Deep learning image reconstruction algorithm for pancreatic protocol dual-energy computed tomography: image quality and quantification of iodine concentration

2021 ◽  
Author(s):  
Yoshifumi Noda ◽  
Nobuyuki Kawai ◽  
Shoma Nagata ◽  
Fumihiko Nakamura ◽  
Takayuki Mori ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Reconstruction Algorithm ◽  
Iodine Concentration ◽  
Dual Energy ◽  
Image Reconstruction Algorithm ◽  
Tomography Image ◽  
Computed Tomography Image

scholarly journals Deep learning based image reconstruction algorithm for limited-angle translational computed tomography

PLoS ONE ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. e0226963
Author(s):  
Jiaxi Wang ◽  
Jun Liang ◽  
Jingye Cheng ◽  
Yumeng Guo ◽  
Li Zeng
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Image Reconstruction ◽  
Reconstruction Algorithm ◽  
Image Reconstruction Algorithm ◽  
Limited Angle

scholarly journals New algorithms for computed tomography image reconstruction to eliminate artifacts

2021 ◽  
Author(s):  
Eli Lechtman
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Reconstruction Algorithm ◽  
Filtered Backprojection ◽  
Reconstruction Algorithms ◽  
Reconstruction Methods ◽  
Tomography Image ◽  
Ct Simulation ◽  
Novel Algorithms ◽  
Computed Tomography Image

Computed tomography (CT) relies on computational algorithms to reconstruct images from CT projections. Current filtered backprojection reconstruction methods have inherent limitations in situations with sharp density gradients and limited beam views. In this thesis two novel reconstruction algorithms were introduced: the Algebraic Image Reconstruction (AIR) algorithm, and the Geometric Image Reconstruction Algorithm (GIRA). A CT simulation was developed to test these novel algorithms and compare their images to filtered backprojection images. AIR and GIRA each demonstrated their proof of principle in these preliminary tests. AIR and its extension, the Parsed AIR algorithm (PAIR), were able to reconstruct optimal images compared to filtered backprojection after empirically determining parameters relevant to the algorithms. While GIRA reconstructed optimal images in preliminary tests, reconstruction was complicated by error propagation for larger imaging domains. The initial success of these novel approaches justifies continued research and development to determine their feasibility for practical CT image reconstruction.

Possibility of Deep Learning in Medical Imaging Focusing Improvement of Computed Tomography Image Quality

2020 ◽  
Vol 44 (2) ◽  
pp. 161-167 ◽  
Author(s):  
Yuko Nakamura ◽  
Toru Higaki ◽  
Fuminari Tatsugami ◽  
Yukiko Honda ◽  
Keigo Narita ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Image Quality ◽  
Medical Imaging ◽  
Tomography Image ◽  
Computed Tomography Image
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