Otgonbaatar C, et al. Korean J Radiol. 2022 Sep;23:e68. https://doi.org/10.3348/kjr.2022.0127

To evaluate the ability of a commercialized deep learning reconstruction technique to depict intracranial vessels on the brain computed tomography angiography and compare the image quality with filtered-back-projection and hybrid iterative reconstruction in terms of objective and subjective measures. Forty-three patients underwent brain computed tomography angiography, and images were reconstructed using three algorithms: filtered-back-projection, hybrid iterative reconstruction, and deep learning reconstruction. The image noise, computed tomography attenuation value, signal-to-noise ratio, and contrast-to-noise ratio were measured in the bilateral cavernous segment of the internal carotid artery, vertebral artery, basilar apex, horizontal segment of the middle cerebral artery and used for the objective assessment of the image quality among the three different reconstructions. The subjective image quality score was significantly higher for the deep learning reconstruction than hybrid iterative reconstruction and filtered-back-projection images. The deep learning reconstruction markedly improved the reduction of blooming artifacts in surgical clips and coiled aneurysms. The deep learning reconstruction method generally improves the image quality of brain computed tomography angiography in terms of objective measurement and subjective grading compared with filtered-back-projection and hybrid iterative reconstruction. Especially, deep learning reconstruction is deemed advantageous for better depiction of small vessels compared to filtered-back projection and hybrid iterative reconstruction.

Oh Y, et al. Neurointervention. 2012 Feb;7(1):50-53. http://dx.doi.org/10.5469/neuroint.2012.7.1.50

Kim KS, et al. Neurointervention. 2012 Feb;7(1):27-33. http://dx.doi.org/10.5469/neuroint.2012.7.1.27

OHE H, et al. J Korean Fract Soc. 2020 Oct;33(4):217-221. https://doi.org/10.12671/jkfs.2020.33.4.217