Ion range estimation by using dual energy computed tomography

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

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

Purpose: To reduce uncertainties in CT data conversion to stopping power ratio (SPR) and tissue composition when using single energy CT (SECT), this study presents a first approach of exploiting dual energy CT (DECT) data for Monte Carlo (MC) based dose calculation in particle therapy. Methods: DECT image data can be converted into an electron density and effective atomic number image. With both tissue parameters elemental weights of 71 tabulated tissues were predicted by dedicated linear fits for each element. The mass density was derived via a single linear fit of the electron density from adipose on. Dose calculations with the MC system TOPAS were performed using monoenergetic pencil beams (protons and carbons) stopped in 12 selected tissues which showed considerable differences in composition predictions from single and dual energy CT. Ranges were compared with predictions from the novel DECT approach and the standard SECT. Results: Predictions of mass density, carbon and oxygen elemental weights profit highly from the additional DECT information. Maximum differences to the true mass density could be reduced from 3.0% (SECT) to 0.9% (DECT) for soft tissue and from 0.7% (SECT) to 0.1% (DECT) for bone tissue. Furthermore, DECT shows a significant improvement also in common tissues: range uncertainties in cartilage were reduced from 1.7% (SECT) to 0.2% (DECT), in yellow marrow from 1.7% (SECT) to 0.5% (DECT), in liver from 0.9% (SECT) to 0.2% (DECT), in brain cerebroisal fluid from 2.2% (SECT) to 0.0% (DECT) and in femur the predicted range deviates by 0.8% (SECT) to 0.0% (DECT) from the true value. Conclusion: DECT can provide more accurate material compositions and mass density as compared to single energy CT. Therewith, DECT information can significantly reduce range uncertainties in particle therapy and can lead to a reduction in currently applied range margins in proton and carbon ion therapy.

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

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

While dual-energy computed tomography (DECT) technology introduces energy-specific information in clinical practice, single-energy CT (SECT) is predominantly used, limiting the number of people who can benefit from DECT. This study proposed a novel method to generate synthetic low-energy virtual monochromatic images at 50keV (sVMI50keV) from SECT images using a transformer-based deep learning model, SwinUNETR. Data were obtained from 85 patients who underwent head and neck radiotherapy. Among these, the model was built using data from 70 patients for whom only DECT images were available. The remaining 15 patients, for whom both DECT and SECT images were available, were used to predict from the actual SECT images. We used the SwinUNETR model to generate sVMI50keV. The image quality was evaluated, and the results were compared with those of the convolutional neural network-based model, Unet. The mean absolute errors from the true VMI50keV were 36.5 ± 4.9 and 33.0 ± 4.4 Hounsfield units for Unet and SwinUNETR, respectively. SwinUNETR yielded smaller errors in tissue attenuation values compared with those of Unet. The contrast changes in sVMI50keV generated by SwinUNETR from SECT were closer to those of DECT-derived VMI50keV than the contrast changes in Unet-generated sVMI50keV. This study demonstrated the potential of transformer-based models for generating synthetic low-energy VMIs from SECT images, thereby improving the image quality of head and neck cancer imaging. It provides a practical and feasible solution to obtain low-energy VMIs from SECT data that can benefit a large number of facilities and patients without access to DECT technology.

Background: The distinction between incidental adrenal lesions is still difficult in diagnostic imaging whereas Dual energy CT has not been thoroughly tested for its ability to diagnose adrenal lesions. This study utilizes dual energy CT to identify between the two most prevalent adrenal neoplasms, adrenal metastasis and adrenal adenomas.Objective:To evaluate the efficacy of non-enhanced single-source dual-energy computed tomography (ssDECT) in differentiating metastases from adenomas in adrenal glands Materials and methods :This is a retrospective study conducted in the department of radio diagnosis of Mysore medical college and research institute after receiving the approval from our institutional ethical committee.A total of fifty six patients (32 men,24 women) with 31 adrenal metastases (AMs) and 32 adrenal adenomas (AAs) underwent a plain dual-energy CT imaging from March 2021 to October 2022 were included. The CT number from the virtual monochromatic spectral (VMS) image sets were measured for the AMs and AAs. The difference of CT numbers between AMs and AAs was statistically compared by P value and the box plot curve. Results: The CT number (medium, range) of metastases (50.47, 29.93 HU at 40 keV and 29.00, 9.36 HU at 140 keV) was significantly higher than that of adenomas (0.76, 33.04 to 13.73, 18.96 HU) at each energy level from 40 to 140 keV (P <.05).Conclusion: Using CT numbers obtained from virtual monochromatic images of single source dual energy CT can be used to differentiate adenomas from adrenal metastasis. The Median CT number of metastases was higher than that of adenomas at 40 Kev and 140 Kev. The median CT number of metastases decreased with increase in incident photon energy in Kev and median CT number of adenomas increased with increase in incident photon energy in Kev. Keywords- Virtual monochromatic spectral (VMS) image,Dual energy computed tomography, adrenal lesions, differential diagnosis.

