A Review of Deep Learning CT Reconstruction: Concepts, Limitations, and Promise in Clinical Practice

Purpose of Review Deep Learning reconstruction (DLR) is the current state-of-the-art method for CT image formation. Comparisons to existing filter back-projection, iterative, and model-based reconstructions are now available in the literature. This review summarizes the prior reconstruction methods,...

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Bibliographic Details
Published in:Current radiology reports (Philadelphia, PA ) PA ), 2022-09, Vol.10 (9), p.101-115
Main Authors: Szczykutowicz, Timothy P., Toia, Giuseppe V., Dhanantwari, Amar, Nett, Brian
Format: Article
Language:English
Subjects:
Computed Tomography
Computed Tomography (S Nicolaou and M Mohammed
Deep learning
Image reconstruction
Imaging
Iterative methods
Literature reviews
Medicine
Medicine & Public Health
Noise reduction
Radiology
Section Editors
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Summary:Purpose of Review Deep Learning reconstruction (DLR) is the current state-of-the-art method for CT image formation. Comparisons to existing filter back-projection, iterative, and model-based reconstructions are now available in the literature. This review summarizes the prior reconstruction methods, introduces DLR, and then reviews recent findings from DLR from a physics and clinical perspective. Recent Findings DLR has been shown to allow for noise magnitude reductions relative to filtered back-projection without suffering from “plastic” or “blotchy” noise texture that was found objectionable with most iterative and model-based solutions. Clinically, early reader studies have reported increases in subjective quality scores and studies have successfully implemented DLR-enabled dose reductions. Summary The future of CT image reconstruction is bright; deep learning methods have only started to tackle problems in this space via addressing noise reduction. Artifact mitigation and spectral applications likely be future candidates for DLR applications.
ISSN:2167-4825
2167-4825
DOI:10.1007/s40134-022-00399-5