| Autor: |
Cijo Chacko, Priya P S, Priyanka ,, Obhuli Chandran M, Saikiran Pendem, Rajagopal Kadavigere |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
F1000Research, Vol 13 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2046-1402 |
| DOI: |
10.12688/f1000research.150773.1 |
| Popis: |
Background Non-contrast Computed Tomography (NCCT) plays a pivotal role in assessing central nervous system disorders and is a crucial diagnostic method. Iterative reconstruction (IR) methods have enhanced image quality (IQ) but may result in a blotchy appearance and decreased resolution for subtle contrasts. The deep-learning image reconstruction (DLIR) algorithm, which integrates a convolutional neural network (CNN) into the reconstruction process, generates high-quality images with minimal noise. Hence, the objective of this study was to assess the IQ of the Precise Image (DLIR) and the IR technique (iDose4) for the NCCT brain. Methods This is a prospective study. Thirty patients who underwent NCCT brain were included. The images were reconstructed using DLIR-standard and iDose4. Qualitative IQ analysis parameters, such as overall image quality (OQ), subjective image noise (SIN), and artifacts, were measured. Quantitative IQ analysis parameters such as Computed Tomography (CT) attenuation (HU), image noise (IN), posterior fossa index (PFI), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in the basal ganglia (BG) and centrum-semiovale (CSO) were measured. Paired t-tests were performed for qualitative and quantitative IQ analyses between the iDose4 and DLIR-standard. Kappa statistics were used to assess inter-observer agreement for qualitative analysis. Results Quantitative IQ analysis showed significant differences (p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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