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

Filtered back projection (FBP) has been the standard CT image reconstruction method for 4 decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in several clinical applications. However, with faster and more advanced CT scanners, FBP has become increasingly obsolete....

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Vydáno v:Radiology Ročník 306; číslo 3; s. e221257
Hlavní autoři: Koetzier, Lennart R, Mastrodicasa, Domenico, Szczykutowicz, Timothy P, van der Werf, Niels R, Wang, Adam S, Sandfort, Veit, van der Molen, Aart J, Fleischmann, Dominik, Willemink, Martin J
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 01.03.2023
Témata:
Algorithms
Artificial Intelligence
Deep Learning
Humans
Image Processing, Computer-Assisted - methods
Radiation Dosage
Radiographic Image Interpretation, Computer-Assisted - methods
Tomography, X-Ray Computed - methods
ISSN:1527-1315, 1527-1315
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Shrnutí:Filtered back projection (FBP) has been the standard CT image reconstruction method for 4 decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in several clinical applications. However, with faster and more advanced CT scanners, FBP has become increasingly obsolete. Higher image noise and more artifacts are especially noticeable in lower-dose CT imaging using FBP. This performance gap was partly addressed by model-based iterative reconstruction (MBIR). Yet, its "plastic" image appearance and long reconstruction times have limited widespread application. Hybrid iterative reconstruction partially addressed these limitations by blending FBP with MBIR and is currently the state-of-the-art reconstruction technique. In the past 5 years, deep learning reconstruction (DLR) techniques have become increasingly popular. DLR uses artificial intelligence to reconstruct high-quality images from lower-dose CT faster than MBIR. However, the performance of DLR algorithms relies on the quality of data used for model training. Higher-quality training data will become available with photon-counting CT scanners. At the same time, spectral data would greatly benefit from the computational abilities of DLR. This review presents an overview of the principles, technical approaches, and clinical applications of DLR, including metal artifact reduction algorithms. In addition, emerging applications and prospects are discussed.
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ISSN:1527-1315
1527-1315
DOI:10.1148/radiol.221257