Evaluation of Radiation Dose Effect on Lung Function Using Iodine Maps Derived From Dual-Energy Computed Tomography.

PURPOSE: There is interest in using dual-energy computed tomography (DECT) to evaluate organ function before and after radiation therapy (RT). The purpose of this study (trial identifier: NCT04863027) is to assess longitudinal changes in lung perfusion using iodine maps derived from DECT in patients with lung cancer treated with conventional or stereotactic RT. METHODS AND MATERIALS: For 48 prospectively enrolled patients with lung cancer, a contrast-enhanced DECT using a dual-source CT simulator was acquired pretreatment and at 6 and 12 months posttreatment. Pulmonary functions tests (PFT) were obtained at baseline and at 6 and 12 months posttreatment. Iodine maps were extracted from the DECT images using a previously described 2-material decomposition framework. Longitudinal iodine maps were normalized using a reference region defined as all voxels with perfusion in the top 10% outside of the 5 Gy isodose volume. Normalized functional responses (NFR) were calculated for 3 dose ranges: <5, 5 to 20, and >20 Gy. Mixed model analysis was used to assess the correlation between dose metrics and NFR. Pearson correlation was used to assess if NFRs were correlated with PFT changes. RESULTS: Out of the 48 patients, 21 (44%) were treated with stereotactic body RT and 27 (56%) were treated with conventionally fractionated intensity-modulated RT. Thirty-one out of these 48 patients were ultimately included in data analysis. It was found that NFR is linearly correlated with dose (P < .001) for both groups. The number of months elapsed post-RT was also found to correlate with NFR (P = .029), although this correlation was not observed for the stereotactic body RT subgroup. The NFR was not found to correlate with PFT changes. CONCLUSIONS: DECT-derived iodine maps are a promising method for detailed anatomic evaluation of radiation effect on lung function, including potentially subclinical changes.

Integrated-mode proton radiography with 2D lateral projections.

Integrated-mode proton radiography leading to water equivalent thickness (WET) maps is an avenue of interest for motion management, patient positioning, andin vivorange verification. Radiographs can be obtained using a pencil beam scanning setup with a large 3D monolithic scintillator coupled with optical cameras. Established reconstruction methods either (1) involve a camera at the distal end of the scintillator, or (2) use a lateral view camera as a range telescope. Both approaches lead to limited image quality. The purpose of this work is to propose a third, novel reconstruction framework that exploits the 2D information provided by two lateral view cameras, to improve image quality achievable using lateral views. The three methods are first compared in a simulated Geant4 Monte Carlo framework using an extended cardiac torso (XCAT) phantom and a slanted edge. The proposed method with 2D lateral views is also compared with the range telescope approach using experimental data acquired with a plastic volumetric scintillator. Scanned phantoms include a Las Vegas (contrast), 9 tissue-substitute inserts (WET accuracy), and a paediatric head phantom. Resolution increases from 0.24 (distal) to 0.33 lp mm-1(proposed method) on the simulated slanted edge phantom, and the mean absolute error on WET maps of the XCAT phantom is reduced from 3.4 to 2.7 mm with the same methods. Experimental data from the proposed 2D lateral views indicate a 36% increase in contrast relative to the range telescope method. High WET accuracy is obtained, with a mean absolute error of 0.4 mm over 9 inserts. Results are presented for various pencil beam spacing ranging from 2 to 6 mm. This work illustrates that high quality proton radiographs can be obtained with clinical beam settings and the proposed reconstruction framework with 2D lateral views, with potential applications in adaptive proton therapy.

One-step iterative reconstruction approach based on eigentissue decomposition for spectral photon-counting computed tomography.

Purpose: We propose a one-step tissue characterization method for spectral photon-counting computed tomography (SPCCT) using eigentissue decomposition (ETD), tailored for highly accurate human tissue characterization in radiotherapy. Methods: The approach combines a Poisson likelihood, a spatial prior, and a quantitative prior constraining eigentissue fractions based on expected values for tabulated tissues. There are two regularization parameters: α for the quantitative prior, and ß for the spatial prior. The approach is validated in a realistic simulation environment for SPCCT. The impact of α and ß is evaluated on a virtual phantom. The framework is tested on a virtual patient and compared with two sinogram-based two-step methods [using respectively filtered backprojection (FBP) and an iterative method for the second step] and a post-reconstruction approach with the same quantitative prior. All methods use ETD. Results: Optimal performance with respect to bias or RMSE is achieved with different combinations of α and ß on the cylindrical phantom. Evaluated in tissues of the virtual patient, the one-step framework outperforms two-step and post-reconstruction approaches to quantify proton-stopping power (SPR). The mean absolute bias on the SPR is 0.6% (two-step FBP), 0.6% (two-step iterative), 0.6% (post-reconstruction), and 0.2% (one-step optimized for low bias). Following the same order, the RMSE on the SPR is 13.3%, 2.5%, 3.2%, and 1.5%. Conclusions: Accurate and precise characterization with ETD can be achieved with noisy SPCCT data without the need to rely on post-reconstruction methods. The one-step framework is more accurate and precise than two-step methods for human tissue characterization.

