A simulation framework for preclinical proton irradiation workflow

Objective.The integration of proton beamlines with x-ray imaging/irradiation platforms has opened up possibilities for image-guided Bragg peak irradiations in small animals. Such irradiations allow selective targeting of normal tissue substructures and tumours. However, their small size and location pose challenges in designing experiments. This work presents a simulation framework useful for optimizing beamlines, imaging protocols, and design of animal experiments. The usage of the framework is demonstrated, mainly focusing on the imaging part.Approach.The fastCAT toolkit was modified with Monte Carlo (MC)-calculated primary and scatter data of a small animal imager for the simulation of micro-CT scans. The simulated CT of a mini-calibration phantom from fastCAT was validated against a full MC TOPAS CT simulation. A realistic beam model of a preclinical proton facility was obtained from beam transport simulations to create irradiation plans in matRad. Simulated CT images of a digital mouse phantom were generated using single-energy CT (SECT) and dual-energy CT (DECT) protocols and their accuracy in proton stopping power ratio (SPR) estimation and their impact on calculated proton dose distributions in a mouse were evaluated.Main results.The CT numbers from fastCAT agree within 11 HU with TOPAS except for materials at the centre of the phantom. Discrepancies for central inserts are caused by beam hardening issues. The root mean square deviation in the SPR for the best SECT (90 kV/Cu) and DECT (50 kV/Al-90 kV/Al) protocols are 3.7% and 1.0%, respectively. Dose distributions calculated for SECT and DECT datasets revealed range shifts <0.1 mm, gamma pass rates (3%/0.1 mm) greater than 99%, and no substantial dosimetric differences for all structures. The outcomes suggest that SECT is sufficient for proton treatment planning in animals.Significance.The framework is a useful tool for the development of an optimized experimental configuration without using animals and beam time.

A direct comparison of multi-energy x-ray and proton CT for imaging and relative stopping power estimation of plastic andex-vivophantoms

Objective.Proton therapy administers a highly conformal dose to the tumour region, necessitating accurate prediction of the patient's 3D map of proton relative stopping power (RSP) compared to water. This remains challenging due to inaccuracies inherent in single-energy computed tomography (SECT) calibration. Recent advancements in spectral x-ray CT (xCT) and proton CT (pCT) have shown improved RSP estimation compared to traditional SECT methods. This study aims to provide the first comparison of the imaging and RSP estimation performance among dual-energy CT (DECT) and photon-counting CT (PCCT) scanners, and a pCT system prototype.Approach.Two phantoms were scanned with the three systems for their performance characterisation: a plastic phantom, filled with water and containing four plastic inserts and a wood insert, and a heterogeneous biological phantom, containing a formalin-stabilised bovine specimen. RSP maps were generated by converting CT numbers to RSP using a calibration based on low- and high-energy xCT images, while pCT utilised a distance-driven filtered back projection algorithm for RSP reconstruction. Spatial resolution, noise, and RSP accuracy were compared across the resulting images.Main results.All three systems exhibited similar spatial resolution of around 0.54 lp/mm for the plastic phantom. The PCCT images were less noisy than the DECT images at the same dose level. The lowest mean absolute percentage error (MAPE) of RSP,(0.28±0.07)%, was obtained with the pCT system, compared to MAPE values of(0.51±0.08)%and(0.80±0.08)%for the DECT- and PCCT-based methods, respectively. For the biological phantom, the xCT-based methods resulted in higher RSP values in most of the voxels compared to pCT.Significance.The pCT system yielded the most accurate estimation of RSP values for the plastic materials, and was thus used to benchmark the xCT calibration performance on the biological phantom. This study underlined the potential benefits and constraints of utilising such a novelex-vivophantom for inter-centre surveys in future.

Estimation of Proton Stopping Power Ratio and Mean Excitation Energy Using Electron Density and Its Applications via Machine Learning Approach

Purpose

The purpose of this study was to develop a simple flexible method for accurate estimation of stopping power ratio (SPR) and mean excitation energy (I) using relative electron density (ρ e).

Materials and Methods

The model was formulated using empirical relationships between SPR, mean excitation energy I, and relative electron density. Some examples were implemented, and a comparison was carried out using other existing methods. The needed coefficients in the model were estimated using optimization tools. Basis vector method (BVM) and Hunemohr and Saito (H-S) method were applied to estimate the ρ e used in the application section. 80 kVp and 150 kVpSn were used as low and high energy, respectively, for the implementation of dual-energy methods.

Results

All the examples of the proposed method considered have modeling error that is ≤0.32% and testing root mean square error (RMSE) ≤0.92% for SPR with a mean error close to 0.00%. The method was able to achieve modeling RMSE of 2.12% for mean excitation energy with room for improvement. Similar or better results were achieved in application to BVM.

Conclusion

The method showed robustness in application by achieving lower testing error than other presented methods in most cases. It achieved accurate estimation which can be improved using the machine learning algorithm since it is flexible to implement in terms of the function (model) degree and tissue classification.

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 Bourqueet 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 (Zeff) and electron density relative to water (ρe). The model was calibrated and evaluated using tissue-equivalent materials from the RMI Gammex electron density phantom with knownρevalues and elemental compositions. TheoreticalZeffvalues were found for the appropriate energy ranges using the elemental composition of the materials.Zeffvaried slightly with energy but was considered a systematic error. Anex vivobovine 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 extractedZeffandρefor PCD-CT were 0.76% and 0.72%, respectively and 1.77% and 1.98% for DECT. The tissue types in theex vivobovine tissue sample were also correctly identified after decomposition. Additionally, gold chloride was separated from theex vivotissue 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.

Material decomposition maps based calibration of dual energy CT scanners for proton therapy planning: a phantom study

We introduce a new calibration method for dual energy CT (DECT) based on material decomposition (MD) maps, specifically iodine and water MD maps. The aim of this method is to provide the first DECT calibration based on MD maps. The experiments were carried out using a general electric (GE) revolution CT scanner with ultra-fast kV switching and used a density phantom by GAMMEX for calibration and evaluation. The calibration process involves several steps. First, we tested the ability of MD values to reproduce Hounsfield unit (HU) values of single energy CT (SECT) acquisitions and it was found that the errors were below 1%, validating their use for HU reproduction. Next, the different definitions of computedZvalues were compared and the robustness of the approach based on the materials' composition was confirmed. Finally, the calibration method was compared with a previous method by Bourqueet al, providing a similar level of accuracy and superior performance in terms of precision. Overall, this novel DECT calibration method offers improved accuracy and reliability in determining tissue-specific physical properties. The resulting maps can be valuable for proton therapy treatments, where precise dose calculations and accurate tissue differentiation are crucial for optimal treatment planning and delivery.

The influence of dual-energy computed tomography image noise in proton therapy treatment planning

Background and purpose

In proton therapy, a 3.5% margin is often used to account for proton range uncertainties, of which computed tomography (CT) image noise is assumed to contribute 0.5%. This work evaluates the noise-sensitivity of three dual-energy computed tomography (DECT)-based methods for mapping proton stopping power relative to water (SPR): Näsmark & Andersson (N&A), Landry-Saito (L-S), and the commercial application DirectSPR.

