1. Improvement of signal and noise performance using single image super-resolution based on deep learning in single photon-emission computed tomography imaging system
- Author
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Kyuseok Kim and Youngjin Lee
- Subjects
Image quality ,Computer science ,020209 energy ,Single photon emission computed tomography ,Quantitative evaluation of image quality ,02 engineering and technology ,Iterative reconstruction ,Single-photon emission computed tomography ,Convolutional neural network ,Imaging phantom ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Computer vision ,medicine.diagnostic_test ,business.industry ,Noise (signal processing) ,Deep learning ,TK9001-9401 ,Nuclear medicine imaging ,Nuclear Energy and Engineering ,Super-resolution ,Nuclear engineering. Atomic power ,Artificial intelligence ,Deep convolutional neural network ,business ,Emission computed tomography - 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.
- Published
- 2021
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