Zobrazeno 1 - 10
of 208
pro vyhledávání: '"Deep-learning image reconstruction"'
Publikováno v:
CT Lilun yu yingyong yanjiu, Vol 33, Iss 6, Pp 683-691 (2024)
Objective: To investigate the value of combining a deep learning image reconstruction algorithm and an energy spectral single-energy technique to improve the image quality of the thoracic aorta with a low contrast agent flow rate. Materials and Metho
Externí odkaz:
https://doaj.org/article/ecfe9cc5038f4dfea23832313770b460
Publikováno v:
Xin yixue, Vol 55, Iss 9, Pp 685-692 (2024)
Objective To explore the application value of 80 kV deep learning image reconstruction （DLIR） algorithm in coronary CT angiography （CCTA）. Methods Sixty patients who underwent CCTA were divided into two groups based on the scanning protocols
Externí odkaz:
https://doaj.org/article/f3e9be9c1e5b4c6fae8b00297e3b3c64
Autor:
L. D’hondt, C. Franck, P-J. Kellens, F. Zanca, D. Buytaert, A. Van Hoyweghen, H. El Addouli, K. Carpentier, M. Niekel, M. Spinhoven, K. Bacher, A. Snoeckx
Publikováno v:
Cancer Imaging, Vol 24, Iss 1, Pp 1-15 (2024)
Abstract Background This study systematically compares the impact of innovative deep learning image reconstruction (DLIR, TrueFidelity) to conventionally used iterative reconstruction (IR) on nodule volumetry and subjective image quality (IQ) at high
Externí odkaz:
https://doaj.org/article/f2e893e4eec7496db11917cdd2f0f6b4
Publikováno v:
Zhenduanxue lilun yu shijian, Vol 23, Iss 02, Pp 139-145 (2024)
Objective To investigate the effect of dual-energy CT (DECT) virtual non-contrast (VNC) images reconstructed by deep learning image reconstruction (DLIR) on the image quality and measurements of renal calculus in CT urography (CTU). Methods The clini
Externí odkaz:
https://doaj.org/article/f72f8e07d0fa46cb88068489bcff7a15
Publikováno v:
European Journal of Radiology Open, Vol 13, Iss , Pp 100599- (2024)
Purpose: To compare image quality and detection accuracy of renal stones between deep learning image reconstruction (DLIR) and Adaptive Statistical Iterative Reconstruction-Veo (ASIR-V) reconstructed virtual non-contrast (VNC) images and true non-con
Externí odkaz:
https://doaj.org/article/19081da7ddb5479e8ac329555bab2afc
Autor:
Varin Jaruvongvanich, Kobkun Muangsomboon, Wanwarang Teerasamit, Voraparee Suvannarerg, Chulaluk Komoltri, Sastrawut Thammakittiphan, Wimonrat Lornimitdee, Witchuda Ritsamrej, Parinya Chaisue, Napapong Pongnapang, Piyaporn Apisarnthanarak
Publikováno v:
Heliyon, Vol 10, Iss 15, Pp e34847- (2024)
Background: Deep learning image reconstruction (DLIR) is a novel computed tomography (CT) reconstruction technique that minimizes image noise, enhances image quality, and enables radiation dose reduction. This study aims to compare the diagnostic per
Externí odkaz:
https://doaj.org/article/ef9ccb33b41840bb99224cac6d663562
Autor:
Wenjie Wu, Hefeng Zhan, Yiran Wang, Xueyan Ma, Jiameng Hou, Lichen Ren, Jie Liu, Luotong Wang, Yonggao Zhang
Publikováno v:
Frontiers in Cardiovascular Medicine, Vol 11 (2024)
ObjectiveThis study aims to investigate the image quality of a high-resolution, low-dose coronary CT angiography (CCTA) with deep learning image reconstruction (DLIR) and second-generation motion correction algorithms, namely, SnapShot Freeze 2 (SSF2
Externí odkaz:
https://doaj.org/article/07c48f5b415e4b2d8195256e0b498a89
Publikováno v:
F1000Research, Vol 13 (2024)
Background Non-contrast Computed Tomography (NCCT) plays a pivotal role in assessing central nervous system disorders and is a crucial diagnostic method. Iterative reconstruction (IR) methods have enhanced image quality (IQ) but may result in a blotc
Externí odkaz:
https://doaj.org/article/67a2e7c0dacf4302940ad140e19d2882
Publikováno v:
F1000Research, Vol 13 (2024)
Background The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in Iterative reconstruction (IR) techniques such as negative image texture and
Externí odkaz:
https://doaj.org/article/128303175f714159a5c5a2966c0a86c1
Autor:
Gyeong Deok Jo, Chulkyun Ahn, Jung Hee Hong, Da Som Kim, Jongsoo Park, Hyungjin Kim, Jong Hyo Kim, Jin Mo Goo, Ju Gang Nam
Publikováno v:
BMC Medical Imaging, Vol 23, Iss 1, Pp 1-10 (2023)
Abstract Objective Few studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to compare the image quality and lung nodule detectability between chest CT using a quarter of th
Externí odkaz:
https://doaj.org/article/13ee5b315d8c412d93d7fd4bb3fe742c