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.
Purpose
Material differentiation has been made possible using dual‐energy computed tomography (DECT), in which the unique, energy‐dependent attenuating characteristics of materials can provide new ...diagnostic information. One promising application is the clinical integration of biodegradable polymers as temporary implantable medical devices impregnated with high‐atomic number (high‐Z) materials. The purpose of this study was to explore the incorporation of high atomic number (high‐Z) contrast materials in a bioresorbable inferior vena cava filter for advanced CT‐based monitoring of its location and differentiating from surrounding materials.
Materials and methods
Imaging optimization and calibration studies were performed using a body phantom. The dual‐energy CT (DECT) ratios for iron, zirconium, barium, gadolinium, ytterbium, tantalum, tungsten, gold, and bismuth were generated for peak kilovoltage combinations of 80/150Sn, 90/150Sn, and 100/150Sn kVp in dual‐source CT via linear regression of the CT numbers at low and high energies. A secondary calibration of the material map to the nominal material concentration was generated to correct for use of materials other than iodine. CT number was calibrated to the material concentration based on single‐energy CT (SECT) with additional filtration (150Sn kVp). These quantification methods were applied to monitoring of biodegradable inferior vena cava filters (IVCFs) made of braided poly(p‐dioxanone) sutures infused with ultrasmall bismuth nanoparticles (BiNPs) implanted in an adult domestic pig.
Results
Qualitative material differentiation was optimal for high‐Z (>73) contrast agents in DECT. However, quantification became nonlinear and inaccurate as the K‐edge of the material increased. Using the high‐energy (150Sn kVp) data component as a SECT scan, the linearity of quantification curves was maintained with lower limits of detection than with DECT. Among the materials tested, bismuth had optimal differentiation from iodine in DECT while maintaining increased contrast in high‐energy SECT for quantification (11.5% error). Coating the IVCF with BiNPs resulted in markedly greater radiopacity (maximum CT number, 2028 HU) than that of an uncoated IVCF (maximum CT number, 127 HU). Using DECT imaging and processing, the BiNP‐IVCF could be clearly differentiated from iodine contrast injected into the inferior vena cava of the pig.
Conclusions
These findings may improve widespread integration of medical devices incorporated with high‐Z materials into the clinic, where technical success, possible complications, and device integrity can be assessed intraoperatively and postoperatively via DECT imaging.
Computed tomography (CT) technology has rapidly evolved since its introduction in the 1970s. It is a highly important diagnostic tool for clinicians as demonstrated by the significant increase in ...utilization over several decades. However, much of the effort to develop and advance CT applications has been focused on improving visual sensitivity and reducing radiation dose. In comparison to these areas, improvements in quantitative CT have lagged behind. While this could be a consequence of the technological limitations of conventional CT, advanced dual‐energy CT (DECT) and photon‐counting detector CT (PCD‐CT) offer new opportunities for quantitation. Routine use of DECT is becoming more widely available and PCD‐CT is rapidly developing. This review covers efforts to address an unmet need for improved quantitative imaging to better characterize disease, identify biomarkers, and evaluate therapeutic response, with an emphasis on multi‐energy CT applications. The review will primarily discuss applications that have utilized quantitative metrics using both conventional and DECT, such as bone mineral density measurement, evaluation of renal lesions, and diagnosis of fatty liver disease. Other topics that will be discussed include efforts to improve quantitative CT volumetry and radiomics. Finally, we will address the use of quantitative CT to enhance image‐guided techniques for surgery, radiotherapy and interventions and provide unique opportunities for development of new contrast agents.
•With DirectSPR, range uncertainties can be reduced below 2% of the particle range.•DirectSPR was for the first time introduced into routine clinical application.•The first relevant reduction of ...CT-based range uncertainty since the 1980’s was achieved.
To quantifiy the range uncertainty in proton treatment planning using dual-energy computed tomography (DECT) for a direct stopping-power prediction (DirectSPR) algorithm and its clinical implementation.
