특이값분해 기반 동적의료영상 재구성기법의 특징 파악을 위한 시뮬레이션 연구
Simulation Study for Feature Identification of Dynamic Medical Image Reconstruction Technique Based on Singular Value Decomposition
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초록
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.
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.
참고문헌 (19)
- Sprawls P, "Online Edition: Magnetic Resonance Imaging Principles, Methods and techniques. Sprawls Educational Foundation" (2018), Emory University
- Brenner DJ, "Computed Tomography — An Increasing Source of Radiation Exposure — NEJM", N Engl J Med, 357, pp.2277~2284 (2007)
- Ana M. Llamas-Álvarez, "Accuracy of Lung Ultrasonography in the Diagnosis of Pneumonia in Adults", Chest, 151(2), pp.374~382 (2017), Elsevier BV[DOI]
- Bateman TM, "Advantages and disadvantages of PET and SPECT in a busy clinical practice", J Nucl Cardiol, 19, pp.S3~S11 (2012)
- Ann D. Cohen, "Early detection of Alzheimer's disease using PiB and FDG PET", Neurobiology of Disease, 72, pp.117~122 (2014), Elsevier BV[DOI]
- Zippo AG, "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, 9, pp.148~ (2015)[DOI]
- Jokinen P, "PET and MR imaging in parkinson’s disease patients with cognitive impairment", pp.2011
- Ishii K, "Statistical brain mapping of 18F-FDG PET in AlLheimer’s disease: validation of anatomic standardiLation for atrophied brains", J Nucl Med, 42(4), pp.548~557 (2001)
- "Nuclear Medicine Physics:A Handbook for Teachers and Students" (2014)[URL]
- Ziegler SI, "Positron emission tomography: Principles, technology, and recent developments", Nucl Phys A, 752(1-4 SPEC), pp.679~687 (2005)
- Carlsson J, "Tumour therapy with radionuclides:assessment of progress and problems", Radiother Oncol, 66, pp.107~117 (2003)
- Praus P, "Water quality assessment using SVD-based principal component analysis of hydrological data", Water SA, 31(4), pp.417~422 (2005)
- Pedersen F, "Principal component analysis of dynamic positron emission tomography images", Eur J Nucl Med, 21(12), pp.1285~1292 (1994)
- Andrews HC, "Singular Value Decompositions And Digital Image Processing", IEEE Trans Acoust, 24(1), pp.26~53 (1976)
- GonLaleL RC, "Digital Image Processing" (2008), Prentice Hall
- Shnayderman A, "Multidimensional image quality measure using Singular Value Decomposition", SPIE-IS&T Electronic Imaging (2004)
- Abbadi NK El, "Brain Tumor Classificati on Based on Singular Value Decomposition", Ijarcce, 5(8), pp.553~557 (2016)
- 편도영, "특이값 분해 기반 Dynamic PET 영상의 노이즈 제거 기법 : 예비 연구", 방사선기술과학, 39(2), pp.227~236 (2016), 대한방사선과학회[DOI]
- 박하영, "급성 뇌졸중 환자의 시공간 분석 기법을 이용한 동적 전산화 단층 검사 : 예비 연구", 방사선기술과학, 39(4), pp.543~547 (2016), 대한방사선과학회[DOI]