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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. [ABSTRACT FROM AUTHOR]

Purpose: To propose a new formalism allowing the characterization of human tissues from multienergy computed tomography (MECT) data affected by noise and to evaluate its performance in estimating proton stopping powers (SPR). Methods: A recently published formalism based on principal component analysis called eigentissue decomposition (ETD) is adapted to the context of noise using a Bayesian estimator. The method, named Bayesian ETD, uses the maximum a posteriori fractions of eigentissues in each voxel to determine physical parameters relevant for proton beam dose calculation. Simulated dual-energy computed tomography (DECT) data are used to evaluate the performance of the proposed method to estimate SPR and to compare it to the initially proposed maximum-likelihood ETD and to a state-of-the-art ρe − Z formalism. To test the robustness of each method towards clinical reality, three different levels of noise are implemented, as well as variations in elemental composition and density of reference tissues. The impact of using more than two energy bins to determine SPR is also investigated by simulating MECT data using two to five energy bins. Finally, the impact of using MECT over DECT for range prediction is evaluated using a probabilistic model. Results: For simulated DECT data of reference tissues, the Bayesian ETD approach systematically gives lower root-mean-square (RMS) errors with negligible bias. For a medium level of noise, the RMS errors on SPR are found to be 2.78%, 2.76% and 1.53% for ρe − Z, maximum-likelihood ETD, and Bayesian ETD, respectively. When variations are introduced to the elemental composition and density, all implemented methods give similar performances at low noise. However, for a medium noise level, the proposed Bayesian method outperforms the two others with a RMS error of 1.94%, compared to 2.79% and 2.78% for ρe − Z and maximum-likelihood ETD, respectively. When more than two energy spectra are used, the Bayesian ETD is able to reduce RMS error on SPR using up to five energy bins. In terms of range prediction, Bayesian ETD with four energy bins in realistic conditions reduces proton beam range uncertainties by a factor of up to 1.5 compared to ρe − Z. Conclusion: The Bayesian ETD is shown to be more robust against noise than similar methods and a promising approach to extract SPR from noisy DECT data. In the advent of commercially available multi-energy CT or photon-counting CT scanners, the Bayesian ETD is expected to allow extracting more information and improve the precision of proton therapy beyond DECT. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the reliability of common sinogram-based DECT reconstruction methods for radiotherapy tissue characterization and to evaluate the advantage of combining them with a stoichiometric calibration. Methods: The sinogram-based DECT method defined by Alvarez and Macovski ["Energy-selective reconstructions in x-ray computerized tomography," Phys. Med. Biol. 21, 733-744 (1976)] is adapted to the XCOM photon cross sections database and also generalized to a two-material decomposition method. A theoretical framework is developed using a test phantom containing human tissue compositions for comparing the sinogram-based methods and the calibration-based method, being defined as the application of the stoichiometric calibration technique of Bourque et al. ["A stoichiometric calibration method for dual energy computed tomography," Phys. Med. Biol. 59, 2059-2088 (2014)] on monoenergetic images being generated with a sinogram-based method. Applying a bias correction to the sinogram-based method, its performance in extracting human tissue parameters in the presence of noise as well as by altering the photon energy spectrum is compared to the calibration-based method. Results: In the absence of noise and without spectrum alteration, the calibration-based method is found to have no benefit on the sinogram-based method. However, the calibration-based method is shown to be potentially more reliable than bias-corrected sinogram-based methods in situations comparable to the clinical environment, where noise is present and the photon energy spectra can differ from what is used during image reconstruction. In determining electron density, the performance of all methods is comparable in the presence of noise only. Moreover, combined with heavy spectrum alteration, the mean errors on electron density are found higher in sinogram-based methods in comparison with the calibration-based method, with 1.2% versus 0.2%. In the presence of significant noise, bias-corrected sinogram-based methods yield mean errors on effective atomic number of about 2.5%, as compared to 0.5% for the calibration-based method. When combined with heavy spectrum alteration, bias-corrected sinogram-based methods can lead to error of up to 4% on the effective atomic number versus 1.8% for the calibration-based method. Conclusions: While sinogram-based methods have the advantage of eliminating beam hardening effects, results of this study suggest improvements in the accuracy and reliability of extracting tissue parameters by applying the DECT stoichiometric calibration of Bourque et al. to monoenergetic images being generated with such DECT reconstruction methods. [ABSTRACT FROM AUTHOR]

The accuracy of radiotherapy dose calculation relies crucially on patient composition data. The computed tomography (CT) calibration methods based on the stoichiometric calibration of Schneider et al (1996 Phys. Med. Biol.41 111–24) are the most reliable to determine electron density (ED) with commercial single energy CT scanners. Along with the recent developments in dual energy CT (DECT) commercial scanners, several methods were published to determine ED and the effective atomic number (EAN) for polyenergetic beams without the need for CT calibration curves. This paper intends to show that with a rigorous definition of the EAN, the stoichiometric calibration method can be successfully adapted to DECT with significant accuracy improvements with respect to the literature without the need for spectrum measurements or empirical beam hardening corrections. Using a theoretical framework of ICRP human tissue compositions and the XCOM photon cross sections database, the revised stoichiometric calibration method yields Hounsfield unit (HU) predictions within less than ±1.3 HU of the theoretical HU calculated from XCOM data averaged over the spectra used (e.g., 80 kVp, 100 kVp, 140 kVp and 140/Sn kVp). A fit of mean excitation energy (I-value) data as a function of EAN is provided in order to determine the ion stopping power of human tissues from ED–EAN measurements. Analysis of the calibration phantom measurements with the Siemens SOMATOM Definition Flash dual source CT scanner shows that the present formalism yields mean absolute errors of (0.3 ± 0.4)% and (1.6 ± 2.0)% on ED and EAN, respectively. For ion therapy, the mean absolute errors for calibrated I-values and proton stopping powers (216 MeV) are (4.1 ± 2.7)% and (0.5 ± 0.4)%, respectively. In all clinical situations studied, the uncertainties in ion ranges in water for therapeutic energies are found to be less than 1.3 mm, 0.7 mm and 0.5 mm for protons, helium and carbon ions respectively, using a generic reconstruction algorithm (filtered back projection). With a more advanced method (sinogram affirmed iterative technique), the values become 1.0 mm, 0.5 mm and 0.4 mm for protons, helium and carbon ions, respectively. These results allow one to conclude that the present adaptation of the stoichiometric calibration yields a highly accurate method for characterizing tissue with DECT for ion beam therapy and potentially for photon beam therapy. [ABSTRACT FROM AUTHOR]