| Autor: |
Tormund Njølstad, Kristin Jensen, Anniken Dybwad, Øyvind Salvesen, Hilde K. Andersen, Anselm Schulz |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
European Journal of Radiology Open, Vol 9, Iss , Pp 100418- (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2352-0477 |
| DOI: |
10.1016/j.ejro.2022.100418 |
| Popis: |
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 |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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