Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm.
computed tomography
deep learning
image quality
image reconstruction
noise power spectrum
Journal
Medical physics
ISSN: 2473-4209
Titre abrégé: Med Phys
Pays: United States
ID NLM: 0425746
Informations de publication
Date de publication:
Sep 2020
Sep 2020
Historique:
received:
17
02
2020
revised:
01
05
2020
accepted:
26
05
2020
pubmed:
9
6
2020
medline:
15
5
2021
entrez:
8
6
2020
Statut:
ppublish
Résumé
To characterize the noise and spatial resolution properties of a commercially available deep learning-based computed tomography (CT) reconstruction algorithm. Two phantom experiments were performed. The first used a multisized image quality phantom (Mercury v3.0, Duke University) imaged at five radiation dose levels (CTDI Compared to FBP, noise magnitude was reduced on average (± one standard deviation) by 74 ± 6% and 68 ± 4% for ASiR-V (at "100%" setting) and True Fidelity (at "High" setting), respectively. The noise texture from ASiR-V had substantially lower noise frequency content with 55 ± 4% lower NPS f The deep learning-based CT reconstruction demonstrated a strong noise magnitude reduction compared to FBP while maintaining similar noise texture and high-contrast spatial resolution. However, the algorithm resulted in images with a locally nonstationary noise in lung textured backgrounds and had somewhat degraded low-contrast spatial resolution similar to what has been observed in currently available iterative reconstruction techniques.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3961-3971Informations de copyright
© 2020 American Association of Physicists in Medicine.
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