Purpose:Dual energy CT can predict stopping power ratios (SPR) for ion therapy treatment planning. Several approaches have been proposed recently, however accuracy and practicability in a clinical workflow are unaddressed. The aim of this work is to provide a fair comparison of available approaches in a human‐like phantom to find the optimal method for tissue characterization in a clinical situation.Methods:The SPR determination accuracy is investigated using simulated DECT images. A virtual human‐like phantom is created containing 14 different standard human tissues. SECT (120 kV) and DECT images (100 kV and 140 kV Sn) are simulated using the software ImaSim. The single energy CT (SECT) stoichiometric calibration method and four recently published calibration‐based DECT methods are implemented and used to predict the SPRs from simulated images. The difference between SPR predictions and theoretical SPR are compared pixelwize. Mean, standard deviation and skewness of the SPR difference distributions are used as measures for bias, dispersion and symmetry.Results:The average SPR differences and standard deviations are (0.22 ± 1.27)% for SECT, and A) (−0.26 ± 1.30)%, B) (0.08 ± 1.12)%, C) (0.06 ± 1.15)% and D) (−0.05 ± 1.05)% for the four DECT methods. While SPR prediction using SECT is showing a systematic error on SPR, the DECT methods B, C and D are unbiased. The skewness of the SECT distribution is 0.57%, and A) −0.19%, B) −0.56%, C) −0.29% and D) −0.07% for DECT methods respectively.Conclusion:The here presented DECT methods B, C and D outperform the commonly used SECT stoichiometric calibration. These methods predict SPR accurately without a bias and within ± 1.2% (68th percentile). This indicates that DECT potentially improves accuracy of range predictions in proton therapy. A validation of these findings using clinical CT images of real tissues is necessary.

PurposeTo determine the iodine contrast-to-noise ratio (CNR) for abdominal computed tomography (CT) when using energy domain noise reduction and virtual monoenergetic dual-energy (DE) CT images and to compare the CNR to that attained with single-energy CT at 80, 100, 120, and 140 kV.Materials and MethodsThis HIPAA-compliant study was approved by the institutional review board with waiver of informed consent. A syringe filled with diluted iodine contrast material was placed into 30-, 35-, and 45-cm-wide water phantoms and scanned with a dual-source CT scanner in both DE and single-energy modes with matched scanner output. Virtual monoenergetic images were generated, with energies ranging from 40 to 110 keV in 10-keV steps. A previously developed energy domain noise reduction algorithm was applied to reduce image noise by exploiting information redundancies in the energy domain. Image noise and iodine CNR were calculated. To show the potential clinical benefit of this technique, it was retrospectively applied to a clinical DE CT study of the liver in a 59-year-old male patient by using conventional and iterative reconstruction techniques. Image noise and CNR were compared for virtual monoenergetic images with and without energy domain noise reduction at each virtual monoenergetic energy (in kiloelectron volts) and phantom size by using a paired t test. CNR of virtual monoenergetic images was also compared with that of single-energy images acquired with 80, 100, 120, and 140 kV.ResultsNoise reduction of up to 59% (28.7/65.7) was achieved for DE virtual monoenergetic images by using an energy domain noise reduction technique. For the commercial virtual monoenergetic images, the maximum iodine CNR was achieved at 70 keV and was 18.6, 16.6, and 10.8 for the 30-, 35-, and 45-cm phantoms. After energy domain noise reduction, maximum iodine CNR was achieved at 40 keV and increased to 30.6, 25.4, and 16.5. These CNRs represented improvement of up to 64% (12.0/18.6) with the energy domain noise reduction technique. For single-energy CT at the optimal tube potential, iodine CNR was 29.1 (80 kV), 21.2 (80 kV), and 11.5 (100 kV). For patient images, 39% (24/61) noise reduction and 67% (0.74/1.10) CNR improvement were observed with the energy domain noise reduction technique when compared with standard filtered back-projection images.ConclusionIodine CNR for adult abdominal CT may be maximized with energy domain noise reduction and virtual monoenergetic DE CT.

