improve the management of certain non-tissue dental implant and restoration materials and subsequently increase dose prediction accuracy.

Mineral quantitative characterization method based on basis material decomposition model by dual-energy computed tomography.

Dual-energy computed tomography (DECT) can reconstruct electron density ρe and effective atomic number Zeff distribution for material discrimination. Image-domain basis material decomposition (IBMD) method is a widely used DECT method. However, IBMD method cannot be used for mineral identification directly due to limitations of complex basis material determination, beam hardening artifacts, and inherent errors caused by approximate empirical formulas. This study proposes an improved IBMD (IIBMD) method to overcome the above limitations. In IIBMD method, the composition of basis material is optimized to obtain accurate decomposition coefficients, which enables accurate ρe and Zeff distribution. Moreover, the thickness of basis material is optimized to reduce the effect of beam hardening. Furthermore, two formulas in place of empirical formulas are proposed to calculate ρe and Zeff. Finally, a threshold technique is applied to separate different mineral phases. Numerical simulations and practical experiments using a photon-counting detector CT system are implemented to verify IIBMD method. Results show that the relative errors of ρe and Zeff for seven common minerals are down to 5%, lower than most of the existing DECT methods for rocks. Reasonable volume fraction results of mineral phases are thus obtained through threshold segmentation. This study demonstrates that the proposed IIBMD method has high practical value in mineralogical identification.

A Novel Dual-Energy CT Method for Detection and Differentiation of Intracerebral Hemorrhage From Contrast Extravasation in Stroke Patients After Endovascular Thrombectomy

PurposeDual-energy computed tomography (DECT) has been shown to be able to differentiate between intracranial hemorrhage (ICH) and extravasation of iodinated contrast media (contrast staining [CS]). TwinSpiral DECT is a recently introduced technique, which allows image acquisition at two different energy levels in two consecutive spiral scans. The aim of this study was to evaluate the feasibility and accuracy of TwinSpiral DECT to distinguish between ICH and CS after endovascular thrombectomy (EVT) in patients with acute ischemic stroke.MethodsThis retrospective single-center study conducted between November 2019 and July 2020 included non-contrast TwinSpiral DECT scans (tube voltages 80 and 150Sn kVp) of 39 ischemic stroke patients (18 females, 21 males, mean age 69 ± 11 years) within 48–72 h after endovascular thrombectomy. Parenchymal hyperdensity was assessed for the presence of ICH or/and CS by two board certified and fellowship-trained, blinded and independent neuroradiologists using standard mixed images and virtual non-contrast (VNC) images with corresponding iodine maps from TwinSpiral DECT. Follow-up examinations (FU; CT or MRI) were used as a standard of reference. Sensitivity, specificity, and accuracy for the detection of ICH as well as the inter-reader agreement were calculated.ResultsParenchymal hyperdensities were detected in 17/39 (44%) patients. Using DECT, they were classified by both readers as ICH in 9 (53%), CS in 8 (47%), and mixture of both in 6 (35%) cases with excellent agreement (κ = 0.81, P < 0.0001). The sensitivity, specificity, and accuracy for the detection of ICH in DECT was 90% (95% confidence interval [CI]: 84–96%), 100% (95% CI 94–100%) and 95% (95% CI 89–100%), and in mixed images 90% (95% CI 84–96%), 86% (95% CI 80–92%) and 88% (95% CI 82–94%), respectively. Inter-reader agreement for detecting ICH on DECT compared to the mixed images was κ = 1.00 (P < 0.0001) vs. κ = 0.51 (P = 0.034).ConclusionTwinSpiral DECT demonstrates high accuracy and excellent specificity for differentiating ICH from CS in patients after mechanical thrombectomy due to acute ischemic stroke, and improves inter-reader agreement for detecting ICH compared to the standard mixed images.

