Brain image quality according to beam collimation width and image reconstruction algorithm: A phantom study

Joël Greffier; Anaïs Viry; Quentin Durand; Steven David Hajdu; Julien Frandon; Jean Paul Beregi; Djamel Dabli; Damien Racine

doi:10.1016/j.ejmp.2023.102558

Brain image quality according to beam collimation width and image reconstruction algorithm: A phantom study

Phys Med. 2023 Apr:108:102558. doi: 10.1016/j.ejmp.2023.102558. Epub 2023 Mar 9.

Authors

Joël Greffier¹, Anaïs Viry², Quentin Durand³, Steven David Hajdu⁴, Julien Frandon³, Jean Paul Beregi³, Djamel Dabli³, Damien Racine²

Affiliations

¹ Department of Medical Imaging, CHU Nîmes, Univ Montpellier, Nîmes Medical Imaging Group, EA 2992, France. Electronic address: joel.greffier@chu-nimes.fr.
² Institute of Radiation Physics, Lausanne University Hospital and University of Lausanne, Rue du Grand-Pré 1, 1007 Lausanne, Switzerland.
³ Department of Medical Imaging, CHU Nîmes, Univ Montpellier, Nîmes Medical Imaging Group, EA 2992, France.
⁴ Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Rue du Bugnon 46, 1011 Lausanne, Switzerland.

PMID: 36905775
DOI: 10.1016/j.ejmp.2023.102558

Abstract

Purpose: 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.

Methods: Acquisitions were performed on an image quality and an anthropomorphic phantoms at three dose levels (CTDI_vol: 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.

Results: 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.

Conclusions: 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: Brain; Computed tomography; Deep-learning image reconstruction algorithm; Wide collimation CT system.

MeSH terms

Algorithms
Brain
Image Processing, Computer-Assisted*
Phantoms, Imaging
Radiation Dosage
Radiographic Image Interpretation, Computer-Assisted / methods
Tomography, X-Ray Computed* / methods