Methods and materials

DECT image data of a CIRS-062M phantom was acquired with CT scanners from two different vendors. Acquisitions were repeated 30 times to account for intra- and inter-scan variations. SPR maps were generated with the three DECT-based methods and range simulated in a commercial treatment planning system.

Results

Noise in input data was amplified in L-S SPR maps, kept level with DirectSPR, while N&A compressed noise overall but displayed sensitivity to the choice of input data, potentially leading to increased noise levels. In our simulations, only N&A improved upon the assumed 0.5% noise contribution to range uncertainty on one scanner. On the other scanner, uncertainties exceeded 0.5% for all three methods. Mitigation of this issue was demonstrated by using a method employing virtual mono-energetic images as input. Increasing imaging radiation dose, as expected, alleviates the problem, while applying noise reduction only helped to a lesser extent.

Conclusions

While range uncertainty due to noise is small compared to other contributions, it becomes more important as we move towards smaller treatment margins and the noise-sensitivity of SPR mapping methods should be carefully estimated and considered before clinical implementation.

MRI-based quantification of carbon and oxygen concentrations in human soft tissues for range verification in proton therapy

BACKGROUND

In-situ range verification of particle therapy based on the detection of secondary emitted radiation requires highly accurate assignment of elemental concentrations (particularly carbon and oxygen) in the human body.

PURPOSE

A method for quantitatively predicting carbon and oxygen concentrations in human soft tissues is proposed. This method relies on an empirical one-to-one correspondence between the mass fraction and water content (WC), which is a measurable tissue quantity based on magnetic resonance (MR) imaging (referred to as "MRWC-based method").

METHODS

A numerical analysis of the MRWC-based method was performed for 47 standard human soft tissues tabulated in the literature as objects of interest with unknown mass fractions of the four main elements-C, O, H, and N. Thereafter, the method was evaluated in terms of the mass-fraction quantification accuracy by comparing it with the gold-standard CT-based method developed by Schneider et al. The MRWC-based method was also applied to the MR imaging data of a virtual head phantom obtained from a three-dimensional MRI-simulated brain database.

RESULTS

The predicted mass fractions in a range of human soft tissues were in better agreement with the reference values than those predicted by the CT-based method. The mean absolute errors of the predicted mass% values for the overall standard soft tissues could be reduced from 4.8 percentage points (pp) (CT-based) to 0.5 pp (MRWC-based) for carbon and from 5.2 pp (CT-based) to 0.4 pp (MRWC-based) for oxygen. The application to the simulated MRI data confirmed the capability of the sufficient recognition of the boundaries between the white matter and gray matter in the brain that could not be realized by the CT-based method. Thus, the MRWC-based method exhibits superior performance in the prediction of carbon and oxygen concentrations in soft tissues.

CONCLUSIONS

This study is limited to a proof-of-concept scope but demonstrates the feasibility of the MRWC-based method for the generation of elemental images of human soft tissues from MRI-derived water-content images.

Creation, evolution, and future challenges of ion beam therapy from a medical physicist's viewpoint (Part 2). Chapter 2. Biophysical model, treatment planning system and image guided radiotherapy

When an ion beam penetrates deeply into the body, its kinetic energy decreases, and its biological effect increases due to the change of the beam quality. To give a uniform biological effect to the target, it is necessary to reduce the absorbed dose with the depth. A bio-physical model estimating the relationship between ion beam quality and biological effect is necessary to determine the relative biological effectiveness (RBE) of the ion beam that changes with depth. For this reason, Lawrence Berkeley Laboratory, National Institute of Radiological Sciences (NIRS) and GSI have each developed their own model at the starting of the ion beam therapy. Also, NIRS developed a new model at the starting of the scanning irradiation. Although the Local Effect Model (LEM) at the GSI and the modified Microdosimetric Kinetic Model (MKM) at the NIRS, the both are currently used, can similarly predict radiation quality-induced changes in surviving fraction of cultured cell, the clinical RBE-weighted doses for the same absorbed dose are different. This is because the LEM uses X-rays as a reference for clinical RBE, whereas the modified MKM uses carbon ion beam as a reference and multiplies it by a clinical factor of 2.41. Therefore, both are converted through the absorbed dose. In PART 2, I will describe the development of such a bio-physical model, as well as the birth and evolution of a treatment planning system and image guided radiotherapy.

Stopping-power ratio estimation for proton radiotherapy using dual-energy computed tomography and prior-image constrained denoising

BACKGROUND

Dual-energy computed tomography (DECT) is a promising technique for estimating stopping-power ratio (SPR) for proton therapy planning. It is known, however, that deriving electron density (ED) and effective atomic number (EAN) from DECT data can cause noise amplification in the resulting SPR images. This can negate the benefits of DECT.

PURPOSE

This work introduces a new algorithm for estimating SPR from DECT with noise suppression, using a pair of CT scans with spectral separation. The method is demonstrated using phantom measurements.

MATERIALS AND METHODS

An iterative algorithm is presented, reconstructing ED and EAN with noise suppression, based on Prior Image Constrained Denoising (PIC-D). The algorithm is tested using a Siemens Definition AS+ CT scanner (Siemens Healthcare, Forchheim, Germany). Three phantoms are investigated: a calibration phantom (CIRS 062M), a QA phantom (CATPHAN 700), and an anthropomorphic head phantom (CIRS 731-HN). A task-transfer function (TTF) and the noise power spectrum are derived from SPR images of the QA phantom for the evaluation of image quality. Comparisons of accuracy and noise for ED, EAN, and SPR are made for various versions of the algorithm in comparison to a solution based on Siemens syngo.via Rho/Z software and the current clinical standard of a single-energy CT stoichiometric calibration. A gamma analysis is also applied to the SPR images of the head phantom and water-equivalent distance (WED) is evaluated in a treatment planning system for a proton treatment field.

RESULTS

The algorithm is effective at suppressing noise in both ED and EAN and hence also SPR. The noise is tunable to a level equivalent to or lower than that of the syngo.via Rho/Z software. The spatial resolution (10% and 50% frequencies in the TTF) does not degrade even for the highest noise suppression investigated, although the average spatial frequency of noise does decrease. The PIC-D algorithm showed better accuracy than syngo.via Rho/Z for low density materials. In the calibration phantom, it was superior even when excluding lung substitutes, with root-mean-square deviations for ED and EAN less than 0.3% and 2%, respectively, compared to 0.5% and 3%. In the head phantom, however, the SPR accuracy of the PIC-D algorithm was comparable (excluding sinus tissue) to that derived from syngo.via Rho/Z: less than 1% error for soft tissue, brain, and trabecular bone substitutes and 5-7% for cortical bone, with the larger error for the latter likely related to the phantom geometry. Gamma evaluation showed that PIC-D can suppress noise in a patient-like geometry without introducing substantial errors in SPR. The absolute pass rates were almost identical for PIC-D and syngo.via Rho/Z. In the WED evaluations no general differences were shown.

CONCLUSIONS

The PIC-D DECT algorithm provides scanner-specific calibration and tunable noise suppression. It is vendor agnostic and applicable to any pair of CT scans with spectral separation. Improved accuracy to current methods was not clearly demonstrated for the complex geometry of a head phantom, but the suppression of noise without spatial resolution degradation and the possibility of incorporating constraints on image properties, suggests the usefulness of the approach.

