Li, Zhoubo

PurposeTo compare algorithms performing material decomposition and classification in dual‐energy CT, it is desirable to know the ground truth of the lesion to be analyzed in real patient data. In this work, we developed and validated a framework to insert digital lesions of arbitrary chemical composition into patient projection data acquired on a dual‐source, dual‐energy CT system.MethodsA model that takes into account beam‐hardening effects was developed to predict the CT number of objects with known chemical composition. The model utilizes information about the x‐ray energy spectra, the patient/phantom attenuation, and the x‐ray detector energy response. The beam‐hardening model was validated on samples of iodine (I) and calcium (Ca) for a second‐generation dual‐source, dual‐energy CT scanner for all tube potentials available and a wide range of patient sizes. The seven most prevalent mineral components of renal stones were modeled and digital stones were created with CT numbers computed for each patient/phantom size and x‐ray energy spectra using the developed beam‐hardening model. Each digital stone was inserted in the dual‐energy projection data of a water phantom scanned on a dual‐source scanner and reconstructed with the routine algorithms in use in our practice. The geometry of the forward projection for dual‐energy data was validated by comparing CT number accuracy and high‐contrast resolution of simulated dual‐energy CT data of the ACR phantom with experimentally acquired data.ResultsThe beam‐hardening model and forward projection method accurately predicted the CT number of I and Ca over a wide range of tube potentials and phantom sizes. The images reconstructed after the insertion of digital kidney stones were consistent with the images reconstructed from the scanner, and the CT number ratios for different kidney stone types were consistent with data in the literature. A sample application of the proposed tool was also demonstrated.ConclusionA framework was developed and validated for the creation of digital objects of known mineral composition, and for inserting the digital objects into projection data from a commercial dual‐source, dual‐energy CT scanner. Among other applications, it will allow a systematic investigation of the impact of scan and reconstruction parameters on kidney stone dual‐energy properties under rigorously controlled conditions.

PurposeTo develop and evaluate an image‐domain noise reduction method for multi‐energy CT (MECT) data.MethodsMulti‐Energy Non‐Local Means (MENLM) is a technique that uses the redundant information in MECT images to achieve noise reduction. In this method, spatio‐spectral features are used to determine the similarity between pixels, making the similarity evaluation more robust to image noise. The performance of this MENLM filter was tested on images acquired on a whole‐body research photon counting CT system. The impact of filtering on image quality was quantitatively evaluated in phantom studies in terms of image noise level (standard deviation of pixel values), noise power spectrum (NPS), in‐plane and cross‐plane spatial resolution, CT number accuracy, material decomposition performance, and subjective low‐contrast spatial resolution using the American College of Radiology (ACR) CT accreditation phantom. Clinical feasibility was assessed by performing MENLM on contrast‐enhanced swine images and unenhanced cadaver head images using clinically relevant doses and dose rates.ResultsThe phantom studies demonstrated that the MENLM filter reduced noise substantially and still preserved the shape and peak frequency of the NPS. With 80% noise reduction, MENLM filtering caused no degradation of high‐contrast spatial resolution, as illustrated by the modulation transfer function (MTF) and slice sensitivity profile (SSP). CT number accuracy was also maintained for all energy channels, demonstrating that energy resolution was not affected by filtering. Material decomposition performance was improved with MENLM filtering. The subjective evaluation using the ACR phantom demonstrated an improvement in low‐contrast performance. MENLM achieved effective noise reduction in both contrast‐enhanced swine images and unenhanced cadaver head images, resulting in improved detection of subtle vascular structures and the differentiation of white/gray matter.ConclusionIn MECT, MENLM achieved around 80% noise reduction and greatly improved material decomposition performance and the detection of subtle anatomical/low‐contrast features while maintaining spatial and energy resolution. MENLM filtering may improve diagnostic or functional analysis accuracy and facilitate radiation dose and contrast media reduction for MECT.

