Photon-counting CT Material Decomposition: Initial Experience in Assessing Adrenal Adenoma.

See also the editorial by Pourmorteza in this issue.

Photon counting computed tomography of in-stent-stenosis in a phantom: Optimal virtual monoenergetic imaging in ultra high resolution.

Rationale and objectives: Coronary computed tomography angiography (CCTA) is becoming increasingly important for the diagnostic workup of coronary artery disease, nevertheless, imaging of in-stent stenosis remains challenging. For the first time, spectral imaging in Ultra High Resolution (UHR) is now possible in clinically available photon counting CT. The aim of this work is to determine the optimal virtual monoenergetic image (VMI) for imaging in-stent stenoses in cardiac stents. Materials and methods: 6 stents with inserted hypodense stenoses were scanned in an established phantom in UHR mode. Images were reconstructed with 3 different kernels for spectral data (Qr56, Qr64, Qr72) with varying levels of sharpness. Based on region of interest (ROI) measurements image quality parameters including contrast-to-noise ratio (CNR) were analyzed for all available VMI (40 keV-190 keV). Finally, based on quantitative results and VMI used in clinical routine, a set of VMI was included in a qualitative reading. Results: CNR showed significant variations across different keV levels (p < 0.001). Due to reduced noise there was a focal maximum in the VMI around 65 keV. The peak values were observed for kernel Qr56 at 116 keV with 19.47 ± 8.67, for kernel Qr64 at 114 keV with 13.56 ± 6.58, and for kernel Qr72 at 106 keV with 12.19 ± 3.25. However, in the qualitative evaluation the VMI with lower keV (55 keV) performed best. Conclusions: Based on these experimental results, a photon counting CCTA in UHR with stents should be reconstructed with the Qr72 kernel for the assessment of in-stent stenoses, and a VMI 55 keV should be computed for the evaluation.

Dedicated virtual non-contrast images adapted for liver tissue in clinical photon counting CT improve virtual non-contrast imaging in various organs beyond the liver.

PURPOSE: Purpose of this study is to re-evaluate the accuracy and diagnostic reliability of virtual non-contrast (VNC) images acquired with the photon-counting computed tomography (PCCT) after an update of the CT scanner software. METHODS: Fifty-four patients were retrospectively enrolled. VNC images were reconstructed from true non-contrast (TNC) images (VNCn) and contrast-enhanced images in portal venous contrast phase (VNCv). Additionally, a liver-specific VNC (VNCl) was assessed. Quantitative image properties of VNC and TNC images were compared and consistency between VNC images was evaluated. Regions of interest were drawn in the liver, spleen, renal cortex, aorta, muscle and subcutaneous fat. RESULTS: Attenuation values on all VNC images differed significantly from TNC images in the liver, renal cortex, aorta and fat. A mean offset of <10HU between TNC and all VNC images was found in the liver, spleen and muscle. The comparison of TNC and VNCl images revealed an offset < 10HU in fat. Differences ≤ 10HU between TNC and VNCv and between TNC and VNCl were found in 68%, respectively in 75%. Differences ≤ 15HU were found in 79%, respectively in 92% of all measurements. Differences ≤ 10HU between TNC and VNCn were found in 79% and differences ≤ 15HU in 85%. CONCLUSION: Although there are statistically significant differences between HU values measured on TNC and VNC images in certain tissues, the minor offsets measured in liver and spleen suggest a good clinical applicability of VNCv and VNCl images. The significantly lower offset in subcutaneous fat on VNCl images suggests a superiority for measurements in adipose tissues.