Organ delineation is crucial to diagnosis and therapy, while it is also labor-intensive and observer-dependent. Dual energy CT (DECT) provides additional image contrast than conventional single energy CT (SECT), which may facilitate automatic organ segmentation. This work aims to develop an automatic multi-organ segmentation approach using deep learning for head-and-neck region on DECT. We proposed a mask scoring regional convolutional neural network (R-CNN) where comprehensive features are firstly learnt from two independent pyramid networks and are then combined via deep attention strategy to highlight the informative ones extracted from both two channels of low and high energy CT. To perform multi-organ segmentation and avoid misclassification, a mask scoring subnetwork was integrated into the Mask R-CNN framework to build the correlation between the class of potential detected organ’s region-of-interest (ROI) and the shape of that organ’s segmentation within that ROI. We evaluated our model on DECT images from 127 head-and-neck cancer patients (66 training, 61 testing) with manual contours of 19 organs as training target and ground truth. For large- and mid-sized organs such as brain and parotid, the proposed method successfully achieved average Dice similarity coefficient (DSC) larger than 0.8. For small-sized organs with very low contrast such as chiasm, cochlea, lens and optic nerves, the DSCs ranged between around 0.5 and 0.8. With the proposed method, using DECT images outperforms using SECT in almost all 19 organs with statistical significance in DSC (p<0.05). Meanwhile, by using the DECT, the proposed method is also significantly superior to a recently developed FCN-based method in most of organs in terms of DSC and the 95th percentile Hausdorff distance. Quantitative results demonstrated the feasibility of the proposed method, the superiority of using DECT to SECT, and the advantage of the proposed R-CNN over FCN on the head-and-neck patient study. The proposed method has the potential to facilitate the current head-and-neck cancer radiation therapy workflow in 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, ) 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. images were converted to mass-densities () to investigate 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 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 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.

Proton radiotherapy treatment plans are currently restricted by the range uncertainties originating from the stopping power ratio (SPR) prediction based on single-energy computed tomography (SECT). Various studies have shown that multi-energy CT (MECT) can reduce the range uncertainties due to medical implant materials and age-related variations in tissue composition. None of these has directly applied the basis material density (MD) images produced by projection-based MECT systems for SPR prediction. To present and evaluate a novel proton SPR prediction method based on MD images from dual-energy CT (DECT), which could reduce the range uncertainties currently associated with proton radiotherapy. A theoretical basis material decomposition into water and iodine material densities was performed for various pediatric and adult human reference tissues, as well as other non-tissue materials, by minimizing the root-mean-square relative attenuation error in the energy interval from 40 to 140keV. A model (here called MD-SPR) mapping predicted MDs to theoretically calculated reference SPRs was created with locally weighted scatterplot smoothing (LOWESS) data-fitting. The goodness of fit of the MD-SPR model was evaluated for the included reference tissues. MD images of two electron density phantoms, combined to form a head- and an abdomen-sized phantom setup, were acquired with a clinical projection-based fast-kV switching DECT scanner. The MD images were compared to the theoretically predicted MDs of the tissue surrogates and other non-tissue materials in the phantoms, as well as used for input to the MD-SPR model for generation of SPR images. The SPR images were subsequently compared to theoretical reference SPRs of the materials in the phantoms, as well as to SPR images from a commercial algorithm (DirectSPR, Siemens Healthineers, Forchheim, Germany) using image-based consecutive scan DECT for the same phantom setups. The predicted SPRs of the tissue surrogates were similar for MD-SPR and DirectSPR, where the adipose and bone tissue surrogates were within 1% difference to the reference SPRs, while other non-adipose soft tissue surrogates (breast, brain, liver, muscle) were all underestimated by between -0.7% and -1.8%. The SPRs of the non-tissue materials (polymethyl methacrylate (PMMA), polyether ether ketone (PEEK), graphite and Teflon) were within 2.8% for MD-SPR images, compared to 6.8% for DirectSPR. The MD-SPR model performed similar compared to other published methods for the human reference tissues. The SPR prediction for tissue surrogates was similar to DirectSPR and showed potential to improve SPR prediction for non-tissue materials.

ID: 114 The impact of dual-energy CT tissue segmentation for low-dose rate prostate brachytherapy Monte Carlo dose calculations

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