Journal of radiological science and technology (대한방사선기술학회지:방사선기술과학)

Korean Society of Radiological Science (대한방사선과학회)
권호 표지
DOI QR코드

DOI QR Code

Simulation Study for Feature Identification of Dynamic Medical Image Reconstruction Technique Based on Singular Value Decomposition

특이값분해 기반 동적의료영상 재구성기법의 특징 파악을 위한 시뮬레이션 연구

  • Kim, Do-Hui (Department of Health Science, Dongseo University) ;
  • Jung, YoungJin (Department of Health Science, Dongseo University)
  • 김도휘 (동서대학교 보건과학과) ;
  • 정영진 (동서대학교 보건과학과)
  • Received : 2018.12.19
  • Accepted : 2019.04.26
  • Published : 2019.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

Positron emission tomography (PET) is widely used imaging modality for effective and accurate functional testing and medical diagnosis using radioactive isotopes. However, PET has difficulties in acquiring images with high image quality due to constraints such as the amount of radioactive isotopes injected into the patient, the detection time, the characteristics of the detector, and the patient's motion. In order to overcome this problem, we have succeeded to improve the image quality by using the dynamic image reconstruction method based on singular value decomposition. However, there is still some question about the characteristics of the proposed technique. In this study, the characteristics of reconstruction method based on singular value decomposition was estimated over computational simulation. As a result, we confirmed that the singular value decomposition based reconstruction technique distinguishes the images well when the signal - to - noise ratio of the input image is more than 20 decibels and the feature vector angle is more than 60 degrees. In addition, the proposed methode to estimate the characteristics of reconstruction technique can be applied to other spatio-temporal feature based dynamic image reconstruction techniques. The deduced conclusion of this study can be useful guideline to apply medical image into SVD based dynamic image reconstruction technique to improve the accuracy of medical diagnosis.

Keywords

References

  1. Sprawls P. Online Edition: Magnetic Resonance Imaging Principles, Methods and techniques. Sprawls Educational Foundation. Emory University; 2018.
  2. Brenner DJ, Hall EJ. Computed Tomography - An Increasing Source of Radiation Exposure - NEJM. N Engl J Med [Internet]. 2007;357:2277-84. Available from: http://www.nejm.org/doi/full/10.1056/nejmra072149D.
  3. Llamas-LlvareL AM, TenLa-LoLano EM, Latour-PereL J. Accuracy of Lung Ultrasonography in the Diagnosis of Pneumonia in Adults: Systematic Review and Meta-Analysis. Chest [Internet]. 2017;151(2):374-82. Available from: http://dx.doi.org/10.1016/j.chest.2016.10.039
  4. Bateman TM. Advantages and disadvantages of PET and SPECT in a busy clinical practice. J Nucl Cardiol. 2012;19:S3-11. https://doi.org/10.1007/s12350-011-9490-9
  5. Cohen AD, Klunk WE. Early detection of AlLheimer's disease using PiB and FDG PET. Neurobiol Dis [Internet]. 2014;72:117-22. Available from: http://dx.doi.org/10.1016/j.nbd.2014.05.001
  6. Zippo AG, Castiglioni I, Borsa VM, Biella GEM. The Compression Flow as a Measure to Estimate the Brain Connectivity Changes in Resting State fMRI and 18F DG-PET AlLheimer's Disease Connectomes. Front Comput Neurosci [Internet]. 2015;9:148. Available from: http://journal.frontiersin.org/Article/10.3389/fncom.2015.00148/abstract
  7. Jokinen P. PET and MR imaging in parkinson's disease patients with cognitive impairment. Turun Yliopisto university of Turku; 2011.
  8. Ishii K, Willoch F, Minoshima S, DrLeLga A, Ficaro EP, Cross DJ, et al. Statistical brain mapping of 18F-FDG PET in AlLheimer's disease: validation of anatomic standardiLation for atrophied brains. J Nucl Med [Internet]. 2001;42(4):548-57. Available from: http://www.ncbi.nlm.nih.gov/pubmed/11337540
  9. Bailey DL, Humm JL, Todd-Pokropek A, van Aswegen A. Nuclear Medicine Physics:A Handbook for Teachers and Students [Internet]. International Atomic Energy Agency; 2014. Available from: https://www.ncbi.nlm.nih.gov/pubmed/19692982
  10. Ziegler SI. Positron emission tomography: Principles, technology, and recent developments. Nucl Phys A. 2005;752(1-4 SPEC. ISS.):679-87. https://doi.org/10.1016/j.nuclphysa.2005.02.067
  11. Carlsson J, Aronsson EF, Hietala SO, Stigbrand T, Tennvall J. Tumour therapy with radionuclides: assessment of progress and problems. Radiother Oncol. 2003;66:107-17. https://doi.org/10.1016/S0167-8140(02)00374-2
  12. Praus P. Water quality assessment using SVD-based principal component analysis of hydrological data. Water SA. 2005;31(4):417-22.
  13. Pedersen F, BergstrLme M, Bengtsson E, Langstrom B. Principal component analysis of dynamic positron emission tomography images. Eur J Nucl Med. 1994;21(12):1285-92. https://doi.org/10.1007/BF02426691
  14. Andrews HC, Patterson CL. Singular Value Decompositions And Digital Image Processing. IEEE Trans Acoust. 1976;24(1):26-53. https://doi.org/10.1109/TASSP.1976.1162766
  15. GonLaleL RC, Woods RE. Digital Image Processing. 3rd ed. New Jersey, USA: Prentice Hall; 2008.
  16. Shnayderman A, Gusev A, Eskicioglu AM. Multidimensional image quality measure using Singular Value Decomposition. In: SPIE-IS&T Electronic Imaging. 2004. Available from: https://doi.org/10.1117/12.530554
  17. Abbadi NK El, Kadhim NE. Brain Tumor Classificati on Based on Singular Value Decomposition. Ijarcce [Internet]. 2016;5(8):553-7. Available from: http://ijarcce.com/upload/2016/august-16/IJARCCE116.pdf https://doi.org/10.17148/IJARCCE.2016.58116
  18. Pyeon DY, Kim JS, Baek CH, Jung YJ. Singular Value Decomposition based Noise Reduction Technique for Dynamic PET Image : Preliminary study. J Radiol Sci Technol. 2016;39(2):227-36. https://doi.org/10.17946/JRST.2016.39.2.12
  19. Park HY, Pyeon D, Kim DH, Jung Y. Dynamic Computed Tomography based on Spatio-temporal Analysis in Acute Stroke : Preliminary Study. J Radiol Sci Technol. 2016;39(4):543-7. https://doi.org/10.17946/JRST.2016.39.4.08