Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Improvement of signal and noise performance using single image super-resolution based on deep learning in single photon-emission computed tomography imaging system

  • Kim, Kyuseok (Department of Intergrative Medicine, Major in Digital Healthcare, Yonsei University) ;
  • Lee, Youngjin (Department of Radiological Science, Gachon University)
  • Received : 2020.10.23
  • Accepted : 2021.01.09
  • Published : 2021.07.25
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Abstract

Because single-photon emission computed tomography (SPECT) is one of the widely used nuclear medicine imaging systems, it is extremely important to acquire high-quality images for diagnosis. In this study, we designed a super-resolution (SR) technique using dense block-based deep convolutional neural network (CNN) and evaluated the algorithm on real SPECT phantom images. To acquire the phantom images, a real SPECT system using a99mTc source and two physical phantoms was used. To confirm the image quality, the noise properties and visual quality metric evaluation parameters were calculated. The results demonstrate that our proposed method delivers a more valid SR improvement by using dense block-based deep CNNs as compared to conventional reconstruction techniques. In particular, when the proposed method was used, the quantitative performance was improved from 1.2 to 5.0 times compared to the result of using the conventional iterative reconstruction. Here, we confirmed the effects on the image quality of the resulting SR image, and our proposed technique was shown to be effective for nuclear medicine imaging.

Keywords

Acknowledgement

We would like to thank Prof. Chan Rok Park for helping us acquire the phantom data.

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