Yu, Lifeng

PurposeThe vast majority of body CT exams are performed with automatic exposure control (AEC), which adapts the mean tube current to the patient size and modulates the tube current either angularly, longitudinally or both. However, most radiation dose estimation tools are based on fixed tube current scans. Accurate estimates of patient dose from AEC scans require knowledge of the tube current values, which is usually unavailable. The purpose of this work was to develop and validate methods to accurately estimate the tube current values prescribed by one manufacturer's AEC system to enable accurate estimates of patient dose.MethodsMethods were developed that took into account available patient attenuation information, user selected image quality reference parameters and x‐ray system limits to estimate tube current values for patient scans. Methods consistent with AAPM Report 220 were developed that used patient attenuation data that were: (a) supplied by the manufacturer in the CT localizer radiograph and (b) based on a simulated CT localizer radiograph derived from image data. For comparison, actual tube current values were extracted from the projection data of each patient. Validation of each approach was based on data collected from 40 pediatric and adult patients who received clinically indicated chest (n = 20) and abdomen/pelvis (n = 20) scans on a 64 slice multidetector row CT (Sensation 64, Siemens Healthcare, Forchheim, Germany). For each patient dataset, the following were collected with Institutional Review Board (IRB) approval: (a) projection data containing actual tube current values at each projection view, (b) CT localizer radiograph (topogram) and (c) reconstructed image data. Tube current values were estimated based on the actual topogram (actual‐topo) as well as the simulated topogram based on image data (sim‐topo). Each of these was compared to the actual tube current values from the patient scan. In addition, to assess the accuracy of each method in estimating patient organ doses, Monte Carlo simulations were performed by creating voxelized models of each patient, identifying key organs and incorporating tube current values into the simulations to estimate dose to the lungs and breasts (females only) for chest scans and the liver, kidney, and spleen for abdomen/pelvis scans. Organ doses from simulations using the actual tube current values were compared to those using each of the estimated tube current values (actual‐topo and sim‐topo).ResultsWhen compared to the actual tube current values, the average error for tube current values estimated from the actual topogram (actual‐topo) and simulated topogram (sim‐topo) was 3.9% and 5.8% respectively. For Monte Carlo simulations of chest CT exams using the actual tube current values and estimated tube current values (based on the actual‐topo and sim‐topo methods), the average differences for lung and breast doses ranged from 3.4% to 6.6%. For abdomen/pelvis exams, the average differences for liver, kidney, and spleen doses ranged from 4.2% to 5.3%.ConclusionsStrong agreement between organ doses estimated using actual and estimated tube current values provides validation of both methods for estimating tube current values based on data provided in the topogram or simulated from image data.

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 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.