To assess the overall uncertainty in stopping-power ratio (SPR) prediction of a DirectSPR implementation calibrated for different patient geometries, the influencing factors were categorized in imaging, modeling as well as others. The respective SPR uncertainty was quantified for lung, soft tissue and bone and translated into range uncertainty for several tumor types. The amount of healthy tissue spared was quantified for 250 patients treated with DirectSPR and the dosimetric impact was evaluated exemplarily for a representative brain-tumor patient.
For bone, soft tissue and lung, an SPR uncertainty (1σ) of 1.6%, 1.3% and 1.3% was determined for DirectSPR, respectively. This allowed for a reduction of the clinically applied range uncertainty from currently (3.5% + 2 mm) to (1.7% + 2 mm) for brain-tumor and (2.0% + 2 mm) for prostate-cancer patients. The 150 brain-tumor and 100 prostate-cancer patients treated using DirectSPR benefitted from sparing on average 2.6 mm and 4.4 mm of healthy tissue in beam direction, respectively. In the representative patient case, dose reduction in organs at risk close to the target volume was achieved, with a mean dose reduction of up to 16% in the brainstem. Patient-specific DECT-based treatment planning with reduced safety margins was successfully introduced into clinical routine.
A substantial increase in range prediction accuracy in clinical proton treatment planning was achieved by patient-specific DECT-based SPR prediction. For the first time, a relevant imaging-based reduction of range prediction uncertainty on a 2% level has been achieved.
Purpose
To assess the potential of a joint dual‐energy computerized tomography (CT) reconstruction process (statistical image reconstruction method built on a basis vector model (JSIR‐BVM)) ...implemented on a 16‐slice commercial CT scanner to measure high spatial resolution stopping‐power ratio (SPR) maps with uncertainties of less than 1%.
Methods
JSIR‐BVM was used to reconstruct images of effective electron density and mean excitation energy from dual‐energy CT (DECT) sinograms for 10 high‐purity samples of known density and atomic composition inserted into head and body phantoms. The measured DECT data consisted of 90 and 140 kVp axial sinograms serially acquired on a Philips Brilliance Big Bore CT scanner without beam‐hardening corrections. The corresponding SPRs were subsequently measured directly via ion chamber measurements on a MEVION S250 superconducting synchrocyclotron and evaluated theoretically from the known sample compositions and densities. Deviations of JSIR‐BVM SPR values from their theoretically calculated and directly measured ground‐truth values were evaluated for our JSIR‐BVM method and our implementation of the Hünemohr–Saito (H‐S) DECT image‐domain decomposition technique for SPR imaging. A thorough uncertainty analysis was then performed for five different scenarios (comparison of JSIR‐BVM stopping‐power ratio/stopping power (SPR/SP) to International Commission on Radiation Measurements and Units benchmarks; comparison of JSIR‐BVM SPR to measured benchmarks; and uncertainties in JSIR‐BVM SPR/SP maps for patients of unknown composition) per the Joint Committee for Guides in Metrology and the Guide to Expression of Uncertainty in Measurement, including the impact of uncertainties in measured photon spectra, sample composition and density, photon cross section and I‐value models, and random measurement uncertainty. Estimated SPR uncertainty for three main tissue groups in patients of unknown composition and the weighted proportion of each tissue type for three proton treatment sites were then used to derive a composite range uncertainty for our method.