Electron density and effective atomic number estimation in a maximum a posteriori framework for dual-energy computed tomography.

PURPOSE: The stoichiometric calibration method for dual-energy CT (DECT) proposed by Bourque et al. (Phys Med Biol. 59:2059; 2014), which provides estimators of the electron density and the effective atomic number, is adapted to a maximum a posteriori (MAP) framework to increase the model's robustness to noise and biases in CT data, specifically for human tissues. Robust physical parameter estimation from noisy DECT scans is required to maximize the precision of quantities used for radiotherapy treatment planning such as the proton stopping power (SPR). METHODS: Estimation of electron density and effective atomic number is performed by constraining their variation to the natural range of values expected for human tissues, while maximizing attenuation data fidelity. The MAP framework is first compared against the original method using theoretical CT numbers with Gaussian noise. The quantitative accuracy of the MAP framework is then validated experimentally on the Gammex 467 phantom. Then, using two clinical datasets, the advantages of the approach are experimentally evaluated, qualitatively, and quantitatively. RESULTS: The theoretical study shows that the root-mean-square error on the electron density, the effective atomic number and the SPR are, respectively, reduced from 2.3 to 1.5, 5.7 to 3.2 and 2.8 to 1.7% with the adapted framework, when analyzing soft tissues and bone together. The experimental validation study shows that the standard deviation in Gammex inserts can be reduced, on average, by factors of 1.4 (electron density), 2.7 (effective atomic number), and 1.9 (SPR), while the quantitative accuracy of the three physical parameters is preserved, on average. Evaluation on clinical datasets show apparent noise reduction in maps of all estimated physical quantities, and suggests that the MAP framework has increased robustness to beam hardening and photon starvation artifacts. Mean values for the electron density, the effective atomic number, and the SPR averaged in four uniform regions of interest (brain, muscle, adipose, and cranium), respectively, differ by 0.7, 1.8, and 0.9% between both frameworks. The standard deviation in the same regions of interest is also reduced, on average, by factors of 1.8, 6.6, and 3.2 with the MAP framework. Differences in mean value and standard deviations are statistically significant. CONCLUSION: Theoretical and experimental results suggest that the MAP framework produces more accurate and precise estimates of the electron density and SPR. Thus, the present approach limits the propagation of noise in DECT attenuation data to radiotherapy-related parameters maps such as the SPR and the electron density. Using a MAP framework with DECT for radiotherapy treatment planning can help maximizing the precision of dose calculation. The method also provides more precise estimates of the effective atomic number. The MAP methodology is presented in a general way such that it can be adapted to any DECT image-based tissue characterization method.

Quantitative imaging performance of MARS spectral photon-counting CT for radiotherapy.

PURPOSE: To evaluate the quantitative imaging performance of a spectral photon-counting computed tomography (SPCCT) scanner for radiotherapy applications. An experimental comparison of the quantitative performance of a Siemens dual-energy CT (DECT) and a MARS SPCCT scanner is performed to estimate physical properties relevant to radiotherapy of human substitute materials and contrast agent solutions. In human substitute materials, the accuracy of quantities relevant to photon therapy, proton therapy, and Monte-Carlo simulations, such as the electron density, proton stopping power, and elemental composition is evaluated. For contrast agent solutions, the accuracy of the contrast agent concentrations and the virtual non-contrast (VNC) electron density is evaluated. METHODS: Human tissue substitute phantoms (Gammex 467 and 472) as well as diluted solutions of contrast agents (iodine and gadolinium based) are scanned with two commercial systems: a Siemens dual-source CT (SOMATOM Definition Flash, Siemens Healthineers, Forchheim, Germany) and a MARS spectral photon-counting micro-CT (MARS V5.2, MARS Bioimaging Ltd., Christchurch, New Zealand). Material decomposition is performed in a maximum a posteriori framework with an optimized material basis tailored to characterize either human substitute materials or contrast agents in the context of experimental multi-energy CT data. RESULTS: The root-mean-square error (RMSE) of the electron density calculated over all Gammex inserts is reduced from 1.09 to 0.89% when going from DECT to SPCCT. For the proton stopping power, the RMSE is reduced from 1.92 to 0.89%. Elemental mass fractions of hydrogen, carbon, nitrogen, oxygen, and calcium are more accurately estimated with the MARS scanner. The RMSE on the iodine-based contrast agents concentration is reduced from 0.27 to 0.12 mg/mL with SPCCT, and the VNC electron density from 0.40 to 0.22%. CONCLUSION: In the present phantom study, a MARS photon-counting scanner provides superior accuracy compared to a Siemens SOMATOM Definition Flash DECT scanner to quantify physical parameters relevant to radiotherapy. This work experimentally demonstrates the benefits of using more energies to characterize human tissue equivalent materials. This highlights the potential of SPCCT for particle therapy, where more accurate tissue characterization is needed, as well as for Monte-Carlo based planning, which requires accurate elemental mass fractions.

