BACKGROUND AND PURPOSE: Photon-counting detectors offer the potential for improved image quality for brain CT but have not yet been evaluated in Vivo. The purpose of this study was to compare photon-counting detector CT with conventional energy-integrating detector CT for human brains. MATERIALS AND METHODS: Radiation dose-matched energy-integrating detector and photon-counting detector head CT scans were acquired with standardized protocols (tube voltage/current, 120 kV(peak)/370 mAs) in both an anthropomorphic head phantom and 21 human asymptomatic volunteers (mean age, 58.9 -8.5 years). Photon-counting detector thresholds were 22 and 52 keV (low-energy bin, 22-52 keV; high-energy bin, 52-120 keV). Image noise, gray matter, and white matter signal-To-noise ratios and GM-WM contrast and contrast-To-noise ratios were measured. Image quality was scored by 2 neuroradiologists blinded to the CT detector type. Reproducibility was assessed with the intraclass correlation coefficient. Energy-integrating detector and photon-counting detector CT images were compared using a paired t test and the Wilcoxon signed rank test. RESULTS: Photon-counting detector CT images received higher reader scores forGM-WMdifferentiation with lower image noise (all P- .001). Intrareader and interreader reproducibility was excellent (intraclass correlation coefficient, -0.86 and 0.79, respectively). Quantitative analysis showed 12.8%-20.6% less image noise for photon-counting detector CT. The SNR of photon-counting detector CT was 19.0%-20.0% higher than of energy-integrating detector CT forGMand WM. The contrast-To-noise ratio of photon-counting detector CT was 15.7% higher for GM-WM contrast and 33.3% higher for GM-WM contrast-To-noise ratio. CONCLUSIONS: Photon-counting detector brain CT scans demonstrated greater gray-white matter contrast compared with conventional CT. This was due to both higher soft-Tissue contrast and lower image noise for photon-counting CT.

Background: CT SQUEEZ is a new automated technique to evaluate regional endocardial strain by tracking features on the endocardium from 4D cine CT data. The objective of this study was to measure the range of endocardial regional strain (RSCT) values obtained with CT SQUEEZ in the normal human left ventricle (LV) from standard clinical 4D coronary CTA exams. Methods: RSCT was measured over the heart cycle in 25 humans with normal LV function using cine CT from three vendors. Mean and standard deviation of RSCT values were computed in 16 AHA LV segments to estimate the range of values expected in the normal LV. Results: Curves describing RSCT vs. time were consistent between subjects. There was a slight gradient of decreasing minimum RSCT value (increased shortening) from the base to the apex of the heart. Mean RSCT values at end-systole were: base = −32% ± 1%, mid = −33% ± 1%, apex = −36% ± 1%. The standard deviation of the minimum systolic RSCT in each segment over all subjects was 5%. The average time to reach maximum shortening was 34% of the RR interval. Conclusions: Regional strain (RSCT) can be rapidly obtained from standard gated coronary CCTA protocols using 4DCT SQUEEZ processing. We estimate that 95% of normal LV end-systolic RSCT values will fall between −23% and −43%; therefore, we hypothesize that an RSCT value higher than −23% will indicate a hypokinetic segment in the human heart.

Resting regional wall motion abnormality (RWMA) has significant prognostic value beyond the findings of computed tomography (CT) coronary angiography. Stretch quantification of endocardial engraved zones (SQUEEZ) has been proposed as a measure of regional cardiac function. The purpose of the work reported here was to determine the effect of lowering the radiation dose on the precision of automatic SQUEEZ assessments of RWMA. Chronic myocardial infarction was created by a 2-h occlusion of the left anterior descending coronary artery in 10 swine (heart rates 80–100, ejection fraction 25–57%). CT was performed 5–11 months post infarct using first-pass contrast enhanced segmented cardiac function scans on a 320-detector row scanner at 80 kVp/500 mA. Images were reconstructed at end diastole and end systole with both filtered back projection and using the “standard” adaptive iterative dose reduction (AIDR) algorithm. For each acquisition, 9 lower dose acquisitions were created. End systolic myocardial function maps were calculated using SQUEEZ for all noise levels and contrast-to-noise ratio (CNR) between the left ventricle blood and myocardium was calculated as a measure of image quality. For acquisitions with CNR > 4, SQUEEZ could be estimated with a precision of ± 0.04 (p < 0.001) or 5.7% of its dynamic range. The difference between SQUEEZ values calculated from AIDR and FBP images was not statistically significant. Regional wall motion abnormality can be quantified with good precision from low dose acquisitions, using SQUEEZ, as long as the blood-myocardium CNR stays above 4.

Purpose The purpose of this study was to evaluate image quality of a spectral photon-counting detector (PCD) computed tomography (CT) system for evaluation of major arteries of the head and neck compared with conventional single-energy CT scans using energy-integrating detectors (EIDs). Methods In this institutional review board-approved study, 16 asymptomatic subjects (7 men) provided informed consent and received both PCD and EID contrast-enhanced CT scans of the head and neck (mean age, 58 years; range, 46-75 years). Tube settings were (EID: 120 kVp/160 mA vs PCD: 140 kVp/108 mA) for all volunteers. Quantitative analysis included measurements of mean attenuation, image noise, and contrast-to-noise ratio (CNR). Spectral PCD data were used to reconstruct virtual monoenergetic images and iodine maps. A head phantom was used to validate iodine concentration measurements in PCD images only. Two radiologists blinded to detector type independently scored the image quality of different segments of the arteries, as well as diagnostic acceptability, image noise, and severity of artifacts of the PCD and EID images. Reproducibility was assessed with intraclass correlation coefficient. Linear mixed models that account for within-subject correlation of analyzed arterial segments were used. Linear regression and Bland-Altman analysis with 95% limits of agreement were used to calculate the accuracy of material decomposition. Results Photon-counting detector image quality scores were significantly higher compared with EID image quality scores with lower image noise (P < 0.01) and less image artifacts (P < 0.001). Photon-counting detector image noise was 9.1% lower than EID image noise (8.0 ± 1.3 HU vs 8.8 ± 1.5 HU, respectively, P < 0.001). Arterial segments showed artifacts on EID images due to beam hardening that were not present on PCD images. On PCD images of the head phantom, there was excellent correlation (R2=0.998) between actual and calculated iodine concentrations without significant bias (bias:-0.4 mg/mL [95% limits of agreements:-1.1 to 0.4 mg/mL]). Iodine maps had 20.7% higher CNR compared with nonspectral PCD (65.2 ± 9.0 vs 54.0 ± 4.5, P = 0.01), and virtual monoenergetic image at 70 keV showed similar CNR to nonspectral images (52.6 ± 4.2 vs 54.0 ± 4.5, P = 0.39). Conclusions Photon-counting CT has the potential to improve the image quality of carotid and intracranial CT angiography compared with single-energy EID CT.