Dual- and multi-energy CT for particle stopping-power estimation: current state, challenges and potential

Range uncertainty has been a key factor preventing particle radiotherapy from reaching its full physical potential. One of the main contributing sources is the uncertainty in estimating particle stopping power (ρ_s) within patients. Currently, theρ_sdistribution in a patient is derived from a single-energy CT (SECT) scan acquired for treatment planning by converting CT number expressed in Hounsfield units (HU) of each voxel toρ_susing a Hounsfield look-up table (HLUT), also known as the CT calibration curve. HU andρ_sshare a linear relationship with electron density but differ in their additional dependence on elemental composition through different physical properties, i.e. effective atomic number and mean excitation energy, respectively. Because of that, the HLUT approach is particularly sensitive to differences in elemental composition between real human tissues and tissue surrogates as well as tissue variations within and among individual patients. The use of dual-energy CT (DECT) forρ_sprediction has been shown to be effective in reducing the uncertainty inρ_sestimation compared to SECT. The acquisition of CT data over different x-ray spectra yields additional information on the material elemental composition. Recently, multi-energy CT (MECT) has been explored to deduct material-specific information with higher dimensionality, which has the potential to further improve the accuracy ofρ_sestimation. Even though various DECT and MECT methods have been proposed and evaluated over the years, these approaches are still only scarcely implemented in routine clinical practice. In this topical review, we aim at accelerating this translation process by providing: (1) a comprehensive review of the existing DECT/MECT methods forρ_sestimation with their respective strengths and weaknesses; (2) a general review of uncertainties associated with DECT/MECT methods; (3) a general review of different aspects related to clinical implementation of DECT/MECT methods; (4) other potential advanced DECT/MECT applications beyondρ_sestimation.

Prediction of proton beam range in phantom with metals based on monochromatic energy CT images

The purpose of the study was to evaluate the accuracy of monochromatic energy (MonoE) computed tomography (CT) images reconstructed by spectral CT in predicting the stopping power ratio $( SP{R}_w)$ of materials in the presence of metal. The CIRS062 phantom was scanned three times using spectral CT. In the first scan, a solid water insert was placed at the center of the phantom $(C{T}_{no\ metal})$. In the second scan, the solid water insert was replaced with a titanium alloy femoral head $(C{T}_{metal})$. The metal artifact reduction (MAR) algorithm was used in the last scan $(C{T}_{metal+ MAR})$. The MonoE-CT images of 40 keV and 80 keV were reconstructed. Finally, the single-energy CT method (SECT) and the dual-energy CT method (DECT) were used to calculate the $SP{R}_w$. The mean absolute error (MAE) of the $SP{R}_w$ of the inner layer inserts calculated by the SECT method were 3.19%, 13.88% and 2.71%, corresponding to $C{T}_{no\ metal}$, $C{T}_{metal}$ and $C{T}_{metal+ MAR}$, respectively. For the outer layer inserts, the MAE of $SP{R}_w$ were 3.43%, 5.42% and 2.99%, respectively. Using the DECT method, the MAE of the $SP{R}_w$ of the inner layer inserts was 1.30%, 3.69% and 1.46% and the MAE of the outer layer inserts- was 1.34%, 1.36% and 1.05%. The studies shows that, compared with the SECT method, the accuracy of the DECT method in predicting the $SP{R}_w$ of a material is more robust to the presence of metal. Using the MAR algorithm when performing CT scans can further improve the accuracy of predicting the SPR of materials in the presence of metal.

Assessment of quantitative information for radiation therapy at a first-generation clinical photon-counting computed tomography scanner

As one of the latest developments in X-ray computed tomography (CT), photon-counting technology allows spectral detection, demonstrating considerable advantages as compared to conventional CT. In this study, we investigated the use of a first-generation clinical photon-counting computed tomography (PCCT) scanner and estimated proton relative (to water) stopping power (RSP) of tissue-equivalent materials from virtual monoenergetic reconstructions provided by the scanner. A set of calibration and evaluation tissue-equivalent inserts were scanned at 120 kVp. Maps of relative electron density (RED) and effective atomic number (EAN) were estimated from the reconstructed virtual monoenergetic images (VMI) using an approach previously applied to a spectral CT scanner with dual-layer detector technology, which allows direct calculation of RSP using the Bethe-Bloch formula. The accuracy of RED, EAN, and RSP was evaluated by root-mean-square errors (RMSE) averaged over the phantom inserts. The reference RSP values were obtained experimentally using a water column in an ion beam. For RED and EAN, the reference values were calculated based on the mass density and the chemical composition of the inserts. Different combinations of low- and high-energy VMIs were investigated in this study, ranging from 40 to 190 keV. The overall lowest error was achieved using VMIs at 60 and 180 keV, with an RSP accuracy of 1.27% and 0.71% for the calibration and the evaluation phantom, respectively.

Dual-Energy Computed Tomography For Differentiation Between Osteoblastic Metastases and Bone Islands

Objective

The objective of our study was to evaluate the utility of Rho/Z on dual-energy computed tomography (DECT) for the differentiation of osteoblastic metastases (OBMs) from bone islands (BIs).

Methods

DECT images of 110 patients with malignancies were collected. The effective atomic number (Z), electron density (Rho), dual energy index (DEI), and regular CT (rCT) values were measured by two observers. Independent-sample t-test was used to compare these values between OBMs and BIs. The diagnostic performance was assessed by receiver operating characteristic (ROC) analysis and the cutoff values were evaluated according to ROC curves.

Results

A total of 205 OBMs and 120 BIs were included. The mean values of Z, Rho, DEI, and rCT of OBMs were significantly lower than those of BIs, whereas the standard deviation values were higher than those of BIs (all p ≤ 0.05). ROC analysis showed that 11.86 was the optimal cutoff value for Z, rendering an area under the ROC curve (AUC) of 0.91, with a sensitivity of 91.2% and a specificity of 82.5%.

Conclusion

DECT can provide quantitative values of Z, Rho, and DEI and has good performance in differentiating between OBMs and BIs.

Dual energy CT for a small animal radiation research platform using an empirical dual energy calibration

Objective. Dual energy computed tomography (DECT) has been shown to provide additional image information compared to conventional CT and has been used in clinical routine for several years. The objective of this work is to present a DECT implementation for a Small Animal Radiation Research Platform (SARRP) and to verify it with a quantitative analysis of a material phantom and a qualitative analysis with an ex-vivo mouse measurement. Approach. For dual energy imaging, two different spectra are required, but commercial small animal irradiators are usually not optimized for DECT. We present a method that enables dual energy imaging on a SARRP with sequential scanning and an Empirical Dual Energy Calibration (EDEC). EDEC does not require the exact knowledge of spectra and attenuation coefficients; instead, it is based on a calibration. Due to the SARRP geometry and reconstruction algorithm, the calibration is done using an artificial CT image based on measured values. The calibration yields coefficients to convert the measured images into material decomposed images. Main results. To analyze the method quantitatively, the electron density and the effective atomic number of a material phantom were calculated and compared with theoretical values. The electron density showed a maximum deviation from the theoretical values of less than 5% and the atomic number of slightly more than 6%. For use in mice, DECT is particularly useful in distinguishing iodine contrast agent from bone. A material decomposition of an ex-vivo mouse with iodine contrast agent was material decomposed to show that bone and iodine can be distinguished and iodine-corrected images can be calculated. Significance. DECT is capable of calculating electron density images and effective atomic number images, which are appropriate parameters for quantitative analysis. Furthermore, virtual monochromatic images can be obtained for a better differentiation of materials, especially bone and iodine contrast agent.