Parametrization of multi-energy CT projection data with eigentissue decomposition.

The purpose of this work is, firstly, to propose an optimized parametrization of the attenuation coefficient to describe human tissues in the context of projection-based material characterization with multi-energy CT. The approach is based on eigentissue decomposition (ETD). Secondly, to evaluate its benefits in terms of accuracy and precision of radiotherapy-related parameters against established parametrizations. The attenuation coefficient is parametrized as a linear combination of virtual materials, eigentissues, obtained by performing principal component analysis on a set of reference tissues in order to optimally represent human tissue composition. Two implementations of ETD are compared with other pre-reconstruction formalisms established for dual-energy and photon-counting CT in a simulation framework. The first implementation uses a single set of eigentissues to describe all human tissues, while the second uses different sets of eigentissues to characterize soft tissues and bones, and includes a post-reconstruction classification step. The simulation framework evaluates the reconstruction accuracy of various radiotherapy-related quantities over a range of 71 human tissues for various noise levels. Compared to conventional parametrizations, the first implementation of ETD reduces the mean error and root-mean-square error (RMSE) in two radiotherapy-related quantities (the proton stopping power and the mass energy absorption coefficient of 21 keV photons from 103Pd seeds used in brachytherapy) for all noise levels and modalities investigated. This illustrates that a decomposition basis selected with principal component analysis is superior to an arbitrary pair of materials to describe human tissues. The mean error on radiotherapy-related parameters can be further reduced with the classification-based approach. In the context of pre-reconstruction material characterization with multi-energy CT, parametrizing the attenuation coefficient with eigentissues provides a more accurate and precise evaluation of human tissues properties for radiotherapy. Accurate quantification can thus be achieved without the need to parametrize tissues using unphysical parameters, such as the energy-dependent effective atomic number.

The potential of photon-counting CT for quantitative contrast-enhanced imaging in radiotherapy.

The aim of this study is to use a simulation environment to evaluate the potential of using photon-counting CT (PCCT) against dual-energy CT (DECT) in the context of quantitative contrast-enhanced CT for radiotherapy. An adaptation of Bayesian eigentissue decomposition by Lalonde et al (2017 Med. Phys. 44 5293-302) that incorporates the estimation of contrast agent fractions and virtual non-contrast (VNC) parameters is proposed, and its performance is validated against conventional maximum likelihood material decomposition methods for single and multiple contrast agents. PCCT and DECT are compared using two simulation frameworks: one including ideal CT numbers with image-based Gaussian noise and another defined as a virtual patient with projection-based Poisson noise and beam hardening artifacts, with both scenarios considering spectral distortion for PCCT. The modalities are compared for their accuracy in estimating four key physical parameters: (1) the contrast agent fraction, as well as VNC parameters relevant to radiotherapy such as the (2) electron density, (3) proton stopping power and (4) photon linear attenuation coefficient. Considering both simulation frameworks, a reduction of root mean square (RMS) errors with PCCT is noted for all physical parameters evaluated, with the exception of the error on the contrast agent fraction being about constant through modalities in the virtual patient. Notably, for the virtual patient, RMS errors on VNC electron density and stopping power are respectively reduced from 2.0% to 1.4% and 2.7% to 1.4% when going from DECT to PCCT with four energy bins. The increase in accuracy is comparable to the differences between contrast-enhanced and non-contrast DECT. This study suggests that in a realistic simulation environment, the overall accuracy of radiotherapy-related parameters can be increased when using PCCT with four energy bins instead of DECT. This confirms the potential of PCCT to provide robust and quantitative tissue parameters for contrast-enhanced CT required in radiotherapy applications.

