Nuclear Engineering and Technology

  • 제55권1호
  • /
  • Pages.222-228
  • /
  • 2023
  • /
  • 1738-5733(pISSN)
  • /
  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
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Feasibility study of improved median filtering in PET/MR fusion images with parallel imaging using generalized autocalibrating partially parallel acquisition

  • Chanrok Park (Department of Radiological Science, Eulji University) ;
  • Jae-Young Kim (Department of Biochemistry, School of Dentistry, IHBR, Kyungpook National University) ;
  • Chang-Hyeon An (Department of Oral and Maxillofacial Radiology, School of Dentistry, IHBR, Kyungpook National University) ;
  • Youngjin Lee (Department of Radiological Science, Gachon University)
  • 투고 : 2022.08.08
  • 심사 : 2022.09.15
  • 발행 : 2023.01.25
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초록

This study aimed to analyze the applicability of the improved median filter in positron emission tomography (PET)/magnetic resonance (MR) fusion images based on parallel imaging using generalized autocalibrating partially parallel acquisition (GRAPPA). In this study, a PET/MR fusion imaging system based on a 3.0T magnetic field and 18F radioisotope were used. An improved median filter that can set a mask of the median value more efficiently than before was modeled and applied to the acquired image. As quantitative evaluation parameters of the noise level, the contrast to noise ratio (CNR) and coefficient of variation (COV) were calculated. Additionally, no-reference-based evaluation parameters were used to analyze the overall image quality. We confirmed that the CNR and COV values of the PET/MR fusion images to which the improved median filter was applied improved by approximately 3.32 and 2.19 times on average, respectively, compared to the noisy image. In addition, the no-reference-based evaluation results showed a similar trend for the noise-level results. In conclusion, we demonstrated that it can be supplemented by using an improved median filter, which suggests the problem of image quality degradation of PET/MR fusion images that shortens scan time using GRAPPA.

키워드

과제정보

This work was supported by the National Research Foundation of Korea (NRF-2021R1F1A1061440).

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