The effects of the skull on CT imaging of the brain: a skull and brain phantom study

[Artifact Assessment in the Skull Base Region of Head CT Images with Various Tilt Angles Relative to the Orbitomeatal Line]

PURPOSE

The aim of this study was to evaluate artifacts in the skull base region of head computed tomography (CT) images with various tilt angles relative to the orbitomeatal line.

METHODS

CT images of a head phantom acquired by helical and non-helical scanning with the tilt angles set from 0 to 20 degrees in 5-degree increments were evaluated in this study. Regions of interest (ROIs) were set at the cerebellum, temporal lobe, frontal lobe, and basal ganglia in the phantom images. Artifacts were evaluated by the coefficient of variation (CV) of the mean CT value between ROIs and the location parameter (β) of the Gumbel method.

RESULTS

The CV and β values increased with increasing tilt angle for both helical and nonhelical images in the frontal lobes, but both decreased in the cerebellar region. In the temporal lobe and basal ganglia, there was no trend of change with tilt angle.

CONCLUSION

Increasing the tilt angle relative to the OM line increased artifacts at the frontal lobes and decreased artifacts at the cerebellar region.

Deep Learning-Based Versus Iterative Image Reconstruction for Unenhanced Brain CT: A Quantitative Comparison of Image Quality

This exploratory retrospective study aims to quantitatively compare the image quality of unenhanced brain computed tomography (CT) reconstructed with an iterative (AIDR-3D) and a deep learning-based (AiCE) reconstruction algorithm. After a preliminary phantom study, AIDR-3D and AiCE reconstructions (0.5 mm thickness) of 100 consecutive brain CTs acquired in the emergency setting on the same 320-detector row CT scanner were retrospectively analyzed, calculating image noise reduction attributable to the AiCE algorithm, artifact indexes in the posterior cranial fossa, and contrast-to-noise ratios (CNRs) at the cortical and thalamic levels. In the phantom study, the spatial resolution of the two datasets proved to be comparable; conversely, AIDR-3D reconstructions showed a broader noise pattern. In the human study, median image noise was lower with AiCE compared to AIDR-3D (4.7 vs. 5.3, p < 0.001, median 19.6% noise reduction), whereas AIDR-3D yielded a lower artifact index than AiCE (7.5 vs. 8.4, p < 0.001). AiCE also showed higher median CNRs at the cortical (2.5 vs. 1.8, p < 0.001) and thalamic levels (2.8 vs. 1.7, p < 0.001). These results highlight how image quality improvements granted by deep learning-based (AiCE) and iterative (AIDR-3D) image reconstruction algorithms vary according to different brain areas.

Head CT: Toward Making Full Use of the Information the X-Rays Give

Although clinical head CT images are typically interpreted qualitatively, automated methods applied to routine clinical head CTs enable quantitative assessment of brain volume, brain parenchymal fraction, brain radiodensity, and brain radiomass. These metrics gain clinical meaning when viewed relative to a reference database and expressed as quantile regression values. Quantitative imaging data can aid in objective reporting and in the identification of outliers, with possible diagnostic implications. The comparison to a reference database necessitates standardization of head CT imaging parameters and protocols. Future research is needed to learn the effects of virtual monochromatic imaging on the quantitative characteristics of head CT images.