Deep learning-based attenuation correction method in 99mTc-GSA SPECT/CT hepatic imaging: a phantom study

Ha-Kawa SK, Tanaka Y, Hasebe S, et al. Compartmental analysis of asialoglycoprotein receptor scintigraphy for quantitative measurement of liver function: a multicentre study. Eur J Nucl Med. 1997;24:130–7.

Article  CAS  PubMed  Google Scholar 

Ogasawara G, Inoue Y, Ito Y, et al. Improved reproducibility of simple quantitative indices from 99mTc-GSA liver function imaging. Ann Nucl Med. 2013;27:487–91.

Article  PubMed  PubMed Central  Google Scholar 

Yoshida M, Shiraishi S, Sakamoto F, et al. Assessment of hepatic functional regeneration after hepatectomy using 99mTc-GSA SPECT/CT fused imaging. Ann Nucl Med. 2014;28:780–8.

Article  CAS  PubMed  Google Scholar 

Zeintl J, Vija AH, Yahil A, et al. Quantitative accuracy of clinical 99mTc SPECT/CT using ordered-subset expectation maximization with 3-dimensional resolution recovery, attenuation, and scatter correction. J Nucl Med. 2010;51:921–8.

Article  PubMed  Google Scholar 

Larkin AM, Serulle Y, Wagner S, et al. Quantifying the increase in radiation exposure associated with SPECT/CT compared to SPECT alone for routine nuclear medicine examinations. Int Mol Imaging. 2011;2011: 897202.

Google Scholar 

Decuyper M, Maebe J, Van Holen R, et al. Artificial intelligence with deep learning in nuclear medicine and radiology. EJNMMI Phys. 2021;8:81.

Article  PubMed  PubMed Central  Google Scholar 

Goodfellow IJ, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. In: NIPS 27 proceeding, 2014; 2672–2680.

Dong X, Wang T, Lei Y, et al. Synthetic CT generation from non-attenuation corrected PET images for whole-body PET imaging. Phys Med Biol. 2019;64: 215016.

Article  PubMed  PubMed Central  Google Scholar 

Chen Y, Goorden MC, Beekman FJ. Convolution neural network based attenuation correction for 123I-FP-CIT SPECT with focused striatum imaging. Phys Med Biol. 2021;66: 195007.

Article  CAS  Google Scholar 

Sakaguchi K, Kaida H, Yoshida S, et al. Attenuation correction using deep learning for brain perfusion SPECT images. Ann Nucl Med. 2021;35:589–99.

Article  CAS  PubMed  Google Scholar 

Torkaman M, Yang J, Shi L, et al. Direct image-based attenuation correction using conditional generative adversarial network for SPECT myocardial perfusion imaging. In: Proc SPIE Int Soc Opt Eng. 2021;11600

Koizumi K, Uchiyama G, Arai T, et al. A new liver functional study using Tc-99m DTPA-galactosyl human serum albumin: evaluation of the validity of several functional parameters. Ann Nucl Med. 1992;6:83–7.

Article  CAS  PubMed  Google Scholar 

Peters SMB, van der Werf NR, Segbers M, et al. Towards standardization of absolute SPECT/CT quantification: a multi-center and multi-vendor phantom study. EJNMMI Phys. 2019;6:29.

Article  PubMed  PubMed Central  Google Scholar 

Okuda K, Nakajima K, Yamada M, et al. Optimization of iterative reconstruction parameters with attenuation correction, scatter correction and resolution recovery in myocardial perfusion SPECT/CT. Ann Nucl Med. 2014;28:60–8.

Article  PubMed  Google Scholar 

Patton JA, Turkington TG. SPECT/CT physical principles and attenuation correction. J Nucl Med Technol. 2008;36:1–10.

Article  PubMed  Google Scholar 

Zhu JY, Park T, Isola P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV2017 Conference Proceedings 2017; 1:2242–51.

Fukui R, Fujii S, Ninomiya H, et al. Generation of the pseudo CT image based on the deep learning technique aimed for the attenuation correction of the PET image. Nihon Hoshasen Gijutsu Zasshi. 2020;76:1152–62.

Article  Google Scholar 

Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Sys Man Cyber. 1979;9:62–6.

Article  Google Scholar 

Wnag Z, Bovik AC, Sheikh HR, et al. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004;13:600–12.

Article  Google Scholar 

Onishi H, Motomura N, Fujino K, et al. Quantitative performance of advanced resolution recovery strategies on SPECT images: evaluation with use of digital phantom models. Radio Phys Technol. 2013;6:42–53.

Article  Google Scholar 

Sumiyoshi T, Shima Y, Tokorodani R, et al. CT/99mTc-GSA SPECT fusion images demonstrate functional differences between the liver lobes. World J Gastroenterol. 2013;19:3217–25.

Article  PubMed  PubMed Central  Google Scholar 

Nakamura Y, Tomiguchi S, Tanaka M. Reliability and advantages of using non-uniform Chang’s attenuation correction method using a CT-based attenuation coefficient map in 99mTc-GSA SPECT/CT hepatic imaging. EJNMMI Phys. 2015;2:17.

Article  PubMed  PubMed Central  Google Scholar 

Maeda H, Yamaki N, Azuma M. Development of the software package of the nuclear medicine data processor for education and research. Nihon Hoshasen Gijutsu Zasshi. 2012;68:299–306.

Article  Google Scholar 

Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. arXiv e-prints; 2015.

Nakamoto Y, Chin BB, Cohade C, et al. PET/CT: artifacts caused by bowel motion. Nucl Med Commun. 2004;25:221–5.

Article  PubMed  Google Scholar 

Suzuki A, Koshida K, Matsubara K. Effects of pacemaker, implantable cardioverter-defibrillator, and left ventricular leads on CT-based attenuation correction. J Nucl Med Technol. 2014;42:37–41.

Article  PubMed  Google Scholar 

Osman MM, Cohade C, Nakamoto Y, et al. Respiratory motion artifacts on PET emission images obtained using CT attenuation correction on PET-CT. Eur J Nucl Med Mol Imaging. 2003;30:603–6.

Article  PubMed  Google Scholar 

留言 (0)

沒有登入
gif