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

Schmidt, Taly Gilat; Barber, Rina Foygel; Sidky, Emil Y.

doi:10.1109/tmi.2017.2696338

Cited by 50 publications

(75 citation statements)

References 29 publications

(57 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, researchers are actively looking into including some of the still un-modelled physical effects and enhancing the accuracy of the incident spectrum and detector response function [ 36 , 37 ]. Additionally, mitigating the noise would probably require using regularised iterative methods, either during the material decomposition process [ 38 ], during the reconstruction process [ 39 ] or in a one-step algorithm that would embed both processes [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

Multicolour imaging with spectral photon-counting CT: a phantom study

Si‐Mohamed¹,

Bar-Ness²,

Sigovan³

et al. 2018

Eur Radiol Exp

View full text Add to dashboard Cite

BackgroundTo evaluate the feasibility of multicolour quantitative imaging with spectral photon-counting computed tomography (SPCCT) of different mixed contrast agents.MethodsPhantoms containing eleven tubes with mixtures of varying proportions of two contrast agents (i.e. two selected from gadolinium, iodine or gold nanoparticles) were prepared so that the attenuation of each tube was about 280 HU. Scans were acquired at 120 kVp and 100 mAs using a five-bin preclinical SPCCT prototype, generating conventional, water, iodine, gadolinium and gold images. The correlation between prepared and measured concentrations was assessed using linear regression. The cross-contamination was measured for each material as the root mean square error (RMSE) of its concentration in the other material images, where no signal was expected. The contrast-to-noise ratio (CNR) relative to a phosphate buffered saline tube was calculated for each contrast agent.ResultsThe solutions had similar attenuations (279 ± 10 HU, mean ± standard deviation) and could not be differentiated on conventional images. However, a distinction was observed in the material images within the same samples, and the measured and prepared concentrations were strongly correlated (R2 ≥ 0.97, 0.81 ≤ slope ≤ 0.95, -0.68 ≤ offset ≤ 0.89 mg/mL). Cross-contamination in the iodine images for the mixture of gold and gadolinium contrast agents (RMSE = 0.34 mg/mL) was observed. CNR for 1 mg/mL of contrast agent was better for the mixture of iodine and gadolinium (CNRiodine = 3.20, CNRgadolinium = 2.80) than gold and gadolinium (CNRgadolinium = 1.67, CNRgold = 1.37).ConclusionsSPCCT enables multicolour quantitative imaging. As a result, it should be possible to perform imaging of multiple uptake phases of a given tissue/organ within a single scan by injecting different contrast agents sequentially.

show abstract

Section: Discussionmentioning

confidence: 99%

Multicolour imaging with spectral photon-counting CT: a phantom study

Si‐Mohamed¹,

Bar-Ness²,

Sigovan³

et al. 2018

Eur Radiol Exp

View full text Add to dashboard Cite

show abstract

“…Interest in estimating the x-ray spectrum of a CT system is recently growing due to the development of spectral CT with a photon-counting detector. [4][5][6] One common approach for reconstructing the x-ray energy spectrum is based on transmission measurements acquired by a CT scanner through a calibration phantom of known thicknesses and materials. This method formulates the measurement process of x-ray photons into a linear system of equations, and after acquiring calibration transmission measurements, the x-ray spectrum is recovered by inverting the linear system.…”

Section: Introductionmentioning

confidence: 99%

Estimating the spectrum in computed tomography via Kullback–Leibler divergence constrained optimization

