To develop and evaluate a low-volume contrast media protocol for thoracoabdominal CT angiography (CTA) with photon-counting detector (PCD) CT. This prospective study included consecutive participants (April-September 2021) who underwent CTA with PCD CT of the thoracoabdominal aorta and previous CTA with energy-integrating detector (EID) CT at equal radiation doses. In PCD CT, virtual monoenergetic images (VMI) were reconstructed in 5-keV intervals from 40 to 60 keV. Attenuation of the aorta, image noise, and contrast-to-noise ratio (CNR) were measured, and subjective image quality was rated by two independent readers. In the first group of participants, the same contrast media protocol was used for both scans. CNR gain in PCD CT compared with EID CT served as the reference for contrast media volume reduction in the second group. Noninferiority analysis was used to test noninferior image quality of the low-volume contrast media protocol with PCD CT. The study included 100 participants (mean age, 75 years ± 8 [SD]; 83 men). In the first group (n = 40), VMI at 50 keV provided the best trade-off between objective and subjective image quality, achieving 25% higher CNR compared with EID CT. Contrast media volume in the second group (n = 60) was reduced by 25% (52.5 mL). Mean differences in CNR and subjective image quality between EID CT and PCD CT at 50 keV were above the predefined boundaries of noninferiority (-0.54 [95% CI: -1.71, 0.62] and -0.36 [95% CI: -0.41, -0.31], respectively). CTA of the aorta with PCD CT was associated with higher CNR, which was translated into a low-volume contrast media protocol demonstrating noninferior image quality compared with EID CT at the same radiation dose.Keywords: CT Angiography, CT-Spectral, Vascular, Aorta, Contrast Agents-Intravenous, Technology Assessment© RSNA, 2023See also the commentary by Dundas and Leipsic in this issue.

Objective: The aim of this study was to determine the potential of photon-counting detector computed tomography (PCD-CT) for radiation dose reduction compared with conventional energy-integrated detector CT (EID-CT) in the assessment of interstitial lung disease (ILD) in systemic sclerosis (SSc) patients. Methods: In this retrospective study, SSc patients receiving a follow-up noncontrast chest examination on a PCD-CT were included between May 2021 and December 2021. Baseline scans were generated on a dual-source EID-CT by selecting the tube current-time product for each of the 2 x-ray tubes to obtain a 100% (D₁₀₀), a 66% (D₆₆), and a 33% dose image (D₃₃) from the same data set. Slice thickness and kernel were adjusted between the 2 scans. Image noise was assessed by placing a fixed region of interest in the subcutaneous fat. Two independent readers rated subjective image quality (5-point Likert scale), presence, extent, diagnostic confidence, and accuracy of SSc-ILD. D₁₀₀ interpreted by a radiologist with 22 years of experience served as reference standard. Interobserver agreement was calculated with Cohen κ, and mean variables were compared by a paired t test. Results: Eighty patients (mean 56 ± 14; 64 women) were included. Although CTDI_vol of PCD-CT was comparable to D₃₃ (0.72 vs 0.76 mGy, P = 0.091), mean image noise of PCD-CT was comparable to D₁₀₀ (131 ± 15 vs 113 ± 12, P > 0.05). Overall subjective image quality of PCD-CT was comparable to D₁₀₀ (4.72 vs 4.71; P = 0.874). Diagnostic accuracy was higher in PCD-CT compared with D₃₃/D₆₆ (97.6% and 92.5%/96.3%, respectively) and comparable to D₁₀₀ (98.1%). Conclusions: With PCD-CT, a radiation dose reduction of 66% compared with EID-CT is feasible, without penalty in image quality and diagnostic performance for the evaluation of ILD.

