Abstract
Background: PET imaging is one of the most widely used neurological disease screening and diagnosis techniques.
Aims: Since PET involves the radiation and tolerance of different people, the improvement that has always been focused on is to cut down radiation, in the meantime, ensuring that the generated images with low-dose tracer and generated images with standard-dose tracer have the same details of images.
Methods: We propose a lightweight low-dose PET super-resolution network (SRPET-Net) based on a convolutional neural network. In this research, We propose a method for accurately recovering highresolution (HR) PET images from low-resolution (LR) PET images. The network learns the details and structure of the image between low-dose PET images and standard-dose PET images and, afterward, reconstructs the PET image by the trained network model.
Results: The experiments indicate that the SRPET-Net can achieve a superior peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) values. Moreover, our method has less memory consumption and lower computational cost.
Conclusion: In our follow-up work, the technology can be applied to medical imaging in many different directions.
[1]
Vaquero JJ, Kinahan P. Positron emission tomography: Current challenges and opportunities for technological advances in clinical and preclinical imaging systems. Annu Rev Biomed Eng 2015; 17(1): 385-414.
[http://dx.doi.org/10.1146/annurev-bioeng-071114-040723] [PMID: 26643024]
[http://dx.doi.org/10.1146/annurev-bioeng-071114-040723] [PMID: 26643024]
[2]
Salata BM, Singh P. Role of cardiac PET in clinical practice. Curr Treat Options Cardiovasc Med 2017; 19(12): 93.
[http://dx.doi.org/10.1007/s11936-017-0591-x]
[http://dx.doi.org/10.1007/s11936-017-0591-x]
[3]
Prasad A, Visweswaran S, Kanagaraj K, et al. 18F-FDG PET/CT scanning: Biological effects on patients: Entrance surface dose, DNA damage, and chromosome aberrations in lymphocytes. Mutat Res Genet Toxicol Environ Mutagen 2019; 838: 59-66.
[http://dx.doi.org/10.1016/j.mrgentox.2018.12.010] [PMID: 30678829]
[http://dx.doi.org/10.1016/j.mrgentox.2018.12.010] [PMID: 30678829]
[4]
Parker JA, Kenyon RV, Troxel DE. Comparison of interpolating methods for image resampling. IEEE Trans Med Imaging 1983; 2(1): 31-9.
[http://dx.doi.org/10.1109/TMI.1983.4307610] [PMID: 18234586]
[http://dx.doi.org/10.1109/TMI.1983.4307610] [PMID: 18234586]
[5]
Bian J, Li Y, Feng J. Single image super-resolution using sparse prior, MIPPR 2011 Pattern Recognit. Comput Vis 2011; 8004: 80040L.
[http://dx.doi.org/10.1117/12.901646]
[http://dx.doi.org/10.1117/12.901646]
[6]
Liwei Wang, Jufu Feng. Comments on “Fundamental limits of reconstruction-based superresolution algorithms under local translation”. IEEE Trans Pattern Anal Mach Intell 2006; 28(5): 846.
[http://dx.doi.org/10.1109/TPAMI.2006.91] [PMID: 16640271]
[http://dx.doi.org/10.1109/TPAMI.2006.91] [PMID: 16640271]
[7]
Bashir SMA, Wang Y, Khan M. A comprehensive review of deep learning-based single image super-resolution. Peer J Computer Science 2021; 7. http://arxiv.org/abs/2102.09351
[8]
Zhou RG, Liu X, Luo J. Quantum circuit realization of the bilinear interpolation method for GQIR. Int J Theor Phys 2017; 56(9): 2966-80.
[http://dx.doi.org/10.1007/s10773-017-3463-y]
[http://dx.doi.org/10.1007/s10773-017-3463-y]
[9]
Keys R. Cubic convolution interpolation for digital image processing. IEEE Trans Acoust Speech Signal Process 1981; 29(6): 1153-60.
[http://dx.doi.org/10.1109/TASSP.1981.1163711]
[http://dx.doi.org/10.1109/TASSP.1981.1163711]
[10]
Weiwei GUO, Pinzheng Z. Super-resolution image reconstruction with iterative back projection Algorithm. J Font Comp Sci Tech 2009; 3(3): 321-9.
[11]
Stark H, Oskoui P. High-resolution image recovery from image-plane arrays, using convex projections. J Opt Soc Am A Opt Image Sci Vis 1989; 6(11): 1715-26.
