Assessment of image quality and impact of deep learning-based software in non-contrast head CT scans.
Sci Rep
; 14(1): 11810, 2024 05 23.
Article
en En
| MEDLINE
| ID: mdl-38782976
ABSTRACT
In this retrospective study, we aimed to assess the objective and subjective image quality of different reconstruction techniques and a deep learning-based software on non-contrast head computed tomography (CT) images. In total, 152 adult head CT scans (77 female, 75 male; mean age 69.4 ± 18.3 years) obtained from three different CT scanners using different protocols between March and April 2021 were included. CT images were reconstructed using filtered-back projection (FBP), iterative reconstruction (IR), and post-processed using a deep learning-based algorithm (PS). Post-processing significantly reduced noise in FBP-reconstructed images (up to 15.4% reduction) depending on the protocol, leading to improvements in signal-to-noise ratio of up to 19.7%. However, when deep learning-based post-processing was applied to FBP images compared to IR alone, the differences were inconsistent and partly non-significant, which appeared to be protocol or site specific. Subjective assessments showed no significant overall improvement in image quality for all reconstructions and post-processing. Inter-rater reliability was low and preferences varied. Deep learning-based denoising software improved objective image quality compared to FBP in routine head CT. A significant difference compared to IR was observed for only one protocol. Subjective assessments did not indicate a significant clinical impact in terms of improved subjective image quality, likely due to the low noise levels in full-dose images.
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Programas Informáticos
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Tomografía Computarizada por Rayos X
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Aprendizaje Profundo
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Cabeza
Límite:
Adult
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Aged
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Aged80
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Sci Rep
/
Sci. rep. (Nat. Publ. Group)
/
Scientific reports (Nature Publishing Group)
Año:
2024
Tipo del documento:
Article
País de afiliación:
Alemania