Diagnostic accuracy of a dual-energy computed tomography-based post-processing method for imaging bone marrow edema following an acute ligamentous knee injury

This study evaluated the ability of a custom dual-energy CT (DECT) post-processing material decomposition method to image bone marrow edema after acute knee injury. Using an independent validation cohort, the DECT method was compared to gold-standard, fluid-sensitive MRI. By including both quantitative voxel-by-voxel validation outcomes and semi-quantitative radiologist scoring-based assessment of diagnostic accuracy, we aimed to provide insight into the relationship between quantitative metrics and the clinical utility of imaging methods. Images from 35 participants with acute anterior cruciate ligament injuries were analyzed. DECT material composition was applied to identify bone marrow edema, and the DECT result was quantitatively compared to gold-standard, registered fluid-sensitive MRI on a per-voxel basis. In addition, two blinded readers rated edema presence in both DECT and fluid-sensitive MR images for evaluation of diagnostic accuracy. Semi-quantitative assessment indicated sensitivity of 0.67 and 0.74 for the two readers, respectively, at the tibia and 0.55 and 0.57 at the femur, and specificity of 0.87 and 0.89 for the two readers at the tibia and 0.58 and 0.89 at the femur. Quantitative assessment of edema segmentation accuracy demonstrated mean dice coefficients of 0.40 and 0.16 at the tibia and femur, respectively. The custom post-processing-based DECT method showed similar diagnostic accuracy to a previous study that assessed edema associated with ligamentous knee injury using a CT manufacturer-provided, built-in edema imaging application. Quantitative outcome measures were more stringent than semi-quantitative scoring methods, accounting for the low mean dice coefficient scores.

Quantification of the volume fraction of fat, water and bone mineral in spongiosa for red marrow dosimetry in molecular radiotherapy by using a dual-energy (SPECT/)CT

A patient-specific absorbed dose calculation for red marrow dosimetry requires quantifying patient-specific volume fractions of the red marrow, yellow marrow, and trabecular bone in the spongiosa of several skeletal sites. This quantification allows selecting appropriate S values calculated from the parameterized radiation transport models for bone and bone marrow dosimetry. Currently, no comprehensive, individualized, and non-invasive procedure is available for quantifying the volume fractions of red marrow, yellow marrow, and trabecular bone in the spongiosa. This study aims to provide a new quantitative method based on dual-energy computed tomography to fill this gap in red marrow dosimetry using a (SPECT/)CT system. MethodsFirst, a method for parametrizing the photon attenuation coefficients relative to water was implemented. Next, a method to calculate the effective atomic number (Zeff) and effective mass density (ρeff) using dual-energy CT (DECT) was employed. Lastly, two- and three-material decomposition using a dual-energy quantitative CT method (DEQCT) was performed in an anthropomorphic spine phantom and two bone samples of a boar, respectively.The measurements of Zeff and ρeff were compared with the syngo.CT DE Rho/Z tool (Siemens Healthineers). Furthermore, the DEQCT method implemented in this study (DEQCT-I) was compared with a second DEQCT method based on the use of external material standards (DEQCT-II). DEQCT-II was used as reference method for calculating relative errors. ResultsThe two-material decomposition in the anthropomorphic spine phantom presented a maximum relative error of −10% for the bone mineral density quantification. Furthermore, Zeff and ρeff calculated by DEQCT-I differed from syngo.CT DE Rho/Z tool by less than 4.4% and 1.9%, respectively. The three-material decomposition in the two bone samples showed a maximum relative error of 21%, −17%, and 15% for the quantification of the volume fractions of fat, water, and bone mineral equivalent materials. Lastly, Zeff and ρeff calculated by DEQCT-I differed from syngo.CT DE Rho/Z tool by less than 8.2% and 7.0%, respectively. ConclusionThis study shows that quantifying the volume fraction of fat, water, and bone mineral using a phantom-independent and post-reconstruction DEQCT method is feasible. DEQCT-I has the advantage of not requiring prior information about the X-ray spectra or the detector sensitivity function, as is the case with spectral-based DEQCT methods. Instead, DEQCT-I, similar to other DEQCT methods depends on the chemical description of reference materials and a beam hardening correction function.DEQCT-I method provides an individualized and non-invasive procedure using a (SPECT/)CT system to apply S values based on the patient-specific volume fractions of yellow marrow, red marrow, and bone mineral in red marrow dosimetry.

Assessment of the impact of CT calibration procedures for proton therapy planning on pediatric treatments.

