dc.contributor.author | Njølstad, Tormund Salvesen | |
dc.contributor.author | Jensen, Kristin | |
dc.contributor.author | Dybwad, Anniken | |
dc.contributor.author | Salvesen, Øyvind | |
dc.contributor.author | Andersen, Hilde Kjernlie | |
dc.contributor.author | Schulz, Anselm | |
dc.date.accessioned | 2023-02-06T14:49:15Z | |
dc.date.available | 2023-02-06T14:49:15Z | |
dc.date.created | 2022-04-28T16:59:50Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | European Journal of Radiology Open (EJR Open). 2022, 9 1-8. | en_US |
dc.identifier.issn | 2352-0477 | |
dc.identifier.uri | https://hdl.handle.net/11250/3048674 | |
dc.description.abstract | Background
A novel deep learning image reconstruction (DLIR) algorithm for CT has recently been clinically approved.
Purpose
To assess low-contrast detectability and dose reduction potential for CT images reconstructed with the DLIR algorithm and compare with filtered back projection (FBP) and hybrid iterative reconstruction (IR).
Material and methods
A customized upper-abdomen phantom containing four cylindrical liver inserts with low-contrast lesions was scanned at CT dose indexes of 5, 10, 15, 20 and 25 mGy. Images were reconstructed with FBP, 50% hybrid IR (IR50), and DLIR of low strength (DLL), medium strength (DLM) and high strength (DLH). Detectability was assessed by 20 independent readers using a two-alternative forced choice approach. Dose reduction potential was estimated separately for each strength of DLIR using a fitted model, with the detectability performance of FBP and IR50 as reference.
Results
For the investigated dose levels of 5 and 10 mGy, DLM improved detectability compared to FBP by 5.8 and 6.9 percentage points (p.p.), and DLH improved detectability by 9.6 and 12.3 p.p., respectively (all p < .007). With IR50 as reference, DLH improved detectability by 5.2 and 9.8 p.p. for the 5 and 10 mGy dose level, respectively (p < .03). With respect to this low-contrast detectability task, average dose reduction potential relative to FBP was estimated to 39% for DLM and 55% for DLH. Relative to IR50, average dose reduction potential was estimated to 21% for DLM and 42% for DLH.
Conclusions:
Low-contrast detectability performance is improved when applying a DLIR algorithm, with potential for radiation dose reduction. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Low-contrast detectability and potential for radiation dose reduction using deep learning image reconstruction—A 20-reader study on a semi-anthropomorphic liver phantom | en_US |
dc.title.alternative | Low-contrast detectability and potential for radiation dose reduction using deep learning image reconstruction—A 20-reader study on a semi-anthropomorphic liver phantom | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.pagenumber | 1-8 | en_US |
dc.source.volume | 9 | en_US |
dc.source.journal | European Journal of Radiology Open (EJR Open) | en_US |
dc.identifier.doi | 10.1016/j.ejro.2022.100418 | |
dc.identifier.cristin | 2019918 | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |