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Improvement of depiction of the intracranial arteries on brain CT angiography using deep learning reconstruction

Authors
 Chuluunbaatar Otgonbaatar  ;  Jae-Kyun Ryu  ;  Seonkyu Kim  ;  Jung Wook Seo  ;  Hackjoon Shim  ;  Dae Hyun Hwang 
Citation
 JOURNAL OF INTEGRATIVE NEUROSCIENCE, Vol.20(4) : 967-976, 2021-12 
Journal Title
JOURNAL OF INTEGRATIVE NEUROSCIENCE
ISSN
 0219-6352 
Issue Date
2021-12
MeSH
Adolescent ; Adult ; Aged ; Aged, 80 and over ; Carotid Artery, Internal / diagnostic imaging ; Cerebral Arteries / diagnostic imaging* ; Computed Tomography Angiography* / methods ; Computed Tomography Angiography* / standards ; Deep Learning* ; Female ; Humans ; Image Processing, Computer-Assisted* / methods ; Image Processing, Computer-Assisted* / standards ; Male ; Middle Aged ; Middle Cerebral Artery / diagnostic imaging ; Retrospective Studies ; Vertebral Artery / diagnostic imaging ; Young Adult
Keywords
Brain angiography ; Computed tomography ; Deep learning reconstruction algorithm ; Image reconstruction ; Intracranial vessel
Abstract
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.
Full Text
https://www.imrpress.com/journal/JIN/20/4/10.31083/j.jin2004097
DOI
10.31083/j.jin2004097
Appears in Collections:
1. College of Medicine (의과대학) > Yonsei Biomedical Research Center (연세의생명연구원) > 1. Journal Papers
Yonsei Authors
Shim, Hack Joon(심학준)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/191110
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