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

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

*Corresponding author for this work

Research output: Contribution to journalReview articleAcademicpeer-review

53 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article numbere221257
JournalRadiology
Volume306
Issue number3
DOIs
Publication statusPublished - 1 Mar 2023

Bibliographical note

Funding Information:
Disclosures of conflicts of interest: L.R.K. No relevant relationships. D.M. Grant from the National Institute of Biomedical Imaging and Bioengineering (no. 5T32EB009035); consulting fees from Segmed; stock or stock options in Segmed; member of the trainee editorial board for Radiology: Cardiothoracic Imaging. T.P.S. Research support from Canon Medical Systems and GE Healthcare; consulting fees from Alara Imaging, Imalogix, Flowhow, Aidoc, and Asto CT/Leo Cancer Care; royalties from Medical Physics Publishing and royalties related to intellectual property from Flowhow and Qaelum. N.R.v.d.W. No relevant relationships. A.S.W. Research support from GE Healthcare, Siemens Healthineers, Varex Imaging, and the National Institutes of Health. V.S. No relevant relationships. A.J.v.d.M. Royalties from Thieme Verlag; payment for lectures from Guerbet; support for travel from Guerbet. D.F. Grant to institution from Siemens Healthineers; deputy editor for Radiology: Cardiothoracic Imaging. M.J.W. Research grant from the American Heart Association; consulting fees from Segmed; payment for expert testimony from Guidepoint; unpaid member of the Society of Cardiovascular Computed Tomography Corporate Relations Committee; stock or stock options in Segmed.

Publisher Copyright:
© RSNA, 2023.

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