대한방사선기술학회지:방사선기술과학 (Journal of radiological science and technology)

  • 제43권6호
  • /
  • Pages.461-467
  • /
  • 2020
  • /
  • 2288-3509(pISSN)
  • /
  • 2384-1168(eISSN)
대한방사선과학회 (Korean Society of Radiological Science)
권호 표지
DOI QR코드

DOI QR Code

핵의학 감마카메라 정도관리의 딥러닝 적용

Deep Learning Application of Gamma Camera Quality Control in Nuclear Medicine

  • 투고 : 2020.11.07
  • 심사 : 2020.12.13
  • 발행 : 2020.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In the field of nuclear medicine, errors are sometimes generated because the assessment of the uniformity of gamma cameras relies on the naked eye of the evaluator. To minimize these errors, we created an artificial intelligence model based on CNN algorithm and wanted to assess its usefulness. We produced 20,000 normal images and partial cold region images using Python, and conducted artificial intelligence training with Resnet18 models. The training results showed that accuracy, specificity and sensitivity were 95.01%, 92.30%, and 97.73%, respectively. According to the results of the evaluation of the confusion matrix of artificial intelligence and expert groups, artificial intelligence was accuracy, specificity and sensitivity of 94.00%, 91.50%, and 96.80%, respectively, and expert groups was accuracy, specificity and sensitivity of 69.00%, 64.00%, and 74.00%, respectively. The results showed that artificial intelligence was better than expert groups. In addition, by checking together with the radiological technologist and AI, errors that may occur during the quality control process can be reduced, providing a better examination environment for patients, providing convenience to radiologists, and improving work efficiency.

키워드

참고문헌

  1. Choi KT. Real-time artificial neural network for high-dimensional medical image. Journal of the Korean Society of Radiology. 2016;10(8):637-43. https://doi.org/10.7742/jksr.2016.10.8.637
  2. Ravi D, Wong C, Deligianni F, et al. Deep learning for health informatics. IEEE Journal of Biomedical and Health Informatics. 2017;21(1):4-21. https://doi.org/10.1109/JBHI.2016.2636665
  3. LeCun Y, Boser B, Denker JS, et al. Backpropagation applied to handwritten zip code recognition. Neural Computation. 1989;1(4):541-51. https://doi.org/10.1162/neco.1989.1.4.541
  4. Li J, Mi Y, Li G, Ju Z. CNN-based facial expression recognition from annotated RGB-D images for human-robot interaction. International Journal of Humanoid Robotics. 2019;16(4):1941002. https://doi.org/10.1142/s0219843619410020
  5. Choi JG, Lee BI. Research for development of standardized system for quality control of nuclear medicine imaging equipments. The Annual Report of Korea Food & Drug administration (ABSTRACT). 2008;12(11-14700000-000071-10):859-60.
  6. Choe JG, Joh CW. Survey of current status of quality control of gamma cameras in republic of Korea. Nuclear Medicine and Molecular Imaging. 2008; 42(4):314-22.
  7. Choi WC. Actual condition of computerized tomography system in use in Seoul and projection evaluation using AAPM CT Phantom [master's thesis]. Korea University, Korea; 2009.
  8. Lim JJ, Kim HK, Kim JP, Jo SW, Kim JE. Evaluation of radiation exposure to medical staff except nuclear medicine department. The Korean Journal of Nuclear Medicine Technology. 2016;20(2):32-5.
  9. Noh SS, Um HS, Kim HC. Development of automatized quantitative analysis method in CT images evaluation using AAPM phantom. The Institute of Electronics and Information Engineers. 2014;51(12): 163-73.
  10. The International Atomic Energy Agency. Quality control of nuclear medicine instruments. IAEA-TECDOC-317; 1984.
  11. National Physical Laboratory. Protocol for establishing and maintaining the calibration of medical radionuclide calibrators and their quality control. A National Measurement Good Practice Guide; 2006:93.
  12. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. axRiv(Computer Vision and Pattern Rescognition). 2015;1512.03385.
  13. Park HK. Artificial intelligence (AI) health care industry status and trends. Convergence Focus, 2019;148:2-9.
  14. Lee HH. CNN Generalization Error Evaluation Method [master's thesis]. Pusan University, Korea; 2020.
  15. Park JK. Medical clinics' quality management of X-ray units in Gyeongbuk area. The Journal of the Korea Contents Association. 2010;10(9):267-75. https://doi.org/10.5392/JKCA.2010.10.9.267
  16. Larson DB, Boland GW. Imaging quality control in the era of artificial intelligence. Journal of the American College of Radiology. 2019;16(9):1259-66. https://doi.org/10.1016/j.jacr.2019.05.048