Pulmonary nodule volumetric accuracy of a deep learning-based reconstruction algorithm in low-dose computed tomography: A phantom study.

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)
PMID: 36347080

Abstract

PURPOSE: To compare the image properties and pulmonary nodule volumetric accuracies among deep learning-based reconstruction (DLR), filtered back projection (FBP), and hybrid iterative reconstruction (hybrid IR) in low-dose computed tomography (LDCT).

Authors

  • Shota Watanabe
    Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University Hospital, 377-2 Ohno-Higashi, Osakasayama, Osaka, 589-8511, Japan; Radiology Center, Kindai University Hospital, 377-2 Ohno-Higashi, Osakasayama, Osaka, 589-8511, Japan. Electronic address: shouta-w@med.kindai.ac.jp.
  • Kenta Sakaguchi
    Radiology Center, Kindai University Hospital, 377-2 Ohnohigashi, Osakasayama, Osaka, 589-8511, Japan. sakaguchi_kenta@med.kindai.ac.jp.
  • Shigetoshi Kitaguchi
    Radiology Center, Kindai University Hospital, 377-2 Ohno-Higashi, Osakasayama, Osaka 589-8511, Japan. Electronic address: s-kitaguchi@med.kindai.ac.jp.
  • Kazunari Ishii
    Division of Positron Emission Tomography, Institute of Advanced Clinical Medicine, Kindai University, Osaka, Japan.

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