Dual-energy CT: minimal essentials for radiologists

Dual-energy CT, the object is scanned at two different energies, makes it possible to identify the characteristics of materials that cannot be evaluated on conventional single-energy CT images. This imaging method can be used to perform material decomposition based on differences in the material-attenuation coefficients at different energies. Dual-energy analyses can be classified as image data-based- and raw data-based analysis. The beam-hardening effect is lower with raw data-based analysis, resulting in more accurate dual-energy analysis. On virtual monochromatic images, the iodine contrast increases as the energy level decreases; this improves visualization of contrast-enhanced lesions. Also, the application of material decomposition, such as iodine- and edema images, increases the detectability of lesions due to diseases encountered in daily clinical practice. In this review, the minimal essentials of dual-energy CT scanning are presented and its usefulness in daily clinical practice is discussed.

Correlation of dual energy computed tomography electron density measurements with cerebral glioma grade

OBJECTIVE

To correlate dual energy computed tomography electron density measurements with histopathological cerebral glioma grading to determine whether it can be used as a non-invasive predictor of cerebral glioma grade.

MATERIALS AND METHODS

Fifty patients with suspected cerebral gliomas on imaging scheduled to undergo resection were included. We tested our hypothesis that with increasing glioma grade, increased tumor cellularity should translate into increased electron density and if a statistically significant difference between electron density of low-grade gliomas and high-grade gliomas is seen, we may have a clinical use of dual energy computed tomography as a non-invasive tool to predict cerebral glioma grade.A pre-operative dual energy computed tomography scan of the brain was performed, and electron density measurements calculated from the solid part of the tumor. Obtaining a ratio with electron density of contralateral normal brain parenchyma normalized these values. The minimum, maximum and mean electron density and their normalized values recorded between high-grade gliomas and low-grade gliomas were compared for presence of statistical significance.

RESULTS

A statistically significant difference was found between all six parameters recorded (minimum electron density and normalized values, mean electron density and normalized values, maximum electron density and normalized values) between low-grade gliomas and high-grade gliomas. The predictivity ranged from 75% (for minimum electron density and maximum normalized values) to 81.25% (for mean normalized values). All six parameters were found to have statistically significant positive correlation with Ki-67 index.

CONCLUSION

Dual energy computed tomography electron density measurements in cerebral gliomas are predictive of pre-operative differentiation of low-grade gliomas from high-grade gliomas and show a linear, statistically significant positive correlation with Ki-67 index.

Physical density estimations of single- and dual-energy CT using material-based forward projection algorithm: a simulation study

OBJECTIVES

This study aims to evaluate the accuracy of physical density prediction in single-energy CT (SECT) and dual-energy CT (DECT) by adapting a fully simulation-based method using a material-based forward projection algorithm (MBFPA).

METHODS

We used biological tissues referenced in ICRU Report 44 and tissue substitutes to prepare three different types of phantoms for calibrating the Hounsfield unit (HU)-to-density curves. Sinograms were first virtually generated by the MBFPA with four representative energy spectra (i.e. 80 kVp, 100 kVp, 120 kVp, and 6 MVp) and then reconstructed to form realistic CT images by adding statistical noise. The HU-to-density curves in each spectrum and their pairwise combinations were derived from the CT images. The accuracy of these curves was validated using the ICRP110 human phantoms.

RESULTS

The relative mean square errors (RMSEs) of the physical density by the HU-to-density curves calibrated with kV SECT nearly presented no phantom size dependence. The kV-kV DECT calibrated curves were also comparable with those from the kV SECT. The phantom size effect became notable when the MV X-ray beams were employed for both SECT and DECT due to beam-hardening effects. The RMSEs were decreased using the biological tissue phantom.

CONCLUSION

Simulation-based density prediction can be useful in the theoretical analysis of SECT and DECT calibrations. The results of this study indicated that the accuracy of SECT calibration is comparable with that of DECT using biological tissues. The size and shape of the calibration phantom could affect the accuracy, especially for MV CT calibrations.

ADVANCES IN KNOWLEDGE

The present study is based on a full simulation environment, which accommodates various situations such as SECT, kV-kV DECT, and even kV-MV DECT. In this paper, we presented the advances pertaining to the accuracy of the physical density prediction when applied to SECT and DECT in the MV X-ray energy range. To the best of our knowledge, this study is the first to validate the physical density estimation both in SECT and DECT using human-type phantoms.

kV-kV and kV-MV DECT based estimation of proton stopping power ratio - a simulation study

PURPOSE

This study aims to estimate the proton stopping power ratio (SPR) by using 80-120 kV and 120 kV-6 MV dual-energy CT (DECT) in a fully simulation-based approach for proton therapy dose calculations.

METHODS

Based on a virtual CT system, a two-step approach is applied to obtain the reference attenuation coefficient for image reconstruction. The effective atomic number (EAN) and electron density ratio (EDR) are estimated from two CT scans. The SPR is estimated using a calibration based on known materials to obtain a piecewise linear relationship between the EAN and the logarithm of the mean excitation energy, lnI_m. The calibration phantoms are constructed from reference tissue materials taken from ICRU Report 44. Our approach is evaluated through using the ICRP110 human phantom. The respective influences of noise and beam hardening effects are studied.

RESULTS

With the beam hardening correction applied, the results of 120 kV-6 MV DECT are comparable to those of 80-120 kV DECT in predicting the EAN, but the former demonstrated a clear improvement in predicting the EDR and SPR. The 120 kV-6 MV DECT is able to predict the SPR within an accuracy of 10% for lung tissue and 5% for pelvis tissue, thereby outperforming the 80-120 kV DECT.

CONCLUSIONS

The 120 kV-6 MV DECT is less sensitive to noise but more susceptible to beam hardening effects. By applying beam hardening correction, the 120 kV-6 MV DECT can predict the SPR more accurately than the 80-120 kV DECT. To utilize our DECT approach most effectively, high-quality reconstructed images are required.

