Ultra-low dose chest CT with silver filter and deep learning reconstruction significantly reduces radiation dose and retains quantitative information in the investigation and monitoring of lymphangioleiomyomatosis (LAM).

Journal: European radiology
Published Date: Feb 22, 2024

Abstract

PURPOSE: Frequent CT scans to quantify lung involvement in cystic lung disease increases radiation exposure. Beam shaping energy filters can optimize imaging properties at lower radiation dosages. The aim of this study is to investigate whether use of SilverBeam filter and deep learning reconstruction algorithm allows for reduced radiation dose chest CT scanning in patients with lymphangioleiomyomatosis (LAM).

Authors

  • Alexa E Golbus
    Cardiovascular Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, 10 Center Dr, MSC 1046, Building 10, Room B1D47, Bethesda, MD, 20892, USA.
  • Chloe Steveson
    Canon Medical Systems, Otawara, Japan.
  • John L Schuzer
    Canon Medical Systems, Otawara, Japan.
  • Shirley F Rollison
    Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, USA.
  • Tat'Yana Worthy
    Office of the Clinical Director, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, USA.
  • Amanda M Jones
    Critical Care Medicine and Pulmonary Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, USA.
  • Patricia Julien-Williams
    Critical Care Medicine and Pulmonary Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, USA.
  • Joel Moss
    Critical Care Medicine and Pulmonary Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, USA.
  • Marcus Y Chen
    National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland.

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