The future of CT: deep learning reconstruction
There have been substantial advances in computed tomography (CT) technology since its introduction in the 1970s. More recently, these advances have focused on image reconstruction. Deep learning reconstruction (DLR) is the latest complex reconstruction algorithm to be introduced, which harnesses adv...
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| Published in: | Clinical radiology Vol. 76; no. 6; pp. 407 - 415 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
England
Elsevier Ltd
01-06-2021
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| Online Access: | Get full text |
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| Summary: | There have been substantial advances in computed tomography (CT) technology since its introduction in the 1970s. More recently, these advances have focused on image reconstruction. Deep learning reconstruction (DLR) is the latest complex reconstruction algorithm to be introduced, which harnesses advances in artificial intelligence (AI) and affordable supercomputer technology to achieve the previously elusive triad of high image quality, low radiation dose, and fast reconstruction speeds. The dose reductions achieved with DLR are redefining ultra-low-dose into the realm of plain radiographs whilst maintaining image quality. This review aims to demonstrate the advantages of DLR over other reconstruction methods in terms of dose reduction and image quality in addition to being able to tailor protocols to specific clinical situations. DLR is the future of CT technology and should be considered when procuring new scanners. |
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| Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-3 content type line 23 ObjectType-Review-1 |
| ISSN: | 0009-9260 1365-229X |
| DOI: | 10.1016/j.crad.2021.01.010 |