Disclosed is a calibration phantom for an x-ray imaging system having an x-ray source and an x-ray detector. The calibration phantom includes a combination of geometric objects of at least three different types and/or compositions including: a first object located in the middle, including a first material; a plurality of second objects arranged around the periphery of the first object, at least a subset of the second objects including a second material different than the first material, wherein the first object is relatively larger than the second objects; a plurality of third objects arranged around the periphery of the first object and/or around the periphery of at least a subset of the second objects, at least a subset of the third objects including a third material different than the first material and the second material, wherein the third objects are relatively smaller than the second objects.

X-ray computed tomography (CT) is a widely used imaging modality that enables visualization of nearly every part of the human body. It is used for diagnosis of disease and injury as well as medical treatment planning. The vast majority of CT scanners in clinical use today have energy-integrating x-ray detectors, which measure the total incident energy in a given measurement.Spectral photon-counting detectors operate by counting individual photons and measuring their energy, and are expected to yield the next major advance in CT, with improvements in spatial resolution, dose efficiency, material differentiation and quantitative imaging capabilities compared to the current state-of-the-art.

In this Thesis, a set of new models and applications for a spectral photon-counting silicon detector developed for CT is investigated. The first part of the Thesis is dedicated to the modeling of spectral photon-counting silicon detectors. A new statistical model for the effects of pulse pileup is presented. Also, the effects on image quality from intra-detector Compton scatter in silicon detectors are investigated via spatio-energetic modeling. In the second part of the Thesis, potential applications for spectral photon-counting detectors are investigated. An experimental study of ex vivo CT imaging of an excised human heart with calcified plaque is presented. It demonstrates the feasibility of unconstrained projection-based three-material decomposition with iodine as a third basis material and explores the potential improvements in spatial resolution and material differentiation that can be achieved with a spectral photon-counting silicon detector compared to a conventional dual-energy CTsystem. Two other applications are investigated with simulations: a method for reconstructing CT images from spectral photon-counting CT data that accurately mimic conventional CT images; and a method for estimating iron concentration in mixtures of liver and adipose tissue when using three basis functions instead of only two to describe the linear attenuation coefficient of tissues in the human body. 

Although the methods presented in this Thesis have been specifically developed for a spectral photon-counting silicon detector, they are also applicable for other types of photon-counting detectors.

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.

An x-ray imaging apparatus includes an x-ray source and detector with multiple detector elements. The source and detector are on a support that rotates around a subject, enabling projections at different view angles. The apparatus operates the x-ray source in switched kVp mode for alternately applying different voltages, including lower and higher voltages, during rotation to enable lower-energy and higher-energy exposures over the projections, providing for lower-energy projections and higher-energy projections. The x-ray detector is a photon-counting multi-bin detector allocating photon counts to multiple energy bins, and the apparatus selects counts from at least a subset of the bins to provide corresponding photon count information for both lower- and higher-energy projections. The apparatus performs material basis decomposition for some of the lower-energy projections and higher-energy projections and/or for some combinations of at least one lower-energy projection and at least one higher-energy projection, based on the corresponding photon count information.

An important question when developing photon-counting detectors for computed tomography is how to select energy thresholds. In this work thresholds are optimized by maximizing signal-difference-to-noise ratio squared (SDNR2) in an optimally weighted image and signal-to-noise ratio squared (SNR2) in a gadolinium basis image in a silicon-strip detector and a cadmium zinc telluride (CZT) detector, factoring in pileup and imperfect energy response based on real-world detector systems. To investigate to what extent one single set of thresholds could be applied in various imaging tasks, the robustness of optimal thresholds with 2 to 8 bins is examined with the variation of phantom thicknesses, target materials and detector configurations. In contrast to previous studies, the optimal threshold locations do not always increase with increasing attenuation if pileup is included. With respect to the tradeoff between higher SDNR2 or SNR2 and less data, setting optimal thresholds for a 30 cm phantom yields robust SDNR2 and setting optimal thresholds for a 50 cm phantom yields robust SNR2 with 6 to 8 bins in the silicon-strip detector. Furthermore, setting optimal thresholds for a 30 cm phantom yields robust SDNR2 or SNR2 with 6 to 8 bins and a pixel size less than or equal to 0.5 x 0.5 mm(2) in the CZT detector.

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 con firm 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.