Dual-Energy CT-Derived Electron Density for Diagnosing Metastatic Mediastinal Lymph Nodes in Non-Small Cell Lung Cancer: Comparison With Conventional CT and FDG PET/CT Findings

Background: Accurate nodal staging is essential to guide treatment selection in patients with non-small cell lung cancer (NSCLC). To our knowledge, measurement of electron density (ED) using dual-energy CT (DECT) is unexplored for this purpose. Objective: To assess the utility of ED from DECT in diagnosing metastatic mediastinal lymph nodes in patients with NSCLC, in comparison with conventional CT and FDG PET/CT. Methods: This retrospective study included 57 patients (36 men, 21 women; mean age 68.4±8.9 years) with NSCLC and surgically resected mediastinal lymph nodes who underwent preoperative DECT and FDG PET/CT. The patients had a total of 117 resected mediastinal lymph nodes (33 metastatic, 84 nonmetastatic). Two radiologists independently reviewed nodes' morphologic features on the 120 kVp images and also measured nodes' iodine concentration (IC) and ED using maps generated from DECT data; consensus was reached for discrepancies. Two separate radiologists assessed FDG PET/CT examinations in consensus for positive node uptake. Diagnostic performance was evaluated for individual and pairwise combinations of features. Results: The sensitivity, specificity, and accuracy for nodal metastasis were 15.2%, 98.8%, and 75.2% for presence of necrosis; 54.5%, 85.7%, and 76.9% for short-axis diameter >8.5 mm; 63.6%, 73.8%, and 70.9% for long-axis diameter >13.0 mm; 51.5%, 79.8%, and 71.8% for attenuation on 120 kVp images ≤95.8 HU; 87.9%, 58.3%, and 66.7% for ED ≤3.48×1023/cm3; and 66.7%, 75.0%, and 72.6% for positive FDG uptake, respectively. Among pairwise combinations of features, accuracy was highest for the combination of ED and short-axis diameter (accuracy 82.9%, sensitivity 54.5%, specificity 94.0%) and the combination of ED and positive FDG uptake (accuracy 82.1%, sensitivity 60.6%, specificity 90.5%); these accuracies were greater than for the individual features (p<.05). Remaining combinations exhibited accuracies ranging from 74.4% to 77.8%. Interobserver agreement analysis demonstrated intraclass correlation coefficient of 0.90 for ED. IC was not significantly different between metastatic and nonmetastatic nodes (p=.18) and was excluded from the diagnostic performance analysis. Conclusion: ED derived from DECT may help diagnose metastatic lymph nodes in NSCLC given decreased ED in metastatic nodes. Clinical Impact: ED may complement conventional CT findings and FDG uptake on PET/CT in diagnosing metastatic nodes.

Proton stopping power prediction based on dual-energy CT-generated virtual monoenergetic images

PURPOSE

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.

MATERIALS AND METHODS

A rapid kV-switching dual-energy CT scanner was used to acquire Gemstone Spectral Imaging (GSI) and 120 kV 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 140 keV 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.

RESULTS

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.

CONCLUSIONS

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.

Quadratic relation for mass density calibration in human body using dual-energy CT data

PURPOSE

To derive the mass density (ρ) from dual-energy computed tomography (DECT) data by calibrating electron density (ρ_e ) and effective atomic numbers (Z_eff ) of human tissues.

METHODS

We propose the DEEDZ-MD method, in which a single polynomial parameterization covers the entire human-tissue range to establish an empirical quadratic relation between the atomic number-to-mass ratio and Z_eff . Then, we numerically evaluate the DEEDZ-MD method in reference human tissues listed in the ICRP Publication 110 and ICRU Report 46. The tissues are considered to have unknown ρ values. The attenuation coefficients of these tissues are calculated using the XCOM Photon Cross Sections Database. The DEEDZ-MD method is also applied to experimental DECT data acquired from a tissue characterization phantom and an anthropomorphic phantom at 90 kV and 150 kV/Sn.

RESULTS

The numerical analysis of the DEEDZ-MD method reveals a single quadratic relation between the atomic number-to-mass ratio and Z_eff in a wide range of human tissues. The simulated ρ values are in excellent agreement with the reference values over ρ values from 0.260 (lung) to 3.225 (hydroxyapatite). The relative deviations from the reference ρ remain within ±0.6% for all the reference human tissues, except for the eye lens (approximate deviation of -1.0%). The overall root-mean-square error is 0.24%. The application of the DEEDZ-MD method to experimental dual-energy CT data confirms this agreement within experimental accuracy, indicating the practical feasibility of the method. The DEEDZ-MD method enables the generation of ρ images with less image noise than the existing DECT-based conversion of ρ from ρ_e and with fewer beam-hardening artifacts than conventional single-energy CT images.

CONCLUSIONS

The DEEDZ-MD method can facilitate the generation of ρ images from dual-energy CT data without relying on the nontrivial segmentation of different tissues.

Characterization of soy-lignin bonded Rhizophora spp. particleboard as substitute phantom material for radiation dosimetric studies - Investigation of CT number, mass attenuation coefficient and effective atomic number

Experimental particleboards are made from Rhizophora spp. wood trunk with three different percentages of lignin and soy flour (0%, 6% and 12%) as adhesives. The objective was to investigate the equivalence of Rhizophora spp. particleboard as phantom material with human soft tissue using Computed Tomography (CT) number. The linear and mass attenuation coefficient of Rhizophora spp. particleboard at low energy range was also explored using X-ray Fluorescence (XRF) configuration technique. Further characterization of the particleboard was performed to determine the effective atomic number, Zeff using Energy Dispersive X-Ray (EDX) method. Adhesive-bonded Rhizophora spp. particleboard showed close similarities with water, based on the average CT numbers, electron density calibration curve and the analysis of CT density profile, compared to the binderless particleboard. The effective atomic number obtained from the study indicated that the attenuation properties of all the particleboards at different percentages of adhesives were almost similar to water. The mass attenuation coefficient calculated from XRF configuration technique showed good agreement with water from XCOM database, suggesting its potential as phantom material for radiation study.

Synthetic dual-energy CT for MRI-only based proton therapy treatment planning using label-GAN

MRI-only treatment planning is highly desirable in the current proton radiation therapy workflow due to its appealing advantages such as bypassing MR-CT co-registration, avoiding x-ray CT exposure dose and reduced medical cost. However, MRI alone cannot provide stopping power ratio (SPR) information for dose calculations. Given that dual energy CT (DECT) can estimate SPR with higher accuracy than conventional single energy CT, we propose a deep learning-based method in this study to generate synthetic DECT (sDECT) from MRI to calculate SPR. Since the contrast difference between high-energy and low-energy CT (LECT) is important, and in order to accurately model this difference, we propose a novel label generative adversarial network-based model which can not only discriminate the realism of sDECT but also differentiate high-energy CT (HECT) and LECT from DECT. A cohort of 57 head-and-neck cancer patients with DECT and MRI pairs were used to validate the performance of the proposed framework. The results of sDECT and its derived SPR maps were compared with clinical DECT and the corresponding SPR, respectively. The mean absolute error for synthetic LECT and HECT were 79.98 ± 18.11 HU and 80.15 ± 16.27 HU, respectively. The corresponding SPR maps generated from sDECT showed a normalized mean absolute error as 5.22% ± 1.23%. By comparing with the traditional Cycle GANs, our proposed method significantly improves the accuracy of sDECT. The results indicate that on our dataset, the sDECT image form MRI is close to planning DECT, and thus shows promising potential for generating SPR maps for proton therapy.

