과제정보
We would like to thank Prof. Chan Rok Park for helping us acquire the phantom data.
참고문헌
- K. Kim, M.H. Lee, Y. Lee, Investigation of a blind-deconvolution framework after noise reduction using a gamma camera in nuclear medicine imaging, Nuclear Engineering and Technology 52 (2020) 2594-2600. https://doi.org/10.1016/j.net.2020.04.032
- P. Zanzonico, Principles of nuclear medicine imaging: planar, SPECT, PET, multi-modality, and autoradiography systems, Radiat. Res. 177 (2012) 349-364. https://doi.org/10.1667/RR2577.1
- Z.H. Cho, Y.D. Son, H.K. Kim, K.N. Kim, S.H. Oh, J.Y. Han, I.K. Hong, Y.B. Kim, A fusion PET-MRI system with a high-resolution research tomograph-PET and ultra-high field 7.0 T-MRI for the molecular-genetic imaging of the brain, Proteomics 8 (2008) 1302-1323. https://doi.org/10.1002/pmic.200700744
- S. Sohn, H.J. Shi, S.H. Wang, S.K. Lee, S.Y. Park, J.S. Lee, J.S. Eom, Mycobacterium avium complex infection-related immune reconstitution inflammatory syndrome mimicking lymphoma in an human immunodeficiency virus-infected patient, Infection & Chemotherapy 50 (2018) 350-356. https://doi.org/10.3947/ic.2018.50.4.350
- C.R. Park, Y. Lee, Comparison of PET image quality using simultaneous PET/MR by attenuation correction with various MR pulse sequences, Nuclear Engineering and Technology 51 (2019) 1610-1615. https://doi.org/10.1016/j.net.2019.04.008
- D.W. Townsend, Multimodality imaging of structure and function, Phys. Med. Biol. 53 (2008) R1-R39. https://doi.org/10.1088/0031-9155/53/4/R01
- C.T. Yang, K.K. Ghosh, P. Padmanabhan, O. Langer, J. Liu, D.N.C. Eng, C. Halldin, B. Gulyas, PET-MR and SPECT-MR multimodality probes: development and challenges, Theranostics 8 (2018) 6210-6232. https://doi.org/10.7150/thno.26610
- F.P. Jansen, J.L. Vanderheyden, The future of SPECT in a time of PET, Nucl. Med. Biol. 34 (2007) 733-735. https://doi.org/10.1016/j.nucmedbio.2007.06.013
- D.J. Rowland, S.R. Cherry, Small-animal preclinical nuclear medicine instrumentation and methodology, Semin. Nucl. Med. 38 (2008) 209-222. https://doi.org/10.1053/j.semnuclmed.2008.01.004
- H.H. Li, J.R. Votaw, Optimization of PET activation studies based on the SNR measured in the 3-D hoffman brain phantom, IEEE Trans. Med. Imag. 17 (1998) 596-605. https://doi.org/10.1109/42.730404
- D.W. Townsend, Positron emission tomography/computed tomography, Semin. Nucl. Med. 38 (2008) 152-166. https://doi.org/10.1053/j.semnuclmed.2008.01.003
- P. Lecoq, Development of new scintillators for medical applications, Nucl. Instrum. Methods Phys. Res. 809 (2016) 130-139. https://doi.org/10.1016/j.nima.2015.08.041
- Y.J. Lee, S.J. Park, S.W. Lee, D.H. Kim, Y.S. Kim, H.J. Kim, Comparison of photon counting and conventional scintillation detectors in a pinhole SPECT system for small animal imaging: Monte Carlo simulation studies, J. Kor. Phys. Soc. 62 (2013) 1317-1322. https://doi.org/10.3938/jkps.62.1317
- S. Abbaspour, B. Mahmoudian, J.P. Islamian, Cadmium telluride semiconductor detector for improved spatial and energy resolution radioisotopic imaging, World J. Nucl. Med. 16 (2017) 101-107. https://doi.org/10.4103/1450-1147.203079
- P. Russo, F.D. Lillo, V. Corvino, P.M. Frallicciardi, A. Sarno, G. Mettivier, CdTe compact gamma camera for coded aperture imaging in radioguided surgery, Phys. Med. 69 (2020) 223-232. https://doi.org/10.1016/j.ejmp.2019.12.024
- S. Pujals, N. Feiner-Gracia, P. Delcanale, I. Voets, L. Albertazzi, Super- resolution microscopy as a powerful tool to study complex synthetic materials, Nature Reviews Chemistry 3 (2019) 68-84. https://doi.org/10.1038/s41570-018-0070-2
- D. Ravi, A.B. Szczotka, S.P. Pereira, T. Vercauteren, Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy, Med. Image Anal. 53 (2019) 123-131. https://doi.org/10.1016/j.media.2019.01.011
- T. Mamyrbayev, K. Ikematsu, P. Meyer, A. Ershov, A. Momose, J. Mohr, Super-resolution scanning transmission X-ray imaging using single biconcave parabolic refractive lens array, Sci. Rep. 9 (2019), https://doi.org/10.1038/s41598-019-50869-8.
