The correction of zero-counts is essential to enable high-quality and accurate low-dose photon counting detector CT (PCD-CT) imaging. Prior to logarithmic normalization, it is necessary to correct for zero-counts in the raw output of PCDs to prevent division-by-zero in the conventional CT reconstruction pipeline. The probability of registering a count of zero for a given detector element is significantly higher for PCDs compared with conventional detectors due to the increased spatial resolution of PCDs and the allocation of counts to multiple energy bins for spectral CT imaging applications. This concern becomes further amplified when considering low-dose PCD-CT applications, large patient imaging, and the presence of metallic structures. However, current methods to correct for zero-counts introduce CT number bias, degrade spatial resolution, or violate the conditional independence assumption. The objective of this work is to develop a zero-correction framework that effectively addresses zero-counts while minimizing bias, preserving conditional independence, and maintaining spatial resolution. Experimental validation studies are performed on a benchtop PCD-CT system to demonstrate the efficacy and generalizability of the proposed correction framework. The results of these studies indicate that the proposed zero-count correction scheme can minimize bias and preserve spatial resolution for both single-energy bin and dual-energy bin PCD-CT imaging acquisitions at low doses.
C-arm x-ray systems equipped with flat panel detectors (FPD) lack spectral and ultra-high-resolution (UHR) capabilities desired by physicians for image guided interventions (IGIs), for example to discriminate between and/or quantify different materials such as iodine and calcium, or in the visualization of very fine structures or devices used in interventional procedures. Photon counting detectors (PCDs) can introduce these capabilities to the interventional suite: In this work, we propose a new dagger-shaped PCD design tailored for IGIs to upgrade the imaging capabilities in the C-arm interventional system while preserving the functionality of the existing FPD and reducing the system cost compared to completely replacing the FPD with a large-area PCD. The design consists of two modules integrated together: One is a long-strip shape for narrow-beam spectral and UHR CT with full axial coverage, and one is rectangle-shaped for volume- and region-of-interest 2D and 3D spectral and UHR imaging. As a proof of concept, prototypes of each module were used to perform phantom and in vivo animal experiments. Results show the potential of the proposed design in discriminating between and quantifying iodine and calcium by leveraging the spectral information provided by PCDs. UHR 2D and 3D PCD images show the improved capabilities of the dagger PCD in delineating small blood vessels with improved contrast-to-noise ratios, as well as resolving fine structures such as stents commonly used in IGIs.
The purpose of this work is develop a novel multi-contrast chest x-ray radiography (MC-CXR) imaging system to enable the simultaneous generation of three mutually complementary x-ray contrast mechanisms to enhance the diagnostic performance of CXR for respiratory diseases. The developed grating-based MC-CXR system employs a scanning beam image acquisition scheme in which the patient table is translated at a speed of up to 9 cm/s. The system is capable of accomplishing MC-CXR imaging of an anthropomorphic chest phantom in under 4 seconds, with an air kerma and effective dose that are well below that of a conventional CXR exam.
Gout is the most prevalent inflammatory arthritis found in men. A prompt diagnosis and early treatment of gout are crucial in preventing eventual functional impairment and reduction in comorbidities. In this work, the quantitative material information provided by a multi-contrast x-ray (MCXR) imaging acquisition is leveraged to develop a rapid, non-invasive, and low dose diagnostic method for gout detection and gout-pseudogout differentiation. This work establishes a theoretical foundation to demonstrate how a single-kV MCXR acquisition is capable of differentiating gout from pseudogout via a projection domain two-material decomposition. Experimental results from a benchtop MCXR system are presented. The imaging performance of the proposed MCXR technique is compared to dual-energy radiography to further validate the method.
CT imaging is one of the primary diagnostic tools utilized in modern radiology departments. Current stateof- the-art spectral CT imaging systems have been implemented using advanced x-ray source and/or detector technologies that have enabled image objects to be rapidly scanned using two distinct x-ray spectra (i.e., different effective beam energies). In this paper, we study the possibility to extract the encoded spectral information from the measured data when a single polychromatic x-ray spectrum is used to acquire data using an energy integration detector. Based upon our physical analysis, a physics-based deep neural network architecture, termed the Deep Spectral Imaging Network, was trained to demonstrate the feasibility of achieving spectral CT imaging using an energy integration detector and a single-kV acquisition.
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