Brain image quality according to beam collimation width and image reconstruction algorithm: A phantom study.
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State: Public
Version: author
License: Not specified
Serval ID
serval:BIB_F4F0337CF2E2
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Brain image quality according to beam collimation width and image reconstruction algorithm: A phantom study.
Journal
Physica medica
ISSN
1724-191X (Electronic)
ISSN-L
1120-1797
Publication state
Published
Issued date
04/2023
Peer-reviewed
Oui
Volume
108
Pages
102558
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Publication Status: ppublish
Abstract
To compare quantitatively and qualitatively brain image quality acquired in helical and axial modes on two wide collimation CT systems according to the dose level and algorithm used.
Acquisitions were performed on an image quality and an anthropomorphic phantoms at three dose levels (CTDI <sub>vol</sub> : 45/35/25 mGy) on two wide collimation CT systems (GE Healthcare and Canon Medical Systems) in axial and helical modes. Raw data were reconstructed using iterative reconstruction (IR) and deep-learning image reconstruction (DLR) algorithms. The noise power spectrum (NPS) was computed on both phantoms and the task-based transfer function (TTF) on the image quality phantom. The subjective quality of images from an anthropomorphic brain phantom was evaluated by two radiologists including overall image quality.
For the GE system, noise magnitude and noise texture (average NPS spatial frequency) were lower with DLR than with IR. For the Canon system, noise magnitude values were lower with DLR than with IR for similar noise texture but the opposite was true for spatial resolution. For both CT systems, noise magnitude was lower with the axial mode than with the helical mode for similar noise texture and spatial resolution. Radiologists rated the overall quality of all brain images as "satisfactory for clinical use", whatever the dose level, algorithm or acquisition mode.
Using 16-cm axial acquisition reduces image noise without changing the spatial resolution and image texture compared to helical acquisitions. Axial acquisition can be used in clinical routine for brain CT examinations with an explored length of less than 16 cm.
Acquisitions were performed on an image quality and an anthropomorphic phantoms at three dose levels (CTDI <sub>vol</sub> : 45/35/25 mGy) on two wide collimation CT systems (GE Healthcare and Canon Medical Systems) in axial and helical modes. Raw data were reconstructed using iterative reconstruction (IR) and deep-learning image reconstruction (DLR) algorithms. The noise power spectrum (NPS) was computed on both phantoms and the task-based transfer function (TTF) on the image quality phantom. The subjective quality of images from an anthropomorphic brain phantom was evaluated by two radiologists including overall image quality.
For the GE system, noise magnitude and noise texture (average NPS spatial frequency) were lower with DLR than with IR. For the Canon system, noise magnitude values were lower with DLR than with IR for similar noise texture but the opposite was true for spatial resolution. For both CT systems, noise magnitude was lower with the axial mode than with the helical mode for similar noise texture and spatial resolution. Radiologists rated the overall quality of all brain images as "satisfactory for clinical use", whatever the dose level, algorithm or acquisition mode.
Using 16-cm axial acquisition reduces image noise without changing the spatial resolution and image texture compared to helical acquisitions. Axial acquisition can be used in clinical routine for brain CT examinations with an explored length of less than 16 cm.
Keywords
Tomography, X-Ray Computed/methods, Image Processing, Computer-Assisted, Algorithms, Phantoms, Imaging, Brain, Radiation Dosage, Radiographic Image Interpretation, Computer-Assisted/methods, Computed tomography, Deep-learning image reconstruction algorithm, Wide collimation CT system
Pubmed
Web of science
Create date
20/03/2023 10:56
Last modification date
03/09/2024 15:52