Relative stopping powers (RSPs) for proton therapy are estimated using single-energy computed tomography (SECT), calibrated with standardized tissues of the adult male. It is assumed that those tissues are representative of tissues of all age and sex. Female, male, and pediatric tissues differ from one another in density and composition. In this study, we use tabulated pediatric tissues and computational phantoms to investigate the impact of this assumption on pediatric proton therapy. The potential of dual-energy CT (DECT) to improve the accuracy of these calculations is explored. We study 51 human body tissues, categorized into male/female for the age groups newborn, 1-, 5-, 10-, and 15-year-old children, and adult, with given compositions and densities. CT numbers are simulated and RSPs are estimated using SECT and DECT methods. Estimated tissue RSPs from each method are compared to theoretical RSPs. The dose and range errors of each approach are evaluated on three computational phantoms (Ewing's sarcoma, salivary sarcoma, and glioma) derived from pediatric proton therapy patients. With SECT, soft tissues have mean estimation errors and standard deviation up to (1.96±4.18)% observed in newborns, compared to (0.20±1.15)% in adult males. Mean estimation errors for bones are up to (-3.35±4.76)% in pediatrics as opposed to (0.10±0.66)% in adult males. With DECT, mean errors reduce to (0.17±0.13)% and (0.23±0.22)% in newborns (soft tissues/bones). With SECT, dose errors in a Ewing's sarcoma phantom are exceeding 5Gy (10% of prescribed dose) at the distal end of the treatment field, with volumes of dose errors >5Gy of mm3 . Similar observations are made in the head and neck phantoms, with overdoses to healthy tissue exceeding 2Gy (4%). A systematic Bragg peak shift resulting in either over- or underdosage of healthy tissues and target volumes depending on the crossed tissues RSP prediction errors is observed. Water equivalent range errors of single beams are between -1.53 and 5.50mm (min, max) (Ewing's sarcoma phantom), -0.78 and 3.62mm (salivary sarcoma phantom), and -0.43 and 1.41mm (glioma phantom). DECT can reduce dose errors to <1Gy and range errors to <1mm. Single-energy computed tomography estimates RSPs for pediatric tissues with systematic shifts. DECT improves the accuracy of RSPs and dose distributions in pediatric tissues compared to the SECT calibration curve based on adult male tissues.

Investigating new CT contrast agents: a phantom study exploring quantification and differentiation methods for high-Z elements using dual-energy CT

To develop a dual-energy CT method for differentiating and quantifying high-Z contrast elements and to evaluate the limitations based on element concentration and atomic number by using an anthropomorphic phantom study. Mass spectrometry standards for iodine, barium, gadolinium, ytterbium, tantalum, gold, and bismuth were diluted from 10.0 to 0.3 mg/mL, placed inside 7-mL vials, and scanned with dual-energy CT using an abdominal phantom and cylindrical water-filled insert. This procedure was repeated with all seven high-Z elements at six isoattenuating values from 250 to 8 HU. Quantification accuracy was measured using a linear regression model and residual error analysis with 90% limits of agreement. The limit of detection for each element was evaluated using the limit of blank of water. Pairwise differentiation of isoattenuating vials was evaluated using AUC values and the difference in fit angles between the two elements. Each high-Z element had a unique concentration vector in a two-dimensional plot of Compton scattering versus photoelectric effect attenuations. Mean quantification values were within ± 0.1 mg/mL of the true values for each element with no proportional bias. Limits of detection ranged from 0.35 to 0.56 mg/mL. Pairwise differentiations were proportional to the isoattenuating HU and the angle between the linear fits with mean AUC values increasing from 0.61 to 0.98 at 8 to 250 HU, respectively. Dual-energy CT can differentiate and quantify isoattenuating high-Z elements. The high-attenuation characteristics and unique concentration vectors of ytterbium, tantalum, gold, and bismuth are well suited for new dual-energy CT contrast agents especially when simultaneously imaged with iodine, barium, or gadolinium. • Dual-energy CT can accurately quantify high-Z contrast elements and readily differentiate iodine, barium, and gadolinium from ytterbium, tantalum, gold, and bismuth. • The differentiation and quantification capabilities for high-Z contrast elements are largely unaffected by phantom size and transaxial location within the phantom. • Potential benefits of new CT contrast agents based on these high-Z elements include alternatives for patients with iodine sensitivity, high conspicuity at both 120 and 140 kVp, simultaneous imaging of two contrast agents, and reduced injection volume.

A quantitative assessment of dual energy computed tomography-based material decomposition for imaging bone marrow edema associated with acute knee injury.

