IMR Press / JIN / Volume 20 / Issue 4 / DOI: 10.31083/j.jin2004097
Open Access Original Research
Improvement of depiction of the intracranial arteries on brain CT angiography using deep learning reconstruction
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1 Department of Radiology, College of Medicine, Seoul National University, 03080 Seoul, Republic of Korea
2 Medical Imaging AI Research Center, Canon Medical Systems Korea, 06173 Seoul, Republic of Korea
3 Department of Radiology, Inje University Ilsan Paik Hospital, 10380 Goyang, Republic of Korea
4 Connect AI Research Center, Yonsei University College of Medicine, 03772 Seoul, Republic of Korea
5 Department of Radiology, Inje University Seoul Paik Hospital, 04551 Seoul, Republic of Korea
*Correspondence: (Hackjoon Shim); (Dae Hyun Hwang)
J. Integr. Neurosci. 2021, 20(4), 967–976;
Submitted: 25 October 2021 | Revised: 22 November 2021 | Accepted: 1 December 2021 | Published: 30 December 2021
Copyright: © 2021 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license (

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.

Computed tomography
Brain angiography
Intracranial vessel
Image reconstruction
Deep learning reconstruction algorithm
Fig. 1.
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