The impact of dual- and multi-energy CT on proton pencil beam range uncertainties: a Monte Carlo study.

The purpose of this work is to evaluate the impact of single-, dual- and multi-energy CT (SECT, DECT and MECT) on proton range uncertainties in a patient like geometry and a full Monte Carlo environment. A virtual patient is generated from a real patient pelvis CT scan, where known mass densities and elemental compositions are overwritten in each voxel. Simulated CT images for SECT, DECT and MECT are generated for two limiting cases: (1) theoretical and idealistic CT numbers only affected by Gaussian noise (case A, the best scenario) and (2) reconstructed polyenergetic sinograms containing beam hardening, projection-based Poisson noise, and reconstruction artifacts (case B, the worst scenario). Conversion of the simulated SECT images into Monte Carlo inputs is done following the stoichiometric calibration method. For DECT and MECT, the Bayesian eigentissue decomposition method of Lalonde (2017 Med. Phys. 44 5293-302) is used. Pencil beams from seven different angles around the virtual patient are simulated using TOPAS to assess the performance of each method. Percentage depth doses curves (PDD) are compared to ground truth in order to determine the accuracy of range prediction of each imaging modality. For the idealistic images of case A, MECT and DECT slightly outperforms SECT. Root mean square (RMS) errors or 0.78 mm, 0.49 mm and 0.42 mm on R 80 mm, are observed for SECT, DECT and MECT respectively. In case B, PDD calculated in the MECT derived Monte Carlo inputs generally shows the best agreement with ground truth in both shape and position, with RMS errors of 2.03 mm, 1.38 mm and 0.86 mm for SECT, DECT and MECT respectively. Overall, the Bayesian eigentissue decomposition used with DECT systematically predicts proton ranges more accurately than the gold standard SECT-based approach. When CT numbers are severely affected by imaging artifacts, MECT with four energy bins becomes more reliable than both DECT and SECT.

Unsupervised classification of tissues composition for Monte Carlo dose calculation.

The purpose of this study is to investigate the potential of k-means clustering to efficiently reduce the variety of materials needed in Monte Carlo (MC) dose calculation. A numerical phantom with 31 human tissues surrounded by water is created. K-means clustering is used to group the tissues in clusters of constant elemental composition. Four different distance measures are used to perform the clustering technique: Euclidean, Standardized Euclidean, Chi-Squared and Cityblock. Dose distributions are calculated with MC simulations for both low-kV photons and MeV protons using the clustered and reference elemental composition. Comparison between the dose distributions in the clustered and non-clustered phantom are made to assess the impact of clustering with each distance measure. The statistical significance of the differences observed between the four different metrics is determined by comparing the accuracy of energy absorption coefficients (EAC) of low-kV photons and proton stopping powers relative to water (SPR) for repeated clustering procedures. The performance of the proposed approach for a larger number of original materials is evaluated similarly by using a population of 62 000 statistically generated materials grouped into classes defined with supervised and unsupervised classification. In the phantom geometry, the Chi-Squared distance is the one introducing the smallest error on dose distribution and significant differences are observed between the EAC and SPR values predicted by each distance metric. The proposed approach is also shown to be equivalent to a state-of-the-art supervised classification method for proton therapy, but beneficial for low-kV photons applications. In conclusion, k-means clustering successfully reduces the variety of materials needed for accurate MC dose calculation. Based on the performance of four distance measures, we conclude that k-means clustering using the Chi-Squared distance introduces the smallest errors on dose distribution. The method is shown to yield similar or improved accuracy on key physical parameters compared to supervised classification.

Hyperspectral reflected light microscopy of plasmonic Au/Ag alloy nanoparticles incubated as multiplex chromatic biomarkers with cancer cells.

A hyperspectral microscopy system based on a reflected light method for plasmonic nanoparticle (NP) imaging was designed and compared with a conventional darkfield method for spatial localization and spectroscopic identification of single Au, Ag and Au/Ag alloy NPs incubated with fixed human cancer cell preparations. A new synthesis protocol based on co-reduction of Au and Ag salts combined with the seeded growth technique was used for the fabrication of monodispersed alloy NPs with sizes ranging from 30 to 100 nm in diameter. We validated theoretically and experimentally the performance of 60 nm Au, Ag and Au/Ag (50 : 50) NPs as multiplexed biological chromatic markers for biomedical diagnostics and optical biosensing. The advantages of the proposed reflected light microscopy method are presented for NP imaging in a complex and highly diffusing medium such as a cellular environment. The obtained information is essential for the development of a high throughput, selective and efficient strategy for cancer detection and treatment.