Convolutional neural network based proton stopping-power-ratio estimation with dual-energy CT: a feasibility study

Dual-energy computed tomography (DECT) has shown a great potential for lowering range uncertainties, which is necessary for truly leveraging the Bragg peak in proton therapy. However, analytical stopping-power-ratio (SPR) estimation methods have limitations in resolving the influence from the beam-hardening artifact, i.e. CT number variation of the same object scanned under different imaging conditions, such as different patient size and location in the field-of-view (FOV). We present a convolutional neural network (CNN)-based framework to estimate proton SPR that accounts for patient geometry variation and addresses CT number variation. The proposed framework was tested on both prostate and head-and-neck (HN) patient datasets. Simulated CT images were used in order to have a well-defined ground-truth SPR for evaluation. Two training scenarios were evaluated: training with patient CT images (ideal scenario) and training with computational phantoms (realistic scenario). For the training in ideal scenario, computational phantoms were created based on 120 kVp patient CT images using a custom-defined density and material translation curve. Then, 80 kVp and 150 kVp Sn DECT image pairs were obtained using ray-tracing simulation, and their corresponding SPR was calculated from the known density and elemental compositions. For the training in realistic scenario, computational phantoms were created based on the geometry of calibration phantoms. For both scenarios, evaluation was performed on the phantoms created from patient CT images. Compared to a conventional parametric model, U-net trained with computational phantoms (realistic scenario) reduced the SPR estimation uncertainty (95th percentile) of the prostate patient from 1.10% to 0.71%, and HN patient from 2.11% to 1.20%. With the U-net trained with patient images (ideal scenario) uncertainty values were 0.32% and 0.42% for prostate and HN patients, respectively. These results suggest that CNN has great potential to improve the accuracy of SPR estimation in proton therapy by incorporating individual patient geometry information.

Comparison of virtual non-contrast dual-energy CT and a true non-contrast CT for contouring in radiotherapy of 3D printed lung tumour models in motion: a phantom study

OBJECTIVES

This work aims to investigate whether virtual non-contrast (VNC) dual-energy CT(DECT) of contrasted lung tumours can be used as an alternative for true non-contrast (TNC) images in radiotherapy. Two DECT techniques and a TNC CT were compared and influences on gross tumour volume (GTV) volume and CT number from motion artefacts in three-dimensional printed lung tumour models (LTM) in amotion phantom were examined.

METHODS

Two spherical LTMs (diameter 3.0 cm) with different inner shapes were created in a three-dimensional printer. The inner shapes contained water or iodine (concentration 5 mg ml^-1) and were scanned with a dual-source DECT (ds-DECT), single-source sequential DECT (ss-DECT) and TNC CT in a respiratory motion phantom (15 breaths/min, amplitude 1.5 cm). CT number and volume of LTMs were measured. Therefore, two GTVs were contoured.

RESULTS

Deviations in GTV volume (outer shape) of LTMs in motion for contrast-enhanced ss-DECT and ds-DECT VNC images compared to TNC images are not significant (p > 0.05). Relative GTV volume and CT number deviations (inner shapes) of LTMs in motion were 6.6 ± 0.6% and 104.4 ± 71.2 HU between ss-DECT and TNC CT and -8.4 ± 10.6% and 25.5 ± 58.5 HU between ds-DECT and TNC, respectively.

CONCLUSION

ss-DECT VNC images could not sufficiently subtract iodine from water in LTMs inmotion, whereas ds-DECT VNC images might be a valid alternative to a TNC CT.

ADVANCES IN KNOWLEDGE

ds-DECT provides a contrasted image for contouring and a non-contrasted image for radiotherapy treatment planning for LTM in motion.

Spectral Computed Tomography: Fundamental Principles and Recent Developments

CT is a diagnostic tool with many clinical applications. The CT voxel intensity is related to the magnitude of X-ray attenuation, which is not unique to a given material. Substances with different chemical compositions can be represented by similar voxel intensities, making the classification of different tissue types challenging. Compared to the conventional single-energy CT, spectral CT is an emerging technology offering superior material differentiation, which is achieved using the energy dependence of X-ray attenuation in any material. A specific form of spectral CT is dual-energy imaging, in which an additional X-ray attenuation measurement is obtained at a second X-ray energy. Dual-energy CT has been implemented in clinical settings with great success. This paper reviews the theoretical basis and practical implementation of spectral/dual-energy CT.

Status and innovations in pre-treatment CT imaging for proton therapy

Pre-treatment CT imaging is a topic of growing importance in particle therapy. Improvements in the accuracy of stopping-power prediction are demanded to allow for a dose conformality that is not inferior to state-of-the-art image-guided photon therapy. Although range uncertainty has been kept practically constant over the last decades, recent technological and methodological developments, like the clinical application of dual-energy CT, have been introduced or arise at least on the horizon to improve the accuracy and precision of range prediction. This review gives an overview of the current status, summarizes the innovations in dual-energy CT and its potential impact on the field as well as potential alternative technologies for stopping-power prediction.

A comparison study between single- and dual-energy CT density extraction methods for neurological proton monte carlo treatment planning

Monte Carlo proton dose calculations requires mass densities calculated from the patient CT image. This work investigates the impact of different single-energy CT (SECT) and dual-energy CT (DECT) to density conversion methods in proton dose distributions for brain tumours.Material and methods: Head CT scans for four patients were acquired in SECT and DECT acquisition modes. Commercial software was used to reconstruct DirectDensity^™ images in Relative Electron Densities (RED, [Formula: see text]) and to obtain DECT-based pseudo-monoenergetic images (PMI). PMI and SECT images were converted to RED using piecewise linear interpolations calibrated on a head-sized phantom, these fits were referred to as "PMI2RED" and "CT2RED". Two DECT-based calibration methods ("Hünemohr-15it" and "Saito-15it") were also investigated. [Formula: see text] images were converted to mass-densities ([Formula: see text]) to investigate [Formula: see text]differences and one representative patient case was used to make a proton treatment plan. Using CT2RED as reference method, dose distribution differences in the target and in five organs-at-risk (OARs) were quantified.Results: In the phantom study, Saito-15it and Hünemohr-15it produced the lowest [Formula: see text]root-mean-square error (0.7%) and DirectDensity^™ the highest error (2.7%). The proton plan evaluated in the Saito-15it and Hünemohr-15it datasets showed the largest relative differences compared to initial CT2RED plan down to -6% of the prescribed dose. Compared to CT2RED, average range differences were calculated: -0.1 ± 0.3 mm for PMI2RED; -0.8 ± 0.4 mm for Hünemohr-15it, and -1.2 ± 0.4 mm for Saito-15it.Conclusion: Given the wide choice of available conversion methods, studies investigating the density accuracy for proton dose calculations are necessary. However, there is still a gap between performing accuracy studies in reference [Formula: see text]phantoms and applying these methods in human CT images. For this treatment case, the PMI2RED method was equivalent to the conventional CT2RED method in terms of dose distribution, CTV coverage and OAR sparing, whereas Hünemohr-15it and Saito-15it presented the largest differences.

Technical Principles of Dual-Energy Cone Beam Computed Tomography and Clinical Applications for Radiation Therapy

PURPOSE

Medical imaging is an indispensable tool in radiotherapy for dose planning, image guidance and treatment monitoring. Cone beam CT (CBCT) is a low dose imaging technique with high spatial resolution capability as a direct by-product of using flat-panel detectors. However, certain issues such as x-ray scatter, beam hardening and other artifacts limit its utility to the verification of patient positioning using image-guided radiotherapy.

