One advantage of photon-counting CT compared to dual-energy CT is the possibility to perform K-edge imaging, where contrast agents such as iodine can be distinguished from other substances based on spectral characteristics. However, for iodine K-edge imaging in clinical CT, the three-basis decomposition problem is ill-conditioned due to the low K-edge energy of iodine, meaning that the decomposition is highly sensitive to both noise and miscalibrations. This makes robust three-basis decomposition difficult using standard techniques. In this simulation study we evaluate a novel method of performing K-edge imaging, which circumvents the challenging three-basis decomposition step by replacing it with multiple two-basis decompositions followed by a deep convolutional neural network to generate three basis images. Based on the XCAT phantom, we generated 1224 spectral phantom image slices of the neck, with iodine-filled blood vessels and calcifications, and simulated CT imaging in CatSim with a silicon-based detector model without quantum noise, i.e. in the high-dose limit. For each simulated slice, we used maximum likelihood to perform three two-basis decompositions, into PE-PVC, PE-iodine, and PVC-iodine, yielding six basis images in total. We then trained a U-Net to map these six input images to the ground-truth basis images, PE, PVC and iodine. Our results show that the proposed method can reproduce PE, PVC and iodine basis images with high accuracy, in the high-dose limit. This suggests that the proposed three-basis decomposition method may be a feasible way of performing K-edge CT imaging with iodine, with important potential implications for imaging of the carotid arteries.
Photon counting detectors in x-ray computed tomography (CT) improve the decomposition of the CT scans into different materials. This decomposition is however not straightforward to solve, both in terms of computation expense and Photon counting detectors in x-ray computed tomography (CT) are a major technological advancement that provides additional energy information, and improve the decomposition of the CT image into material images. This material decomposition problem is however a non-linear inverse problem that is difficult to solve, both in terms of computation expense and accuracy. The most accepted solution consists in defining an optimization problem based on a maximum likelihood (ML) estimate with Poisson statistics, which is a model-based approach very dependent on the considered forward model and the chosen optimization solver. This may make the material decomposition result noisy and slow to be computed. To incorporate data-driven enhancement to the ML estimate, we propose a deep learning post-processing technique. Our approach is based on convolutional residual blocks that mimic the updates of an iterative optimization process and consider the ML estimate as an input. Therefore, our architecture implicitly considers the physical models of the problem, and in consequence needs less training data and fewer parameters than other standard convolutional networks typically used in medical imaging. We have studied a simulation case of our deep learning post-processing, first on a set of 350 Shepp-Logan -based phantoms, and then in a 600 human numerical phantoms. Our approach has shown denoising enhancement over two different ray-wise decomposition methods: one based on a Newton’s method to solve the ML estimation, and one based on a linear least-squares approximation of the ML expression. We believe this new deep learning post-processing approach is a promising technique to denoise material-decomposed sinograms in photon-counting CT.
Photon-counting detectors are expected to bring a range of improvements to patient imaging with x-ray computed tomography (CT). One is higher spatial resolution. We demonstrate the resolution obtained using a commercial CT scanner where the original energy-integrating detector has been replaced by a single-slice, silicon-based, photon-counting detector. This prototype constitutes the first full-field-of-view silicon-based CT scanner capable of patient scanning. First, the pixel response function and focal spot profile are measured and, combining the two, the system modulation transfer function is calculated. Second, the prototype is used to scan a resolution phantom and a skull phantom. The resolution images are compared to images from a state-of-the-art CT scanner. The comparison shows that for the prototype 19 lp / cm are detectable with the same clarity as 14 lp / cm on the reference scanner at equal dose and reconstruction grid, with more line pairs visible with increasing dose and decreasing image pixel size. The high spatial resolution remains evident in the anatomy of the skull phantom and is comparable to that of other photon-counting CT prototypes present in the literature. We conclude that the deep silicon-based detector used in our study could provide improved spatial resolution in patient imaging without increasing the x-ray dose.
Photon counting detectors are expected to be the next big step in the development of medical computed tomography. Accurate modeling of the behavior of photon counting detectors in the high count rate regime is therefore important for detector performance evaluations and the development of accurate image reconstruction methods. The commonly used ideal nonparalyzable detector model is based on the assumption that photon interactions are converted to pulses with zero extent in time, which is too simplistic to accurately predict the behavior of photon counting detectors in both low and high count rate regimes. In this work we develop a statistical count model for a nonparalyzable detector with finite pulse length and use it to derive the asymptotic mean and variance of the output count distribution using tools from renewal theory. We use the statistical moments of the distribution to construct an estimator of the true number of counts for pileup correction. We confirm the accuracy of the model and evaluate the pileup correction using Monte Carlo simulations. The results show that image quality is preserved for surprisingly high count rates.
Course Instructor
SC1129: Photon Counting CT
This course explains the principles of photon counting detectors for spectral x-ray imaging. Typical technical implementations are described and fundamental differences to energy integrating systems are pointed out. In particular, the issues of high-rate handling and the effect of detector cross talk on energy resolution are described. Requirements on electronics for spectral imaging in computed tomography is also discussed.
A second objective of the course is to describe how energy sensitive counting detectors make use of the energy sampling of the linear attenuation coefficients of the background and target materials for any given imaging task; methods like material basis decomposition and optimal energy weighting will be explained.
The second objective highlights the interesting fact that while the spatial-frequency descriptor of signal-to-noise-ratio transfer (DQE) of a system gives a complete characterization of performance for energy integrating (and pure photon counting) systems, it fails to characterize multibin systems since a complete description of the transfer characteristics requires specification of how the information of each energy bin is handled. The latter is in turn dependent on the imaging case at hand which shows that there is no such thing as an imaging case independent system DQE for photon counting multibin systems. We also suggest how this issue could be resolved.
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