دورية أكاديمية
Improvement of depiction of the intracranial arteries on brain CT angiography using deep learning reconstruction
| العنوان: | Improvement of depiction of the intracranial arteries on brain CT angiography using deep learning reconstruction |
|---|---|
| المساهمون: | Chuluunbaatar Otgonbaatar, Jae-Kyun Ryu, Seonkyu Kim, Jung Wook Seo, Hackjoon Shim, Dae Hyun Hwang, Shim, Hack Joon |
| بيانات النشر: | IMR Press |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Adolescent, Adult, 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, Computer-Assisted* / standards, Male, Middle Aged, Middle Cerebral Artery / diagnostic imaging, Retrospective Studies, Vertebral Artery / diagnostic imaging, Young Adult, Brain angiography, Computed tomography, Deep learning reconstruction algorithm, Image reconstruction, Intracranial vessel |
| الوصف: | 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. ; restriction |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 0219-6352 |
| العلاقة: | JOURNAL OF INTEGRATIVE NEUROSCIENCE; J04316; OAK-2022-05361; https://ir.ymlib.yonsei.ac.kr/handle/22282913/191110Test; https://www.imrpress.com/journal/JIN/20/4/10.31083/j.jin2004097Test; T999202276; JOURNAL OF INTEGRATIVE NEUROSCIENCE, Vol.20(4) : 967-976, 2021-12 |
| DOI: | 10.31083/j.jin2004097 |
| الإتاحة: | https://doi.org/10.31083/j.jin2004097Test https://ir.ymlib.yonsei.ac.kr/handle/22282913/191110Test |
| حقوق: | CC BY-NC-ND 2.0 KR |
| رقم الانضمام: | edsbas.55E0734A |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 02196352 |
|---|---|
| DOI: | 10.31083/j.jin2004097 |