Investigation on slice direction dependent denoising performance of convolutional neural network in cone-beam CT images

Eunhyeok Lee, Byeongjoon Kim, Jongduk Baek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In FDK reconstruction, distribution of noise power is different along the axial (i.e., high pass noise) and coronal slice (i.e., low pass or white noise), which may results in different detectability of same objects. In this work, we examined denoising performance of convolutional neural network trained using axial and coronal slice images separately, and how the direction of image slice affects the detectability of small objects in denoised images. We used the modified version of U-Net. For network training, we used Adam optimizer with a learning rate of 0.001, batch size of 4, and VGG loss was used. The XCAT simulator was used to generate the training, validation, and test dataset. Projection data was acquired by Siddon's method for the XCAT phantoms, and different levels of Poisson noise was added to the projection data to generate quarter dose and normal dose CT images, which were then reconstructed by FDK algorithm. The reconstructed quarter dose and normal dose CT images were used as training, validation, and test dataset for our network. The performance of denoised output images from U-Net-Axial (i.e., network trained using axial images) and U-Net-Coronal (i.e., network trained using coronal images) were evaluated using structural similarity (SSIM) and mean square error (MSE). The results showed that output images from both U-Net-Axial and U-Net-Coronal shows the improved image quality compared to quarter dose images. However, it was observed that the detectability of small objects were higher in U-Net-Coronal.

Original languageEnglish
Title of host publicationMedical Imaging 2019
Subtitle of host publicationPhysics of Medical Imaging
EditorsTaly Gilat Schmidt, Guang-Hong Chen, Hilde Bosmans
PublisherSPIE
ISBN (Electronic)9781510625433
DOIs
Publication statusPublished - 2019 Jan 1
EventMedical Imaging 2019: Physics of Medical Imaging - San Diego, United States
Duration: 2019 Feb 172019 Feb 20

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10948
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Physics of Medical Imaging
CountryUnited States
CitySan Diego
Period19/2/1719/2/20

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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    Lee, E., Kim, B., & Baek, J. (2019). Investigation on slice direction dependent denoising performance of convolutional neural network in cone-beam CT images. In T. G. Schmidt, G-H. Chen, & H. Bosmans (Eds.), Medical Imaging 2019: Physics of Medical Imaging [1094848] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10948). SPIE. https://doi.org/10.1117/12.2512186