The aim of this study was to characterize image quality and to determine the optimal strength levels of a novel iterative reconstruction algorithm (quantum iterative reconstruction, QIR) for low-dose, ultra-high-resolution (UHR) photon-counting detector CT (PCD-CT) of the lung. Images were acquired on a clinical dual-source PCD-CT in the UHR mode and reconstructed with a sharp lung reconstruction kernel at different strength levels of QIR (QIR-1 to QIR-4) and without QIR (QIR-off). Noise power spectrum (NPS) and target transfer function (TTF) were analyzed in a cylindrical phantom. 52 consecutive patients referred for low-dose UHR chest PCD-CT were included (CTDI_vol: 1 ± 0.6 mGy). Quantitative image quality analysis was performed computationally which included the calculation of the global noise index (GNI) and the global signal-to-noise ratio index (GSNRI). The mean attenuation of the lung parenchyma was measured. Two readers graded images qualitatively in terms of overall image quality, image sharpness, and subjective image noise using 5-point Likert scales. In the phantom, an increase in the QIR level slightly decreased spatial resolution and considerably decreased noise amplitude without affecting the frequency content. In patients, GNI decreased from QIR-off (202 ± 34 HU) to QIR-4 (106 ± 18 HU) (p < 0.001) by 48%. GSNRI increased from QIR-off (4.4 ± 0.8) to QIR-4 (8.2 ± 1.6) (p < 0.001) by 87%. Attenuation of lung parenchyma was highly comparable among reconstructions (QIR-off: −849 ± 53 HU to QIR-4: −853 ± 52 HU, p < 0.001). Subjective noise was best in QIR-4 (p < 0.001), while QIR-3 was best for sharpness and overall image quality (p < 0.001). Thus, our phantom and patient study indicates that QIR-3 provides the optimal iterative reconstruction level for low-dose, UHR PCD-CT of the lungs.

Virtual noncontrast images from contrast-enhanced arterial and venous phase abdominal scans acquired using photon-counting detector CT yielded accurate attenuation values and good overall image qua... Background Accurate CT attenuation and diagnostic quality of virtual noncontrast (VNC) images acquired with photon-counting detector (PCD) CT are needed to replace true noncontrast (TNC) scans. Pur...

This document from the European Society of Thoracic Imaging (ESTI) and the European Society of Radiology (ESR) discusses the role of imaging in the long-term follow-up of COVID-19 patients, to define which patients may benefit from imaging, and what imaging modalities and protocols should be used. Insights into imaging features encountered on computed tomography (CT) scans and potential pitfalls are discussed and possible areas for future review and research are also included. Key Points: • Post-COVID-19 pneumonia changes are mainly consistent with prior organizing pneumonia and are likely to disappear within 12 months of recovery from the acute infection in the majority of patients. • At present, with the longest series of follow-up examinations reported not exceeding 12 months, the development of persistent or progressive fibrosis in at least some individuals cannot yet be excluded. • Residual ground glass opacification may be associated with persisting bronchial dilatation and distortion, and might be termed “fibrotic-like changes” probably consistent with prior organizing pneumonia.

Objective: The aim of this study was to evaluate the image quality (IQ) and performance of an artificial intelligence (AI)-based computer-aided detection (CAD) system in photon-counting detector computed tomography (PCD-CT) for pulmonary nodule evaluation at different low-dose levels. Materials and Methods: An anthropomorphic chest-phantom containing 14 pulmonary nodules of different sizes (range, 3–12 mm) was imaged on a PCD-CT and on a conventional energy-integrating detector CT (EID-CT). Scans were performed with each of the 3 vendor-specific scanning modes (QuantumPlus [Q+], Quantum [Q], and High Resolution [HR]) at decreasing matched radiation dose levels (volume computed tomography dose index ranging from 1.79 to 0.31 mGy) by adapting IQ levels from 30 to 5. Image noise was measured manually in the chest wall at 8 different locations. Subjective IQ was evaluated by 2 readers in consensus. Nodule detection and volumetry were performed using a commercially available AI-CAD system. Results: Subjective IQ was superior in PCD-CT compared with EID-CT (P < 0.001), and objective image noise was similar in the Q+ and Q-mode (P > 0.05) and superior in the HR-mode (PCD 55.8 ± 11.7 HU vs EID 74.8 ± 5.4 HU; P = 0.01). High resolution showed the lowest image noise values among PCD modes (P = 0.01). Overall, the AI-CAD system delivered comparable results for lung nodule detection and volumetry between PCD- and dose-matched EID-CT (P = 0.08–1.00), with a mean sensitivity of 95% for PCD-CT and of 86% for dose-matched EID-CT in the lowest evaluated dose level (IQ5). Q+ and Q-mode showed higher false-positive rates than EID-CT at lower-dose levels (IQ10 and IQ5). The HR-mode showed a sensitivity of 100% with a false-positive rate of 1 even at the lowest evaluated dose level (IQ5; CDTI_vol, 0.41 mGy). Conclusions: Photon-counting detector CT was superior to dose-matched EID-CT in subjective IQ while showing comparable to lower objective image noise. Fully automatized AI-aided nodule detection and volumetry are feasible in PCD-CT, but attention has to be paid to false-positive findings.