Sidky

Barber

et al. 2018

Medical Physics

Self Cite

View full text Add to dashboard Cite

Purpose: We study the problem of spectrum estimation from transmission data of a known phantom. The goal is to reconstruct an x-ray spectrum that can accurately model the x-ray transmission curves and reflects a realistic shape of the typical energy spectra of the CT system. Methods: Spectrum estimation is posed as an optimization problem with x-ray spectrum as unknown variables, and a Kullback-Leibler (KL) divergence constraint is employed to incorporate prior knowledge of the spectrum and enhance numerical stability of the estimation process. The formulated constrained optimization problem is convex and can be solved efficiently by use of the exponentiated-gradient (EG) algorithm. We demonstrate the effectiveness of the proposed approach on the simulated and experimental data. The comparison to the expectation-maximization (EM) method is also discussed. Results: In simulations, the proposed algorithm is seen to yield x-ray spectra that closely match the ground truth and represent the attenuation process of x-ray photons in materials, both included and not included in the estimation process. In experiments, the calculated transmission curve is in good agreement with the measured transmission curve, and the estimated spectra exhibits physically realistic looking shapes. The results further show the comparable performance between the proposed optimization-based approach and EM. Conclusions: Our formulation of a constrained optimization provides an interpretable and flexible framework for spectrum estimation. Moreover, a KL-divergence constraint can include a prior spectrum and appears to capture important features of x-ray spectrum, allowing accurate and robust estimation of x-ray spectrum in CT imaging.

show abstract

“…A software solution is a model‐based compensation algorithm . Such algorithms can address the bias using a charge sharing model; however, they cannot eliminate the noise added by charge sharing, because it is impossible to estimate (and subtract) counts of each random noise realization.…”

Section: Introductionmentioning

confidence: 99%

Multi‐energy inter‐pixel coincidence counters for charge sharing correction and compensation in photon counting detectors

Taguchi

2020

Medical Physics

View full text Add to dashboard Cite

Purpose: Smaller pixel sizes of x-ray photon counting detectors (PCDs) are advantageous for count rate capabilities but disadvantageous for charge sharing. With charge sharing, the energy of an x-ray photon may be split and one photon may produce two or more counts at adjacent pixels, both at lower energies than the incident energy. This "double-counting" increases noise variance and degrades the spectral response. Overall, it has a significantly negative impact on the performance of PCD-based computed tomography (CT). Charge sharing is induced by the detection physics and occurs regardless of count rates; thus, it is impossible to avoid. We propose in this paper a method that has a potential to address both noise and bias added by charge sharing. Methods: We propose applying a multi-energy inter-pixel coincidence counter (MEICC) technique, which uses energy-dependent coincidence counters, keeps the book of charge sharing events during data acquisition, and provides the exact number of charge sharing occurrences, which can be used to either correct or compensate for them after the acquisition is completed. MEICC does not interfere with the primary counting process; therefore, PCDs with MEICC will remain as fast as those without MEICC. MEICC can be implemented using current electronics technology because its inter-pixel coincidence counters used to handle digital data are rather simple. We evaluated Cram er-Rao lower bound (CRLB) of PCDs with and without MEICC using a Monte Carlo simulation. Results: When the number of energy windows was four or larger and eight neighboring pixels were used, the CRLBs of 225-µm PCD with MEICC normalized by those of the current PCD with the same number of windows were 0.361-0.383 for water density images of two basis functions, which was only 5.7-16.4% worse than those of a PCD without charge sharing (which were at 0.329-0.358). In contrast, the normalized CRLBs of the PCD with one coincidence counter were 0.466-0.499, which were 37.3-45.6% worse than the PCD without charge sharing. The use of eight neighboring pixels provided~10% better CRLB values than four neighboring pixels for MEICC. With four energy windows, decreasing the number of coincidence counters from 16 to 9 only slightly increased the CRLB from 0.255 to 0.269 (which corresponded to as little as a 5.5% change). The normalized CRLBs of MEICC for K-edge imaging (gold) were 0.295-0.426, while those of the one coincidence counter were 0.926-0.959 and the ideal PCDs were 0.126-0.146. Conclusions:The proposed MEICC provides spectral information that can be used to address charge sharing problems in PCDs and is expected to satisfy the requirements for clinical x-ray CT. MEICC is very effective, especially for K-edge imaging, which requires accurate spectral information.

show abstract

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

Cited by 50 publications

References 29 publications

Multicolour imaging with spectral photon-counting CT: a phantom study

Multicolour imaging with spectral photon-counting CT: a phantom study

Estimating the spectrum in computed tomography via Kullback–Leibler divergence constrained optimization

Multi‐energy inter‐pixel coincidence counters for charge sharing correction and compensation in photon counting detectors

Contact Info

Product

Resources

About