Evaluation of deep-learning image reconstruction for chest CT examinations at two different dose levels.

Journal: Journal of applied clinical medical physics
Published Date: Dec 30, 2022

Abstract

AIMS: The aims of the present study were to, for both a full-dose protocol and an ultra-low dose (ULD) protocol, compare the image quality of chest CT examinations reconstructed using TrueFidelity (Standard kernel) with corresponding examinations reconstructed using ASIR-V (Lung kernel) and to evaluate if post-processing using an edge-enhancement filter affects the noise level, spatial resolution and subjective image quality of clinical images reconstructed using TrueFidelity.

Authors

  • Angelica Svalkvist
    Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden.
  • Erika Fagman
    Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
  • Jenny Vikgren
    Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
  • Sara Ku
    Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
  • Micael Oliveira Diniz
    Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
  • Rauni Rossi Norrlund
    Department of Radiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
  • Åse A Johnsson
    Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.

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