This study developed methods to quantify and improve the accuracy of dual-energy CT (DECT)-based bone marrow edema imaging using a clinical CT system. Objectives were: (a) to quantitatively compare DECT with gold-standard, fluid-sensitive MRI for imaging of edema-like marrow signal intensity (EMSI) and (b) to identify image analysis parameters that improve delineation of EMSI associated with acute knee injury on DECT images. DECT images from ten participants with acute knee injury were decomposed into estimated fractions of bone, healthy marrow, and edema based on energy-dependent differences in tissue attenuation. Fluid-sensitive MR images were registered to DECT for quantitative, voxel-by-voxel comparison between the two modalities. An optimization scheme was developed to find attenuation coefficients for healthy marrow and edema that improved EMSI delineation, compared to MRI. DECT method accuracy was evaluated by measuring dice coefficients, mutual information, and normalized cross correlation between the DECT result and registered MRI. When applying the optimized three-material decomposition method, dice coefficients for EMSI identified through DECT vs MRI were 0.32 at the tibia and 0.13 at the femur. Optimization of attenuation coefficients improved dice coefficient, mutual information, and cross-correlation between DECT and gold-standard MRI by 48%-107% compared to three-material decomposition using non-optimized parameters, and improved mutual information and cross-correlation by 39%-58% compared to the manufacturer-provided two-material decomposition. This study quantitatively evaluated the performance of DECT in imaging knee injury-associated EMSI and identified a method to optimize DECT-based visualization of complex tissues (marrow and edema) whose attenuation parameters cannot be easily characterized. Further studies are needed to improve DECT-based EMSI imaging at the femur.

Neural MLAA for PET-enabled Dual-Energy CT Imaging.

The PET-enabled dual-energy CT method allows dual-energy CT imaging on PET/CT scanners without the need for a second x-ray CT scan. A 511 keV γ-ray attenuation image can be reconstructed from time-of-flight PET emission data using the maximum-likelihood attenuation and activity (MLAA) algorithm. However, the attenuation image reconstructed by standard MLAA is commonly noisy. To suppress noise, we propose a neural-network approach for MLAA reconstruction. The PET attenuation image is described as a function of kernelized neural networks with the flexibility of incorporating the available x-ray CT image as anatomical prior. By applying optimization transfer, the complex optimization problem of the proposed neural MLAA reconstruction is solved by a modularized iterative algorithm with each iteration decomposed into three steps: PET activity image update, attenuation image update, and neural-network learning using a weighed mean squared-error loss. The optimization algorithm is guaranteed to monotonically increase the data likelihood. The results from computer simulations demonstrated the neural MLAA algorithm can achieve a significant improvement on the γ-ray CT image quality as compared to other algorithms.

Feasibility of using post-contrast dual-energy CT for pediatric radiation treatment planning and dose calculation.

When iodinated contrast is administered during CT simulation, standard practice requires a separate non-contrast CT for dose calculation. The objective of this study is to validate our hypothesis that since iodine affects Hounsfield units (HUs) more than electron density (ED), the information from post-contrast dual-layer CT (DLCT) would be sufficient for accurate dose calculation for both photon and proton therapy. 10 pediatric patients with abdominal tumors underwent DLCT scans before and after iodinated contrast administration for radiotherapy planning. Dose distributions with these DLCT-based methods were compared to those with conventional calibration-curve methods that map HU images to ED and stopping-power ratio (SPR) images. For photon plans, conventional and DLCT approaches based on post-contrast scans underestimated the PTV D99 by 0.87 ± 0.70% (p = 0.18) and 0.36 ± 0.31% (p = 0.34), respectively, comparing to their non-contrast optimization plans. Renal iodine concentration was weakly associated with D99 deviation for both conventional (R2 = 0.10) and DLCT (R2 = 0.02) approaches. For proton plans, the clinical target volume D99 errors were 3.67 ± 2.43% (p = 0.0001) and 0.30 ± 0.25% (p = 0.40) for conventional and DLCT approaches, respectively. The proton beam range changed noticeably with the conventional approach. Renal iodine concentration was highly associated with D99 deviation for the conventional approach (R2 = 0.83) but not for DLCT (R2 = 0.007). Conventional CT with iodine contrast resulted in a large dosimetric error for proton therapy, compared to true non-contrast plans, but the error was less for photon therapy. These errors can be greatly reduced in the case of the proton plans if DLCT is used, raising the possibility of using only a single post-contrast CT for radiotherapy dose calculation, thus reducing the time and imaging dose required. This study is the first to compare directly the differences in the calculated dose distributions between pre- and post-contrast CT images generated by single-energy CT and dual-energy CT methods for photon and proton therapy.

