A preliminary study of super-resolution deep learning reconstruction with cardiac option for evaluation of endovascular-treated intracranial aneurysms.

Autor: Otgonbaatar C; Department of Radiology, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea.; Medical Imaging AI Research Center, Canon Medical Systems Korea, Seoul, 06173, Republic of Korea., Kim H; Department of Radiology, Wonju Severance Christian Hospital, Wonju College of Medicine, Yonsei University of Korea, Wonju 26426, Republic of Korea., Jeon PH; Department of Radiology, Wonju Severance Christian Hospital, Wonju College of Medicine, Yonsei University of Korea, Wonju 26426, Republic of Korea., Jeon SH; Department of Radiology, Wonju Severance Christian Hospital, Wonju College of Medicine, Yonsei University of Korea, Wonju 26426, Republic of Korea., Cha SJ; Department of Radiology, Wonju Severance Christian Hospital, Wonju College of Medicine, Yonsei University of Korea, Wonju 26426, Republic of Korea., Ryu JK; Medical Imaging AI Research Center, Canon Medical Systems Korea, Seoul, 06173, Republic of Korea., Jung WB; Korea Brain Research Institute (KBRI), Daegu, 41062, Republic of Korea., Shim H; Medical Imaging AI Research Center, Canon Medical Systems Korea, Seoul, 06173, Republic of Korea.; ConnectAI Research Center, Yonsei University College of Medicine, Seoul, 03772, Republic of Korea., Ko SM; Department of Radiology, Wonju Severance Christian Hospital, Wonju College of Medicine, Yonsei University of Korea, Wonju 26426, Republic of Korea., Kim JW; Department of Radiology, Wonju Severance Christian Hospital, Wonju College of Medicine, Yonsei University of Korea, Wonju 26426, Republic of Korea.
Jazyk: angličtina
Zdroj: The British journal of radiology [Br J Radiol] 2024 Aug 01; Vol. 97 (1160), pp. 1492-1500.
DOI: 10.1093/bjr/tqae117
Abstrakt: Objectives: To investigate the usefulness of super-resolution deep learning reconstruction (SR-DLR) with cardiac option in the assessment of image quality in patients with stent-assisted coil embolization, coil embolization, and flow-diverting stent placement compared with other image reconstructions.
Methods: This single-centre retrospective study included 50 patients (mean age, 59 years; range, 44-81 years; 13 men) who were treated with stent-assisted coil embolization, coil embolization, and flow-diverting stent placement between January and July 2023. The images were reconstructed using filtered back projection (FBP), hybrid iterative reconstruction (IR), and SR-DLR. The objective image analysis included image noise in the Hounsfield unit (HU), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and full width at half maximum (FWHM). Subjectively, two radiologists evaluated the overall image quality for the visualization of the flow-diverting stent, coil, and stent.
Results: The image noise in HU in SR-DLR was 6.99 ± 1.49, which was significantly lower than that in images reconstructed with FBP (12.32 ± 3.01) and hybrid IR (8.63 ± 2.12) (P < .001). Both the mean SNR and CNR were significantly higher in SR-DLR than in FBP and hybrid IR (P < .001 and P < .001). The FWHMs for the stent (P < .004), flow-diverting stent (P < .001), and coil (P < .001) were significantly lower in SR-DLR than in FBP and hybrid IR. The subjective visual scores were significantly higher in SR-DLR than in other image reconstructions (P < .001).
Conclusions: SR-DLR with cardiac option is useful for follow-up imaging in stent-assisted coil embolization and flow-diverting stent placement in terms of lower image noise, higher SNR and CNR, superior subjective image analysis, and less blooming artifact than other image reconstructions.
Advances in Knowledge: SR-DLR with cardiac option allows better visualization of the peripheral and smaller cerebral arteries. SR-DLR with cardiac option can be beneficial for CT imaging of stent-assisted coil embolization and flow-diverting stent.
(© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)
Databáze: MEDLINE