- L. Xu, X. Zeng, Z. Huang, W. Li, H. Zhang, Low-dose chest X-ray image super-resolution using generativeadversarial nets with spectral normalization, Biomed. Signal Process Contr. 55 (2020), 101600. https://doi.org/10.1016/j.bspc.2019.101600
- M. Elad, A. Feuer, Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images, IEEE Transsanctions on Image Processing 6 (1997) 1646-1658. https://doi.org/10.1109/83.650118
- N. Nguyen, P. Milanfar, G. Golub, Efficient generalized cross-validation with applications to parametric image restoration and resolution enhancement, IEEE Transsanctions on Image Processing 10 (2001) 1299-1308. https://doi.org/10.1109/83.941854
- S.C. Park, M.K. Park, M.G. Kang, Super-resolution image reconstruction: a technical overview, IEEE Signal Process. Mag. (2003) 21-36.
- M. Irani, S. Peleg, Improving resolution by image registration, CVGIP Graph. Models Image Process. 53 (1991) 231-239. https://doi.org/10.1016/1049-9652(91)90045-L
- S. Farsiu, M.D. Robinson, M. Elad, P. Milanfar, Fast and robust multiframe super resolution, IEEE Transsanctions on Image Processing 13 (2004) 1327-1344. https://doi.org/10.1109/TIP.2004.834669
- S. Baker, T. Kanade, Limits on super-resolution and how to break them, IEEE Trans. Pattern Anal. Mach. Intell. 24 (2002) 1167-1183. https://doi.org/10.1109/TPAMI.2002.1033210
- Z. Lin, H. Shum, Fundamental limits of reconstruction-based superresolution algorithms under local translation, IEEE Trans. Pattern Anal. Mach. Intell. 26 (2004) 83-97. https://doi.org/10.1109/TPAMI.2004.1261081
- W.T. Freeman, E.C. Pasztor, O.T. Carmichael, Learning low-level vision, Int. J. Comput. Vis. 40 (2000) 25-47. https://doi.org/10.1023/A:1026501619075
- W.T. Freeman, T.R. Jones, E.C. Pasztor, Example-based super-resolution, IEEE Computer Graphics and Applications 22 (2002) 56-65.
- R. Fattal, Image upsampling via imposed edge statistics, ACM Trans. Graph. 26 (2007), 95-1-8. https://doi.org/10.1145/1276377.1276496
- J. Sun, J. Sun, Z. Xu, H. Shum, Image super-resolution using gradient profile prior, in: 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 23-28.
- D. Glasner, S. Bagon, M. Irani, Super-resolution from a single image, in: 2009 IEEE 12th International Conference on Computer Vision, 2009, https://doi.org/10.1109/ICCV.2009.5459271.
- J. Yang, Z. Wang, Z. Lin, S. Cohen, T. Huang, Coupled dictionary training for image super-resolution, IEEE Transsanctions on Image Processing 21 (2012) 3467-3478. https://doi.org/10.1109/TIP.2012.2192127
- J. Yang, Z. Lin, S. Cohen, Fast image super-resolution based on in-place example regression, in: 2013 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2013, pp. 1059-1066.
- J. Huang, A. Singh, N. Ahuja, Single image super-resolution from transformed self-exemplars, in: 2015 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2015, pp. 5197-5206.
- C. Dong, C.C. Loy, K. He, X. Tang, Image super-resolution using deep convolutional networks, IEEE Trans. Pattern Anal. Mach. Intell. 38 (2016) 295-307. https://doi.org/10.1109/TPAMI.2015.2439281
- J. Kim, J.K. Lee, K.M. Lee, Accurate image super-resolution using very deep convolutional networks, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2016, pp. 1646-1654.
- J. Kim, J.K. Lee, K.M. Lee, Deeply-recursive convolutional network for image super-resolution, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2016, pp. 1637-1645.
- G. Huang, Z. Liu, L.V.D. Maaten, K.Q. Weinberger, Densely connected convolutional networks, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2017, pp. 2261-2269.
- T. Tong, G. Li, X. Liu, Q. Gao, Image super-resolution using dense skip connections, in: 2017 IEEE International Conference on Computer Vision, CVPR, 2017, pp. 4809-4817.
- Y. Zhang, Y. Tian, Y. Kong, B. Zhong, Y. Fu, Residual dense network for image super-resolution, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 2472-2481.
- M. Koutalonis, H. Delis, G. Spyrou, L. Costaridou, G. Tzanakos, G. Panayiotakis, Contrast-to-noise ratio in magnification mammography: a Monte Carlo study, Phys. Med. Biol. 52 (2007) 3185-3199. https://doi.org/10.1088/0031-9155/52/11/017
- O.M. Rijal, H. Ebrahimian, N.M. Noor, Determining features for discriminating PTB and normal lungs using phase congruency model, in: Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2012, 2012, pp. 341-344.
- L. Yu, X. Zhang, Y. Chu, Super-resolution reconstruction algorithm for infrared image with double regular items based on sub-pixel convolution, Appl. Sci. 10 (2020), https://doi.org/10.3390/app10031109.
- J.T. Dobbins III, E. Samei, N.T. Ranger, Y. Chen, Intercomparison of methods for image quality characterization. II. Noise power spectrum, Med. Phys. 33 (2006) 1466-1475. https://doi.org/10.1118/1.2188816
- S. Ioffe, C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, arXiv preprint (2015) arXiv:1502.03167.
- D. P. Kingma, J. Ba, Adam: a Method for Stochastic Optimization, arXiv preprint (2014) arXiv:1412.6980.
- S.Y. Chun, J.A. Fessler, Y.K. Dewaraja, Post-reconstruction non-local means filtering methods using CT side information for quantitative SPECT, Phys. Med. Biol. 58 (2013) 6225-6240. https://doi.org/10.1088/0031-9155/58/17/6225