A full‐spectral Bayesian reconstruction approach based on the material decomposition model applied in dual‐energy computed tomography

Cai, Chang; Rodet, Thomas; Legoupil, Samuel; Mohammad-Djafari, Ali

doi:10.1118/1.4820478

Cited by 62 publications

(69 citation statements)

References 40 publications

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“…This was done to compare the underlying performance of the different approaches without potentially confounding the results by inclusion of other factors, such as iterative reconstruction. The material decomposition performance shown here most likely can be further improved if iterative reconstruction and material decomposition methods that include noise reduction techniques were to be used . Note that DS technology required image‐based decomposition methods, which were indeed used for this study.…”

Section: Discussionmentioning

confidence: 93%

“…The material decomposition performance shown here most likely can be further improved if iterative reconstruction and material decomposition methods that include noise reduction techniques were to be used. [43][44][45][46][47][48][49][50][51][52][53] Note that DS technology required image-based decomposition methods, which were indeed used for this study.…”

Section: Discussionmentioning

confidence: 99%

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Feasibility of multi‐contrast imaging on dual‐source photon counting detector (PCD) CT: An initial phantom study

et al. 2019

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Purpose Photon‐counting‐detector‐computed tomography (PCD‐CT) allows separation of multiple, simultaneously imaged contrast agents, such as iodine (I), gadolinium (Gd), and bismuth (Bi). However, PCDs suffer from several technical limitations such as charge sharing, K‐edge escape, and pulse pile‐up, which compromise spectral separation of multi‐energy data and degrade multi‐contrast imaging performance. The purpose of this work was to determine the performance of a dual‐source (DS) PCD‐CT relative to a single‐source (SS) PCD‐CT for the separation of simultaneously imaged I, Gd, and Bi contrast agents. Methods Phantom experiments were performed using a research whole‐body PCD‐CT and head/abdomen‐sized phantoms containing vials of different I, Gd, Bi concentrations. To emulate a DS‐PCD‐CT, the phantoms were scanned twice on the SS‐PCD‐CT using different tube potentials for each scan. A tube potential of 80 kV (energy thresholds = 25/50 keV) was used for low‐energy tube, while the high‐energy tube used Sn140 kV (Sn indicates tin filter) and thresholds of 25/90 keV. The same phantoms were scanned also on the SS‐PCD‐CT using the chess acquisition mode. In chess mode, the 4 × 4 subpixels within a macro detector pixel are split into two sets based on a chess‐board pattern. With each subpixel set having two energy thresholds, chess mode allows four energy‐bin data sets, which permits simultaneous multi‐contrast imaging. Because of this design, only 50% area of each detector pixel is configured to receive photons of a pre‐defined threshold, leading to 50% dose utilization efficiency. To compensate for this dose inefficiency, the radiation dose for this scan was doubled compared to DS‐PCD‐CT. A 140 kV tube potential and thresholds = 25/50/75/90 keV were used. These settings were determined based on the K‐edges of Gd, and Bi, and were found to yield good differentiation of I/Gd/Bi based on phantom experiments and other literature. The energy‐bin images obtained from each scan (scan pair) were used to generate I‐, Gd‐, Bi‐specific image via material decomposition. Root‐mean‐square‐error (RMSE) between the known and measured concentrations was calculated for each scenario. A 20‐cm water cylinder phantom was scanned on both systems, which was used for evaluating the magnitude of noise, and noise power spectra (NPS) of I/Gd/Bi‐specific images. Results Phantom results showed that DS‐PCD‐CT reduced noise in material‐specific images for both head and body phantoms compared to SS‐PCD‐CT. The noise level of SS‐PCD was reduced from 2.55 to 0.90 mg/mL (I), 1.97 to 0.78 mg/mL (Gd), and 0.85 to 0.74 mg/mL (Bi) using DS‐PCD. NPS analysis showed that the noise texture of images acquired on both systems is similar. For the body phantom, the RMSE for SS‐PCD‐CT was reduced relative to DS‐PCD‐CT from 10.52 to 2.76 mg/mL (I), 7.90 to 2.01 mg/mL (Gd), and 1.91 to 1.16 mg/mL (Bi). A similar trend was observed for the head phantom: RMSE reduced from 2.59 (SS‐PCD) to 0.72 (DS‐PCD) mg/mL (I), 2.02 to 0.58 mg/mL (Gd), and 0.85 to 0.57 mg/mL (Bi). Co...

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Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Feasibility of multi‐contrast imaging on dual‐source photon counting detector (PCD) CT: An initial phantom study

et al. 2019

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“…Some methods are also performed in iterative reconstruction schemes, which requires access to raw data. 24,25 A different approach with the aim of preserving the quantitative accuracy of tissue parameters is to regularize parameters based on expected values for the tissues of interest. This was proposed for eigentissue decomposition by Lalonde et al, 17 where material fractions are constrained to vary within the expected material fractions of 71 reference tissues tabulated in a reference database (White and Woodard 26,27 ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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“…This method, while relatively simple, is susceptible to beam hardening effects. A third approach is to directly estimate the basis material maps from the energy-window measurements, for which several iterative algorithms have been proposed [11], [12], [13], [14], [15]. The direct inversion approach is challenging due to the ill-conditioned inversion and nonlinear polyenergetic X-ray measurement model.…”

Section: Introductionmentioning

confidence: 99%

A Spectral CT Method to Directly Estimate Basis Material Maps From Experimental Photon-Counting Data

Schmidt

Barber

Sidky

2017

IEEE Trans. Med. Imaging

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The proposed spectral CT method solves the constrained one-step spectral CT reconstruction (cOSSCIR) optimization problem to estimate basis material maps while modeling the nonlinear X-ray detection process and enforcing convex constraints on the basis map images. In order to apply the optimization-based reconstruction approach to experimental data, the presented method empirically estimates the effective energy-window spectra using a calibration procedure. The amplitudes of the estimated spectra were further optimized as part of the reconstruction process to reduce ring artifacts. A validation approach was developed to select constraint parameters. The proposed spectral CT method was evaluated through simulations and experiments with a photon-counting detector. Basis material map images were successfully reconstructed using the presented empirical spectral modeling and cOSSCIR optimization approach. In simulations, the cOSSCIR approach accurately reconstructed the basis map images (< 1% error). In experiments, the proposed method estimated the LDPE region of the basis maps with 0.5% error in the PMMA image and 4% error in the aluminum image. For the Teflon region, the experimental results demonstrated 8% and 31% error in the PMMA and aluminum basis material maps, respectively, compared to −24% and 126% error without estimation of the effective energy window spectra, with residual errors likely due to insufficient modeling of detector effects. The cOSSCIR algorithm estimated the material decomposition angle to within 1.3 degree error, where, for reference, the difference in angle between PMMA and muscle tissue is 2.1 degrees. The joint estimation of spectral-response scaling coefficients and basis material maps was found to reduce ring artifacts in both a phantom and tissue specimen. The presented validation procedure demonstrated feasibility for automated determination of algorithm constraint parameters.

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A full‐spectral Bayesian reconstruction approach based on the material decomposition model applied in dual‐energy computed tomography

Cited by 62 publications

References 40 publications

Feasibility of multi‐contrast imaging on dual‐source photon counting detector (PCD) CT: An initial phantom study

Feasibility of multi‐contrast imaging on dual‐source photon counting detector (PCD) CT: An initial phantom study

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

A Spectral CT Method to Directly Estimate Basis Material Maps From Experimental Photon-Counting Data

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