A Technique to Identify Isoattenuating Gallstones with Dual-Layer Spectral CT: An ex Vivo Phantom Study.

Background Previously reported dual-energy CT methods for detecting noncalcified gallstones have reduced accuracy for gallstones smaller than 9 mm. Purpose To develop a dual-energy CT method for differentiating isoattenuating gallstones from bile and compare it with previously reported dual-energy CT methods by using a prospective ex vivo phantom reader study. Materials and Methods From May 2017 to May 2018, gallstones were collected from 105 patients (34 men; mean age, 51 years; age range, 18-84 years) undergoing cholecystectomy and placed inside 120-mL vials containing ox bile. The vials were placed inside a water-filled phantom and were scanned with dual-layer dual-energy CT. Thirty isoattenuating gallstones (4.3-24.7 mm in diameter) were evaluated. Conventional CT images, virtual noncontrast images, and monoenergetic images at 200 and 40 keV were created. Segmented images were created by using a two-dimensional histogram of Compton and photoelectric attenuation. Six readers evaluated the presence of isoattenuating gallstones in each image. Intra- and interreader agreement was measured by using percentage agreement, diagnostic performance was evaluated by using mean area under the receiver operating characteristic curve (AUC) estimates and pairwise comparisons, and the agreement of gallstone sizes measured at pathologic examination with those measured on segmented images was compared by using Bland-Altman analysis. Results For all gallstones, segmented images provided the highest mean intrareader (88.1%) and interreader (88.2% and 93.6%) agreements for all readers and reading sessions and the highest overall AUC (0.99; 95% confidence interval [CI]: 0.97, 1.00; adjusted P < .02 for all). For gallstones larger than 9 mm, no significant difference was found between the segmented and monoenergetic AUCs (all P > .94, adjusted P > .05 for all). For gallstones measuring 9 mm or smaller, the segmented images had the highest overall AUC (0.99; 95% CI: 0.97, 1.00; adjusted P < .01 for all). The mean difference in stone sizes was -0.6 mm, with limits of agreement from 2.6 to -3.8 mm. Conclusion Segmented images from Compton and photoelectric attenuation coefficients improve detection of isoattenuating gallstones compared with previously reported dual-energy CT methods. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Matos in this issue.

Influence of intravenous contrast agent on dose calculation in proton therapy using dual energy CT

The purpose of this study is to evaluate the effect of an intravenous (IV) contrast agent on proton therapy dose calculation using dual-energy computed tomography (DECT). Two DECT methods are considered. The first one, , attempts to accurately predict the proton stopping powers relative to water (SPR) of contrast enhanced (CE) DECT images, while the second generates a virtual non-contrast (VNC) volume that can be processed as a native non-contrast (NC) one. Both methods are compared against single-energy computed tomography (SECT). The accuracy of SPR predicted for different concentrations of IV contrast diluted in water is first evaluated using simulated data. Results then are validated in an experimental set-up comparing SPR predictions for both NC and CE images to measurements made with a multi-layer ionisation chamber (MLIC). Finally, the impact of IV contrast on dose calculation using both SECT and DECT is evaluated for one liver and one head and neck patient. Using simulated data, DECT is shown to be less sensitive to the presence of IV contrast than SECT, although the performance of the method is sensitive to the level of beam hardening considered. For different concentrations of IV contrast diluted in water, experimental MLIC measurement of SPR agrees with DECT predictions within 3% while SECT introduce errors above 20%. This error in the SPR value results in a range error of up to 3.2 mm (2.6%) for proton beams calculated on SECT CE patient images. The error is reduced below 1 mm using DECT with the and VNC methods. Globally, it is observed that the influence of IV contrast on proton therapy dose calculation is mitigated using DECT over SECT. In patient anatomies, the VNC approach provides the best agreement with the reference dose distribution.

