Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects-Reference-Cited by-同舟云学术

Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects

Published:2023-03-01 Issue:3 Volume:306 Page:
ISSN:0033-8419
Container-title:Radiology
language:en
Short-container-title:Radiology

Author:

Koetzier Lennart R.,Mastrodicasa Domenico^ORCID,Szczykutowicz Timothy P.^ORCID,van der Werf Niels R.,Wang Adam S.^ORCID,Sandfort Veit,van der Molen Aart J.^ORCID,Fleischmann Dominik^ORCID,Willemink Martin J.^ORCID

Publisher

Radiological Society of North America (RSNA)

Subject

Radiology, Nuclear Medicine and imaging

Link

http://pubs.rsna.org/doi/pdf/10.1148/radiol.221257

Reference91 articles.

1. Algebraic Reconstruction Techniques (ART) for three-dimensional electron microscopy and X-ray photography

2. The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence

3. Digital radiography: The balance between image quality and required radiation dose

4. Iterative reconstruction techniques for computed tomography Part 1: Technical principles

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