High quality imaging from sparsely sampled computed tomography data with deep learning and wavelet transform in various domains.

Journal: Medical physics
Published Date: Nov 28, 2018

Abstract

PURPOSE: Sparsely sampled computed tomography (CT) has been attracting attention as a technique that can reduce the high radiation dose of conventional CT. In general, iterative reconstruction techniques have been applied to sparsely sampled CT to realize high quality images. These methodologies require high computing power due to the modeling of the system and the trajectory of radiation rays. Therefore, the purpose of this study was to obtain high quality three-dimensional (3D) reconstructed images with deep learning under sparse sampling conditions.

Authors

  • Donghoon Lee
    Department of Radiation Convergence Engineering, Research Institute of Health Science, Yonsei Univeristy, 1 Yonseidae-gil, Wonju, Gangwon, 26493, Korea.
  • Sunghoon Choi
    Department of Radiological Science, College of Health Science, Yonsei University, 1 Yonseidae-gil, Wonju, Gangwon, 26493, Korea.
  • Hee-Joung Kim
    Department of Radiation Convergence Engineering, College of Health Science, Yonsei University, 1 Yonseidae-gil, Wonju, Gangwon 220-710, Republic of Korea; Department of Radiological Science, College of Health Science, Yonsei University, 1 Yonseidae-gil, Wonju, Gangwon 220-710, Republic of Korea. Electronic address: hjk1@yonsei.ac.kr.

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