Super-resolution deep-learning reconstruction for cardiac CT: impact of radiation dose and focal spot size on task-based image quality.
Multidetector computed tomography
Spatial resolution
Super-resolution deep learning image reconstruction algorithm
Task-based image quality assessment
Journal
Physical and engineering sciences in medicine
ISSN: 2662-4737
Titre abrégé: Phys Eng Sci Med
Pays: Switzerland
ID NLM: 101760671
Informations de publication
Date de publication:
17 Jun 2024
17 Jun 2024
Historique:
received:
07
09
2023
accepted:
04
04
2024
medline:
17
6
2024
pubmed:
17
6
2024
entrez:
17
6
2024
Statut:
aheadofprint
Résumé
This study aimed to evaluate the impact of radiation dose and focal spot size on the image quality of super-resolution deep-learning reconstruction (SR-DLR) in comparison with iterative reconstruction (IR) and normal-resolution DLR (NR-DLR) algorithms for cardiac CT. Catphan-700 phantom was scanned on a 320-row scanner at six radiation doses (small and large focal spots at 1.4-4.3 and 5.8-8.8 mGy, respectively). Images were reconstructed using hybrid-IR, model-based-IR, NR-DLR, and SR-DLR algorithms. Noise properties were evaluated through plotting noise power spectrum (NPS). Spatial resolution was quantified with task-based transfer function (TTF); Polystyrene, Delrin, and Bone-50% inserts were used for low-, intermediate, and high-contrast spatial resolution. The detectability index (d') was calculated. Image noise, noise texture, edge sharpness of low- and intermediate-contrast objects, delineation of fine high-contrast objects, and overall quality of four reconstructions were visually ranked. Results indicated that among four reconstructions, SR-DLR yielded the lowest noise magnitude and NPS peak, as well as the highest average NPS frequency, TTF
Identifiants
pubmed: 38884668
doi: 10.1007/s13246-024-01423-y
pii: 10.1007/s13246-024-01423-y
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Japan Society for the Promotion of Science KAKENHI
ID : 23K14873
Informations de copyright
© 2024. Australasian College of Physical Scientists and Engineers in Medicine.
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