To further understand the fundamentals of photon-counting spectral CT and provide guidelines on its design and implementation, we propose a singular value decomposition (SVD) based approach to assess the conditioning of spectral channelization and its impact on the performance of spectral imaging under both ideal and realistic detector spectral response. The study runs over two- and three-material decomposition based spectral imaging (material specific imaging). A specially designed phantom that mimics the soft and bony tissues in the head is used to reveal the relationship between the conditioning of spectral channelization and the imaging performance (noise and contrast-to-noise ratio). The study also runs over the cases with up to 50% spectral overlapping and gapping. Under ideal detector spectral response, the condition number of spectral channelization reaches the minimum while no overlapping occurs in spectral channels, and increments with increasing spectral overlapping, and so does in the situation while gapping occurs. The distortion in detector’s spectral response due to charge-sharing and fluorescent escaping inevitably leads to spectral overlapping and thus degrades the conditioning of spectral channelization. With increasing condition number, the noise increases and contrast-to-noise ratio decreases, respectively. The proposed approach, especially its coverage on the situation wherein gapping occurs in spectral channelization, is novel and may provide information for insightful understanding of the fundamentals and guidelines on implementation of spectral imaging in photon-counting and energy-integration CT, and other x-ray imaging modalities such as radiography and tomosynthesis.
With technological advancement in x-ray photon-counting detection, the research and development in photon-counting spectral CT is gaining momentum towards clinical applications. Compared to its energy-integration counterpart, the photon-counting spectral CT is of freedom in energy binning to support three-material (or multiple-material) decomposition for spectral imaging without contrast agent, in which the selection of basis materials and energy binning (spectral channelization), including the cutting-off at low and high energy ends, the number of energy bins, the allocation of bin center and width and the adoption of spectral filtration, plays pivotal roles in determining the imaging performance. Via phantom studies carried out with a photon-counting spectral CT simulation software package, we investigate the potential performance, including contrast, noise and contrast-to-noise ratio (CNR), of three-material decomposition for spectral imaging (mainly virtual monochromatic imaging/analysis) without contrast agents and its variation as functions over base material selection and energy binning, with emphasis on soft tissue differentiation towards clinical and preclinical applications. The preliminary data show that, given identical x-ray dose, the three-material decomposition based virtual monochromatic imaging/analysis can outperform its two-material decomposition based counterpart in the best case CNR and in the scenarios while the energy goes down to approach 18 keV.
Implemented with energy-integration x-ray detector, dual energy spectral CT has been playing increasingly important roles in the clinic for diagnostic imaging. Encouraged by the clinical value added by dual energy spectral CT in oncological, cardiovascular and neurovascular applications, the technological community is investing more resource and effort on photon-counting spectral CT that is anticipated to outperform its energy-integration counterpart in many advanced clinical applications. Based on the preliminary data of phantom and animal studies acquired using a prototype photoncounting spectral CT system, we revisit the underlying physics, mathematics, dimensionality of material space and selection of basis materials for spectral imaging. Focused on three-material decomposition for virtual monochromatic imaging/analysis, we investigate the feasibility and performance of spectral imaging without contrast agents in photoncounting CT, along with an in-depth analysis of the pros and cons of three-material decomposition over two-material decomposition for spectral imaging.
While energy-integration spectral CT with the capability of material decomposition has been providing added value to diagnostic CT imaging in the clinic, photon-counting spectral CT is gaining momentum in research and development, with the potential of overcoming more clinically relevant challenges. In practice, the photon-counting spectral CT provides the opportunity for principal component analysis to effectively extract information from the raw data. However, the principal component analysis in spectral CT may suffer from high noise induced by photon starvation, especially in energy bins at the high energy end. Via phantom and small animal studies, we investigate the feasibility of principal component analysis in photon-counting spectral CT and the benefit that can be offered by de-noising with the Content-Oriented Sparse Representation method.
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