[http://dx.doi.org/10.1364/JOSAA.6.001715] [PMID: 2585170]
[http://dx.doi.org/10.1364/JOSAA.6.001715] [PMID: 2585170]
[12]
Schultz RR, Stevenson RL. Extraction of high-resolution frames from video sequences. IEEE Trans Image Process 1996; 5(6): 996-1011.
[http://dx.doi.org/10.1109/83.503915] [PMID: 18285187]
[http://dx.doi.org/10.1109/83.503915] [PMID: 18285187]
[13]
Dong C, Loy CC, He K, Tang X. Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 2016; 38(2): 295-307.
[http://dx.doi.org/10.1109/TPAMI.2015.2439281] [PMID: 26761735]
[http://dx.doi.org/10.1109/TPAMI.2015.2439281] [PMID: 26761735]
[14]
Dong C, Loy CC, Tang X. Accelerating the super-resolution convolutional neural network. Lect Notes Comput Sci 2016; 391-407.
[http://dx.doi.org/10.1007/978-3-319-46475-6_25]
[http://dx.doi.org/10.1007/978-3-319-46475-6_25]
[15]
Shi W, Caballero J, Huszar F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2016; Dec; 2016; pp. 1874-83.
[http://dx.doi.org/10.1109/CVPR.2016.207]
[http://dx.doi.org/10.1109/CVPR.2016.207]
[16]
Yu J, Fan Y, Yang J, et al. Wide activation for efficient and accurate image super-resolution 2018; ArXiv, abs/1808.08718.
[17]
Ahn N, Kang B, Sohn KA. Fast, accurate, and lightweight super-resolution with cascading residual network ArXiv 2018 ArXiv, abs/1803
[http://dx.doi.org/10.1007/978-3-030-01249-6_16]
[http://dx.doi.org/10.1007/978-3-030-01249-6_16]
[18]
Tian C, Xu Y, Zuo W, Zhang B, Fei L, Lin CW. Coarse-to-fine CNN for image super-resolution. IEEE Trans Multimed 2021; 23: 1489-502.
[http://dx.doi.org/10.1109/TMM.2020.2999182]
[http://dx.doi.org/10.1109/TMM.2020.2999182]
[19]
Xu L, Zeng X, Huang Z, Li W, Zhang H. Low-dose chest X-ray image super-resolution using generative adversarial nets with spectral normalization. Biomed Signal Process Control 2020; 55: 101600.
[http://dx.doi.org/10.1016/j.bspc.2019.101600]
[http://dx.doi.org/10.1016/j.bspc.2019.101600]
[20]
Gu P, Jiang C, Ji M, et al. Low-dose computed tomography image super-resolution reconstruction via random forests. Sensors (Basel) 2019; 19(1): 207.
[http://dx.doi.org/10.3390/s19010207] [PMID: 30626109]
[http://dx.doi.org/10.3390/s19010207] [PMID: 30626109]
[21]
Woo S, Park J, Lee JY, Kweon IS. CBAM: Convolutional block attention module. In: Lect Notes Comput Sci. 2018; pp. 3-19.
[http://dx.doi.org/10.1007/978-3-030-01234-2_1]
[http://dx.doi.org/10.1007/978-3-030-01234-2_1]
[22]
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2016; Dec; 2016; pp. 770-8.
[http://dx.doi.org/10.1109/CVPR.2016.90]
[http://dx.doi.org/10.1109/CVPR.2016.90]
[23]
Brown MJ, Hutchinson LA, Rainbow MJ, Deluzio KJ, De Asha AR. A comparison of self-selected walking speeds and walking speed variability when data are collected during repeated discrete trials and during continuous walking. J Appl Biomech 2017; 33(5): 384-7.
[http://dx.doi.org/10.1123/jab.2016-0355] [PMID: 28530503]
[http://dx.doi.org/10.1123/jab.2016-0355] [PMID: 28530503]
[24]
Douillard C, Jézéquel M, Berrou C, et al. Iterative correction of intersymbol interference: Turbo-equalization. Eur Trans Telecommun 1995; 6(5): 507-11.
[http://dx.doi.org/10.1002/ett.4460060506]
[http://dx.doi.org/10.1002/ett.4460060506]
[25]
Horé A, Ziou D. Image quality metrics: PSNR vs. SSIM Proc Int Conf Pattern Recognit. 2366-9.
[http://dx.doi.org/10.1109/ICPR.2010.579]
[http://dx.doi.org/10.1109/ICPR.2010.579]
[26]
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 2004; 13(4): 600-12.
[http://dx.doi.org/10.1109/TIP.2003.819861] [PMID: 15376593]
[http://dx.doi.org/10.1109/TIP.2003.819861] [PMID: 15376593]