METHODS AND MATERIALS

Dual-energy (DE)-CBCT has recently demonstrated promise as an improved tool for tumor visualization in benchtop applications. It has the potential to improve soft-tissue contrast and reduce artifacts caused by beam hardening and metal. In this review, the practical aspects of developing a DE-CBCT based clinical and technical workflow are presented based on existing DE-CBCT literature and concepts adapted from the well-established library of work in DE-CT. Furthermore, the potential applications of DE-CBCT on its future role in radiotherapy are discussed.

RESULTS AND CONCLUSIONS

Based on current literature and an investigation of future applications, there is a clear potential for DE-CBCT technologies to be incorporated into radiotherapy. The applications of DE-CBCT include (but are not limited to): adaptive radiotherapy, brachytherapy, proton therapy, radiomics and theranostics.

Evaluation of raw-data-based and calculated electron density for contrast media with a dual-energy CT technique

Objectives

The aim of the current study is to evaluate the accuracy and the precision of raw-data-based relative electron density (RED_raw) and the calibration-based RED (RED_cal) at a range of low-RED to high-RED for tissue-equivalent phantom materials by comparing them with reference RED (RED_ref) and to present the difference of RED_raw and RED_cal for the contrast medium using dual-energy CT (DECT).

Methods

The RED_raw images were reconstructed by raw-data-based decomposition using DECT. For evaluation of the accuracy of the RED_raw, RED_ref was calculated for the tissue-equivalent phantom materials based on their specified density and elemental composition. The RED_cal images were calculated using three models: Lung-Bone model, Lung-Ti model and Lung-Ti (SEMAR) model which used single-energy metal artifact reduction (SEMAR). The difference between RED_raw and RED_cal was calculated.

Results

In the titanium rod core, the deviations of RED_raw and RED_cal (Lung-Bone model, Lung-Ti model and Lung-Ti model with SEMAR) from RED_ref were 0.45%, 50.8%, 15.4% and 15.0%, respectively. The largest differences between RED_raw and RED_cal (Lung-Bone model, Lung-Ti model and Lung-Ti model with SEMAR) in the contrast medium phantom were 8.2%, -23.7%, and 28.7%, respectively. However, the differences between RED_raw and RED_cal values were within 10% at 20 mg/ml. The standard deviation of the RED_raw was significantly smaller than the RED_cal with three models in the titanium and the materials that had low CT numbers.

Conclusion

The RED_cal values could be affected by beam hardening artifacts and the RED_cal was less accurate than RED_raw for high-Z materials as titanium.

Advances in knowledge

The raw-data-based reconstruction method could reduce the beam hardening artifact compared with image-based reconstruction and increase the accuracy for the RED estimation in high-Z materials, such as titanium and iodinated contrast medium.

Development of a Method to Determine Electron Density and Effective Atomic Number of High Atomic Number Solid Materials Using Dual-Energy Computed Tomography

Aim:

This study aims to develop a method using dual-energy computed tomography (DECT) to determine the effective atomic number and electron density of substances.

Materials and Methods:

Ten chemical substances of pure analytical grade were obtained from various manufacturers. These chemicals were pelletized using a hydraulic press. These pellets were scanned using DECT. A relation was obtained for the pellet's atomic number and electron density with their CT number or Hounsfield unit (HU) values. Calibration coefficients were determined. Five new chemical pellets were scanned, and their effective atomic number and electron densities were determined using the calibration coefficients to test the efficacy of the calibration method.

Results:

The results obtained for effective atomic number and electron density from the HU number of DECT images were within ±5% and ±3%, respectively, of their actual values.

Conclusions:

DECT can be used as an effective tool for determining the effective atomic number and electron density of high atomic number substance.

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

PURPOSE

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.

METHODS

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 150 kVp 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).

RESULTS

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.

CONCLUSIONS

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%.

Murine vs human tissue compositions: implications of using human tissue compositions for photon energy absorption in mice

METHODS:

Dual energy CT (DECT) images of 9 female mice were used to extract the effective atomic number Zeff and the relative electron density ρe for each voxel in the images. To investigate the influence of the tissue compositions on the absorbed radiation dose for a typical kilovoltage photon beam, mass energy-absorption coefficients μen/ρ were calculated for 10 different tissues in each mouse.

RESULTS

Differences between human and murine tissue compositions can lead to errors around 7.5 % for soft tissues and 20.1 % for bone tissues in μen/ρ values for kilovoltage photon beams. When considering the spread within tissues, these errors can increase up to 17.5 % for soft tissues and 53.9 % for bone tissues within only a single standard deviation away from the mean tissue value.

CONCLUSION:

This study illustrates the need for murine reference tissue data. However, assigning only a single mean reference value to an entire tissue can still lead to large errors in dose calculations given the large spread within tissues of μen/ρ values found in this study. Therefore, new methods such as DECT and spectral CT imaging need to be explored, which can be important next steps in improving tissue assignment for dose calculations in small animal radiotherapy.

ADVANCES IN KNOWLEDGE:

This is the first study that investigates the implications of using human tissue compositions for dose calculations in mice for kilovoltage photon beams.

Simulation of photon-counting detectors for conversion of dual-energy-subtracted computed tomography number to electron density

For accurate tissue-inhomogeneity correction in radiotherapy treatment planning, the author previously proposed a conversion of the energy-subtracted computed tomography (CT) number to electron density (ΔHU-ρ_e conversion). The purpose of the present study was to provide a method for investigating the accuracy of a photon-counting detector (PCD) used in the ΔHU-ρ_e conversion by performing dual-energy CT image simulations of a PCD system with two energy bins. To optimize the tube voltage and threshold energy, the image noise and errors in ρ_e calibration were evaluated using three types of virtual phantoms: a 35-cm-diameter pure water phantom, 33-cm-diameter solid water surrogate phantom equipped with 16 inserts, and another solid water surrogate phantom with a 25-cm diameter. The third phantom was used to investigate the effect of the object's size on the ρ_e-calibration accuracy of PCDs. Two different scenarios for the PCD energy response were considered, corresponding to the ideal and realistic cases. In addition, a simple correction method for improving the spectral separation of the dual energies in a realistic PCD was proposed to compensate for its performance loss. In the realistic PCD case, there exists a trade-off between the image noise and ρ_e-calibration errors. Furthermore, the weakest image noise was nearly twice that for the ideal case, and the ρ_e-calibration error did not reach practical levels for any threshold energy. Nevertheless, the proposed correction method is likely to decrease the ρ_e-calibration errors of a realistic PCD to the level of the ideal case, yielding more accurate ρ_e values that are less affected by object size variation.

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

PURPOSE

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.

METHODS

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 equation for 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 140 kVp 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.

RESULTS

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.

CONCLUSION

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.

The effect of different image reconstruction techniques on pre-clinical quantitative imaging and dual-energy CT

OBJECTIVE:

To analyse the effect of different image reconstruction techniques on image quality and dual energy CT (DECT) imaging metrics.

