Task-specific spatial resolution properties of iterative and deep learning-based reconstructions in computed tomography: Comparison using tasks assuming small and large enhanced vessels.

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Published Date: Feb 2, 2022

Abstract

PURPOSE: The present study aims to evaluate TTFs of deep-learning-based image reconstruction (DLIR) and iterative reconstruction (IR) in computed tomography (CT) using a conventional task with a rod object with a diameter of 30 mm and a newly-proposed task with a wire of 1 mm in diameter, simulating large and small enhanced vessels, respectively.

Authors

  • Kanae Matsuura
    Dept of Radiological Technology, Faculty of Health Science, Suzuka University of Medical Science, 1001-1 Kishioka-cho, Suzuka 510-0293, Japan; Division of Health Sciences, Graduate School of Medical Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, 920-0942, Japan. Electronic address: k-matsu@suzuka-u.ac.jp.
  • Katsuhiro Ichikawa
    Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University.
  • Hiroki Kawashima
    Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, 5-11-80 Kodatsuno, Kanazawa, 920-0942, Japan. Electronic address: kawa3@med.kanazawa-u.ac.jp.

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