Photon-Counting Detector CT of the Brain Reduces Variability of Hounsfield Units and Has a Mean Offset Compared with...

Distinguishing GM from WM is essential for CT of the brain. The recently established photon-counting detector (PCD)-CT technology uses a novel detection technique that might allow more precise measurement of tissue attenuation for an improved delineation of attenuation values (Hounsfield units [HU]) and improved image quality in comparison with energy-integrating detector (EID)-CT. To investigate this, we compared HU, GM versus WM contrast, and image noise by using automated deep learning-based brain segmentations.

We retrospectively included patients who received either PCD-CT or EID-CT and did not display a cerebral pathology. A deep learning-based segmentation of the GM and WM was used to extract HU. From this, the gray-white matter ratio (GWR) and contrast-to-noise ratio (CNR) were calculated.

We included 329 patients with EID-CT (mean age 59.8 ± 20.2 years) and 180 with PCD-CT (mean age 64.7 ± 16.5 years). GM and WM showed significantly lower HU in PCD-CT (GM: 40.4 ± 2.2 HU; WM: 33.4 ± 1.5 HU) compared with EID-CT (GM: 45.1 ± 1.6 HU; WM: 37.4 ± 1.6 HU; P < .001). Standard deviations of HU were also lower in PCD-CT (GM and WM both P < .001) and CNR was significantly higher in PCD-CT compared with EID-CT (P < .001). GWRs were not significantly different across both modalities (P > .99). In an age-matched subset (n=157 patients from both cohorts), all findings were replicated.

This comprehensive comparison of HU in cerebral GM and WM revealed substantially reduced image noise and an average offset with lower HU in PCD-CT while the ratio between GM and WM remained constant. The potential need to adapt windowing presets based on this finding should be investigated in future studies.