METHODS:

A software platform for pre-clinical cone beam CT X-ray image reconstruction was built using the open-source reconstruction toolkit. Pre-processed projections were reconstructed with filtered back-projection and iterative algorithms, namely Feldkamp, Davis, and Kress (FDK), Iterative FDK, simultaneous algebraic reconstruction technique (SART), simultaneous iterative reconstruction technique and conjugate gradient. Imaging metrics were quantitatively assessed, using a quality assurance phantom, and DECT analysis was performed to determine the influence of each reconstruction technique on the relative electron density (ρ_e) and effective atomic number (Z_eff) values.

RESULTS:

Iterative reconstruction had favourable results for the DECT analysis: a significantly smaller spread for each material in the ρ_e-Z_eff space and lower Z_eff and ρ_e residuals (on average 24 and 25% lower, respectively). In terms of image quality assurance, the techniques FDK, Iterative FDK and SART provided acceptable results. The three reconstruction methods showed similar geometric accuracy, uniformity and CT number results. The technique SART had a contrast-to-noise ratio up to 76% higher for solid water and twice as high for Teflon, but resolution was up to 28% lower when compared to the other two techniques.

CONCLUSIONS:

Advanced image reconstruction can be beneficial, but the benefit is small, and calculation times may be unacceptable with current technology. The use of targeted and downscaled reconstruction grids, larger, yet practicable, pixel sizes and GPU are recommended.

ADVANCES IN KNOWLEDGE:

An iterative CBCT reconstruction platform was build using RTK.

Dual-layer spectral computed tomography: measuring relative electron density

Background

X-ray and particle radiation therapy planning requires accurate estimation of local electron density within the patient body to calculate dose delivery to tumour regions. We evaluate the feasibility and accuracy of electron density measurement using dual-layer computed tomography (DLCT), a recently introduced dual-energy CT technique.

Methods

Two calibration phantoms were scanned with DLCT and virtual monoenergetic images (VMIs) at 50 keV and 200 keV were generated. We investigated two approaches to obtain relative electron densities from these VMIs: to fit an analytic interaction cross-sectional model and to empirically calibrate a conversion function with one of the phantoms. Knowledge of the emitted x-ray spectrum was not required for the presented work.

Results

The results from both methods were highly correlated to the nominal values (R > 0.999). Except for the water and lung inserts, the error was within 1.79% (average 1.53%) for the cross-sectional model and 1.61% (average 0.87%) for the calibrated conversion. Different radiation doses did not have a significant influence on the measurement (p = 0.348, 0.167), suggesting that the methods are reproducible. Further, we applied these methods to routine clinical data.

Conclusions

Our study shows a high validity of electron density estimation based on DLCT, which has potential to improve the procedure and accuracy of measuring electron density in clinical practice.

Improved differentiation between high- and low-grade gliomas by combining dual-energy CT analysis and perfusion CT

The purpose of this study was to investigate the value of the cerebral blood volume (CBV) obtained with perfusion computed tomography (CT) and the electron density (ED) measured by dual-energy CT for differentiating high- from low-grade glioma (HGG, LGG).The CBV and ED were obtained in 9 LGG and 7 HGG patients. The CBV and ED of LGGs and HGGs were compared. Receiver operating characteristic (ROC) curves were generated for CBV, ED, and CBV plus ED. The correlation between CBV, ED, and the MIB-1 labeling index of the tumors was examined. All of these analyses were also performed using relative CBV (rCBV) and ED (rED) (the value of tumors/the value of contralateral white matter).The mean CBV, ED, rCBV, and rED values were significantly higher in HGG than LGG (P < .05). By ROC analysis, the combination of rCBV plus rED as well as CBV plus ED were more accurate than CBV, ED, rCBV, rED alone. There was a significant correlation between ED and MIB-1 (P = .04).ED improved diagnostic accuracy of perfusion CT for differentiating HGG from LGG.

A framework for clinical commissioning of 3D-printed patient support or immobilization devices in photon radiotherapy

PURPOSE

The objective of this work is to outline a framework for dosimetric characterization that will comprehensively detail the clinical commissioning steps for 3D-printed materials applied as patient support or immobilization devices in photon radiotherapy. The complex nature of 3D-printed materials with application to patient-specific configurations requires careful consideration. The framework presented is generalizable to any 3D-printed object where the infill and shell combinations are unknown.

METHODS

A representative cylinder and wedge were used as test objects to characterize devices that may be printed of unknown, patient-specific dimensions. A case study of a 3D-printed CSI immobilization board was presented as an example of an object of known, but adaptable dimensions and proprietary material composition. A series of measurements were performed to characterize the material's kV radiologic properties, MV attenuation measurements and calculations, energy spectrum water equivalency, and surface dose measurements. These measurements complement the recommendations of the AAPM's TG176 to characterize the additional complexity of 3D-printed materials for use in a clinical radiotherapy environment.

RESULTS

The dosimetric characterization of 3D-printed test objects and a case study device informed the development of a step-by-step template that can easily be followed by clinicians to accurately and safely utilize 3D-printed materials as patient-specific support or immobilization devices.

CONCLUSIONS

A series of steps is outlined to provide a formulaic approach to clinically commission 3D-printed materials that may possess varying material composition, infill patterns, and patient-specific dimensions.

Automatic contrast medium extraction system using electron density data with dual-energy CT

OBJECTIVE:

The purpose of the current study is to create a contrast medium extraction method using raw-data-based electron density (rED) and CT number from dual-energy CT (DECT) for automatic delineation of the contrast region.

METHODS:

A CT-ED phantom containing tissue-equivalent inserts and an acrylic phantom with an iodinated contrast medium were scanned by DECT. The contrast medium extraction system was created using Python. The accuracy of the contrast medium extraction was evaluated by measuring the diameter in terms of the full width at half maximum (FWHM) and the ratio of the volume (ROV).

RESULTS:

Mean-2SD CT numbers and the difference of the CT numbers (DCT) of the contrast medium at 0-130 mg ml^-1 contrast medium concentration and the bone materials were more than -33 and -20 HU, respectively. In the correlation of rED and CT number, the gradient with the contrast medium phantom was greater than that with the CT-ED phantom. The accuracy of the contrast medium at 80 kV/135 kV and 100 kV/135 kV tube voltages. The gradient of the CT-ED and contrast medium phantoms were different. The gradient in the CT-ED phantom and the contrast medium was 0.0012 and 0.0003 at 80 kV/135 kV, and 0.0015 and 0.0005 at 100 kV/135 kV tube voltages, respectively. The ratio of the measured to the actual diameter in FWHM and ROV was 0.98-1.00 at 2-130 mg ml^-1. At a tube voltage of 100 kV/135 kV. The ratio of the measured to the actual diameter in ROV was 0.66 and FWHM was 0.90 at 2 mg ml^-1 contrast medium concentration. The ratio of the measured to the actual diameter in FWHM and ROV was 0.98-1.00 at 3-130 mg ml^-1.

CONCLUSION:

We created the contrast medium extraction method with rED and CT number images. The contrast medium extraction method could be used with DECT images at 80 kV/135 kV. The method is expected to only extract images from the region containing the contrast medium.

ADVANCES IN KNOWLEDGE:

We created the contrast medium extraction method using raw-data-based electron density and CT number from DECT and it is expected to only extract information from the region containing the contrast medium.