Abstract Photon-counting computed tomography (PCCT) is an emerging imaging technique that enables multi-energy imaging with only a single scan acquisition. To enable multi-energy imaging, the detected photons corresponding to the full x-ray spectrum are divided into several subgroups of bin data that correspond to narrower energy windows. Consequently, noise in each energy bin increases compared to the full-spectrum data. This work proposes an iterative reconstruction algorithm for noise suppression in the narrower energy bins used in PCCT imaging. The algorithm is based on the framework of prior image constrained compressed sensing (PICCS) and is called spectral PICCS; it uses the full-spectrum image reconstructed using conventional filtered back-projection as the prior image. The spectral PICCS algorithm is implemented using a constrained optimization scheme with adaptive iterative step sizes such that only two tuning parameters are required in most cases. The algorithm was first evaluated using computer simulations, and then validated by both physical phantoms and in vivo swine studies using a research PCCT system. Results from both computer-simulation and experimental studies showed substantial image noise reduction in narrow energy bins (43–73%) without sacrificing CT number accuracy or spatial resolution.

Abstract This study evaluated the conventional imaging performance of a research whole-body photon-counting CT system and investigated its feasibility for imaging using clinically realistic levels of x-ray photon flux. This research system was built on the platform of a 2nd generation dual-source CT system: one source coupled to an energy integrating detector (EID) and the other coupled to a photon-counting detector (PCD). Phantom studies were conducted to measure CT number accuracy and uniformity for water, CT number energy dependency for high-Z materials, spatial resolution, noise, and contrast-to-noise ratio. The results from the EID and PCD subsystems were compared. The impact of high photon flux, such as pulse pile-up, was assessed by studying the noise-to-tube-current relationship using a neonate water phantom and high x-ray photon flux. Finally, clinical feasibility of the PCD subsystem was investigated using anthropomorphic phantoms, a cadaveric head, and a whole-body cadaver, which were scanned at dose levels equivalent to or higher than those used clinically. Phantom measurements demonstrated that the PCD subsystem provided comparable image quality to the EID subsystem, except that the PCD subsystem provided slightly better longitudinal spatial resolution and about 25% improvement in contrast-to-noise ratio for iodine. The impact of high photon flux was found to be negligible for the PCD subsystem: only subtle high-flux effects were noticed for tube currents higher than 300 mA in images of the neonate water phantom. Results of the anthropomorphic phantom and cadaver scans demonstrated comparable image quality between the EID and PCD subsystems. There were no noticeable ring, streaking, or cupping/capping artifacts in the PCD images. In addition, the PCD subsystem provided spectral information. Our experiments demonstrated that the research whole-body photon-counting CT system is capable of providing clinical image quality at clinically realistic levels of x-ray photon flux.

Objectives The purpose of this work was to measure and compare the iodine contrast-to-noise ratio (CNR) between a commercial energy-integrating detector (EID) computed tomography (CT) system and a photon-counting detector (PCD) CT scanner capable of human imaging at clinical dose rates, as well as to determine clinical feasibility using human cadavers. Materials and Methods A research dual-source PCD-CT scanner was used, where the “A” tube/detector subsystem used an EID and the “B” tube/detector subsystem used a PCD. Iodine CNR was measured in 4 anthropomorphic phantoms, simulating 4 patient sizes, at 4 tube potential settings. After biospecimen committee approval, PCD scans were performed on a fresh-frozen human head and a whole-body cadaver using clinical dose rates. Scans were repeated using the EID and identical parameters, and qualitative side-by-side comparisons were performed. Results For the same photon fluence, phantom measurements demonstrated a mean increase in CNR of 11%, 23%, 31%, 38% for the PCD system, relative to the EID system, at 80, 100, 120, and 140 kV, respectively. Photon-counting detector CT additionally provided energy-selective imaging, where low- and high-energy images reflected the energy dependence of the iodine signal. Photon-counting detector images of cadaveric anatomy demonstrated decreased beam hardening and calcium blooming in the high-energy bin images and increased contrast in the low-energy bins images relative to the EID images. Threshold-based PCD images were qualitatively deemed equivalent in other aspects. Conclusions The evaluated research PCD-CT system was capable of clinical levels of image quality at clinical dose rates. It further provided improved CNR relative to state-of-the-art EID-CT. The energy-selective bin images provide further opportunity for dual-energy and multienergy analyses.