Comparison of image quality between Deep learning image reconstruction and Iterative reconstruction technique for CT Brain- a pilot study.


Journal

F1000Research
ISSN: 2046-1402
Titre abrégé: F1000Res
Pays: England
ID NLM: 101594320

Informations de publication

Date de publication:
2024
Historique:
accepted: 12 06 2024
medline: 4 7 2024
pubmed: 4 7 2024
entrez: 4 7 2024
Statut: epublish

Résumé

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 (iDose This is a prospective study. Thirty patients who underwent NCCT brain were included. The images were reconstructed using DLIR-standard and iDose Quantitative IQ analysis showed significant differences (p<0.05) in IN, SNR, and CNR between the iDose DLIR standard yielded superior quantitative and qualitative IQ compared to the IR technique (iDose4). The DLIR-standard significantly reduced the IN and artifacts compared to iDose

Sections du résumé

Background UNASSIGNED
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 (iDose
Methods UNASSIGNED
This is a prospective study. Thirty patients who underwent NCCT brain were included. The images were reconstructed using DLIR-standard and iDose
Results UNASSIGNED
Quantitative IQ analysis showed significant differences (p<0.05) in IN, SNR, and CNR between the iDose
Conclusion UNASSIGNED
DLIR standard yielded superior quantitative and qualitative IQ compared to the IR technique (iDose4). The DLIR-standard significantly reduced the IN and artifacts compared to iDose

Identifiants

pubmed: 38962692
doi: 10.12688/f1000research.150773.1
pmc: PMC11221345
doi:

Banques de données

figshare
['10.6084/m9.figshare.25658829.v7']

Types de publication

Journal Article Comparative Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

691

Informations de copyright

Copyright: © 2024 Chandran M O et al.

Déclaration de conflit d'intérêts

No competing interests were disclosed.

Auteurs

Obhuli Chandran M (O)

Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.

Saikiran Pendem (S)

Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.

Priya P S (P)

Department of Radio Diagnosis and Imaging, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.

Cijo Chacko (C)

Clinical Scientist, Philips Research and Development, Philips innovation campus, Yelahanka, Karnataka, 560064, India.
Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.

Rajagopal Kadavigere (R)

Department of Radio Diagnosis and Imaging, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.

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