Results
Mean JSIR‐BVM SPR estimates deviated by less than 1% from their theoretical and directly measured ground‐truth values for most inserts and phantom geometries except for high‐density Delrin and Teflon samples with SPR error relative to proton measurements of 1.1% and −1.0% (head phantom) and 1.1% and −1.1% (body phantom). The overall root‐mean‐square (RMS) deviations over all samples were 0.39% and 0.52% (head phantom) and 0.43% and 0.57% (body phantom) relative to theoretical and directly measured ground‐truth SPRs, respectively. The corresponding RMS (maximum) errors for the image‐domain decomposition method were 2.68% and 2.73% (4.68% and 4.99%) for the head phantom and 0.71% and 0.87% (1.37% and 1.66%) for the body phantom. Compared to H‐S SPR maps, JSIR‐BVM yielded 30% sharper and twofold sharper images for soft tissues and bone‐like surrogates, respectively, while reducing noise by factors of 6 and 3, respectively. The uncertainty (coverage factor k = 1) of the DECT‐to‐benchmark values comparison ranged from 0.5% to 1.5% and is dominated by scanning‐beam photon‐spectra uncertainties. An analysis of the SPR uncertainty for patients of unknown composition showed a JSIR‐BVM uncertainty of 0.65%, 1.21%, and 0.77% for soft‐, lung‐, and bony‐tissue groups which led to a composite range uncertainty of 0.6–0.9%.
Conclusions
Observed JSIR‐BVM SPR estimation errors were all less than 50% of the estimated k = 1 total uncertainty of our benchmarking experiment, demonstrating that JSIR‐BVM high spatial resolution, low‐noise SPR mapping is feasible and is robust to variations in the geometry of the scanned object. In contrast, the much larger H‐S SPR estimation errors are dominated by imaging noise and residual beam‐hardening artifacts. While the uncertainties characteristic of our current JSIR‐BVM implementation can be as large as 1.5%, achieving < 1% total uncertainty is feasible by improving the accuracy of scanner‐specific scatter‐profile and photon‐spectrum estimates. With its robustness to beam‐hardening artifact, image noise, and variations in phantom size and geometry, JSIR‐BVM has the potential to achieve high spatial‐resolution SPR mapping with subpercentage accuracy and estimated uncertainty in the clinical setting.
Background
Thermochemical ablation (TCA) is a minimally invasive therapy under development for hepatocellular carcinoma. TCA simultaneously delivers an acid (acetic acid, AcOH) and base (sodium ...hydroxide, NaOH) directly into the tumor, where the acid/base chemical reaction produces an exotherm that induces local ablation. However, AcOH and NaOH are not radiopaque, making monitoring TCA delivery difficult.
Purpose
We address the issue of image guidance for TCA by utilizing cesium hydroxide (CsOH) as a novel theranostic component of TCA that is detectable and quantifiable with dual‐energy CT (DECT).
Materials and methods
To quantify the minimum concentration of CsOH that can be positively identified by DECT, the limit of detection (LOD) was established in an elliptical phantom (Multi‐Energy CT Quality Assurance Phantom, Kyoto Kagaku, Kyoto, Japan) with two DECT technologies: a dual‐source system (SOMATOM Force, Siemens Healthineers, Forchheim, Germany) and a split‐filter, single‐source system (SOMATOM Edge, Siemens Healthineers). The dual‐energy ratio (DER) and LOD of CsOH were determined for each system. Cesium concentration quantification accuracy was evaluated in a gelatin phantom before quantitative mapping was performed in ex vivo models.
Results
On the dual‐source system, the DER and LOD were 2.94 and 1.36‐mM CsOH, respectively. For the split‐filter system, the DER and LOD were 1.41‐ and 6.11‐mM CsOH, respectively. The signal on cesium maps in phantoms tracked linearly with concentration (R2 = 0.99) on both systems with an RMSE of 2.56 and 6.72 on the dual‐source and split‐filter system, respectively. In ex vivo models, CsOH was detected following delivery of TCA at all concentrations.
Conclusions
DECT can be used to detect and quantify the concentration of cesium in phantom and ex vivo tissue models. When incorporated in TCA, CsOH performs as a theranostic agent for quantitative DECT image‐guidance.
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.
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.