Non-linear dual-energy method development and evaluation for industrial computed tomography

Industrial computed tomography of multi-material objects can be problematic due to difficulties in optimising the x-ray spectra of the scan. A possible solution to the problem is to use two x-ray spectra when scanning objects. The results from such scans can be fused into a single data-set that contains enhanced information. This practice is known as dual-energy computed tomography (DECT). In this work, the aim was to investigate two DECT methods ability to improve measurements in multi-material phantoms. To determine the performance of the methods three different phantoms containing precision spheres as measurement objects were investigated. To improve measurements in this work was defined as improving the measurement consistency of diameter measurements. The phantoms were also scanned with a single setting for comparison. The fusion of the data-sets was done using two methods, a linear fusion, and a novel non-linear fusion. Both of the methods relies on pre-reconstruction fusion of data-sets. The results show that both of the DECT methods improved measurement results significantly compared to the reference method. Further, the results show that the novel non-linear DECT method produces more accurate measurement results compared to the linear method.

Systematic analysis of the impact of imaging noise on dual-energy CT-based proton stopping power ratio estimation.

Dual-energy CT (DECT) has been shown to have a great potential in reducing the uncertainty in proton stopping power ratio (SPR) estimation, when compared to current standard method - the stoichiometric method based on single-energy CT (SECT). However, a few recent studies indicated that imaging noise may have a substantial impact on the performance of the DECT-based approach, especially at a high noise level. The goal of this study is to quantify the uncertainty in SPR and range estimation caused by noise in the DECT-based approach under various conditions. Two widely referred parametric DECT methods were studied: the Hünemohr-Saito (HS) method and the Bourque method. Both methods were calibrated using Gammex tissue substitute inserts scanned on the Siemens Force DECT scanner. An energy pair of 80 and 150kVp with a tin filter was chosen to maximize the spectral separation. After calibrating the model with the Gammex phantom, CT numbers were synthesized using the density and elemental composition from ICRU 44 human tissues to be used as a reference, in order to evaluate the impact of noise alone while putting aside other sources of uncertainty. Gaussian noise was introduced to the reference CT numbers and its impact was measured with the difference between estimated SPR and its noiseless reference SPR. The uncertainty caused by noise was divided into two independent categories: shift of the mean SPR and variation of SPR. Their overall impact on range uncertainty was evaluated on homogeneous and heterogeneous tissue samples of various water equivalent path lengths (WEPL). Due to the algorithms being nonlinear and/or having hard thresholds in the CT number to SPR mapping, noise in the CT numbers induced a shift in the mean SPR from its noiseless reference SPR. The degree of the mean shift was dependent on the algorithm and tissue type, but its impact on the SPR uncertainty was mostly small compared to the variation. All mean shifts observed in this study were within 0.5% at a noise level of 2%. The ratio of the influence of variation to mean shift was mostly greater than 1, indicating that variation more likely determined the uncertainty caused by noise. Overall, the range uncertainty (95th percentile) caused by noise was within 1.2% and 1.0% for soft and bone tissues, respectively, at 2% noise with 50 voxels. This value can be considered an upper limit as more voxels and lower noise level rapidly decreased the uncertainty. We have systematically evaluated the impact of noise to the DECT-based SPR estimation and identified under various conditions that the variation caused by noise is the dominant uncertainty-contributing component. We conclude that, based on the noise level and tumor depth, it is important to estimate and include the uncertainty due to noise in estimating the overall range uncertainty before implementing a small margin in the range of 1%.

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.

PO-0971: Monte Carlo validation of a new dual-energy CT method for proton therapy in a patient-like geometry

PO-0971: Monte Carlo validation of a new dual-energy CT method for proton therapy in a patient-like geometry

On the equivalence of image-based dual-energy CT methods for the determination of electron density and effective atomic number in radiotherapy.

Dual-energy computed tomography enables the determination of relative electron density and effective atomic number. As this can increase accuracy in radiotherapy treatment planning, a substantial number of algorithms for the determination of the two quantities has been suggested – most of them based on reconstructed CT images. We show that many of these methods share a common theoretical framework. Equations can be transformed from one method to the other by re-definition of the calibration parameters. We suggest that further work should be spent on practical calibration and the reliability of CT numbers rather than on the theoretical framework.

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.