Purpose
The purpose of this study was to assess the performance of a novel dual‐energy CT (DECT) approach for proton stopping power ratio (SPR) mapping that integrates image reconstruction and ...material characterization using a joint statistical image reconstruction (JSIR) method based on a linear basis vector model (BVM). A systematic comparison between the JSIR‐BVM method and previously described DECT image‐ and sinogram‐domain decomposition approaches is also carried out on synthetic data.
Methods
The JSIR‐BVM method was implemented to estimate the electron densities and mean excitation energies (I‐values) required by the Bethe equation for SPR mapping. In addition, image‐ and sinogram‐domain DECT methods based on three available SPR models including BVM were implemented for comparison. The intrinsic SPR modeling accuracy of the three models was first validated. Synthetic DECT transmission sinograms of two 330 mm diameter phantoms each containing 17 soft and bony tissues (for a total of 34) of known composition were then generated with spectra of 90 and 140 kVp. The estimation accuracy of the reconstructed SPR images were evaluated for the seven investigated methods. The impact of phantom size and insert location on SPR estimation accuracy was also investigated.
Results
All three selected DECT‐SPR models predict the SPR of all tissue types with less than 0.2% RMS errors under idealized conditions with no reconstruction uncertainties. When applied to synthetic sinograms, the JSIR‐BVM method achieves the best performance with mean and RMS‐average errors of less than 0.05% and 0.3%, respectively, for all noise levels, while the image‐ and sinogram‐domain decomposition methods show increasing mean and RMS‐average errors with increasing noise level. The JSIR‐BVM method also reduces statistical SPR variation by sixfold compared to other methods. A 25% phantom diameter change causes up to 4% SPR differences for the image‐domain decomposition approach, while the JSIR‐BVM method and sinogram‐domain decomposition methods are insensitive to size change.
Conclusion
Among all the investigated methods, the JSIR‐BVM method achieves the best performance for SPR estimation in our simulation phantom study. This novel method is robust with respect to sinogram noise and residual beam‐hardening effects, yielding SPR estimation errors comparable to intrinsic BVM modeling error. In contrast, the achievable SPR estimation accuracy of the image‐ and sinogram‐domain decomposition methods is dominated by the CT image intensity uncertainties introduced by the reconstruction and decomposition processes.
In equine medicine, assisted bone regeneration, including use of biomaterial substitutes like hydroxyapatite (HAP), is crucial for addressing bone defects. To follow-up on the outcome of HAP-based ...bone defect treatment, the advancement in quantified diagnostic imaging protocols is needed. This study aimed to quantify and compare the radiological properties of the HAP graft and natural equine bone using Magnetic Resonance (MR) and Computed Tomography (CT), both Single (SECT) and Dual Energy (DECT). SECT and DECT, allow for the differentiation of three HAP grain sizes, by progressive increase in relative density (RD). SECT, DECT, and MR enable the differentiation between natural cortical bone and synthetic HAP graft by augmentation in Effective Z and material density (MD) in HAP/Water, Calcium/Water, and Water/Calcium reconstructions, alongside the reduction in T2 relaxation time. The proposed quantification provided valuable radiological insights into the composition of HAP grafts, which may be useful in follow-up bone defect treatment.
In equine medicine, there is a need for advanced strategies for assisted bone regeneration, particularly for treating bone diseases like subchondral cystic lesions (SCLs). One strategy involves filling bone defects with biomaterial substitutes like hydroxyapatite (HAP). This study aimed to quantify and compare the radiological properties of HAP grafts and natural equine bone using Single Energy Computed Tomography (SECT), Dual Energy Computed Tomography (DECT), and Magnetic Resonance (MR). Three HAP types with different grain sizes were combined with equine blood or bone marrow and imaged in a two-stage experiment. The first stage investigated grain size-dependent changes, while the second stage tested the similarity to natural bone. SECT (A), DECT (B), and MR (C) images were then quantified. This protocol provided valuable insights into the composition of HAP grafts, which may be useful in follow-up bone defect treatment. Display omitted
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