Fundamental study on improving the quality of X-ray fluorescence computed tomography images by applying deep image prior to projection images as a pre-denoising method

Larobina M, Brunetti A, Salvatore M (2006) Small animal PET: a review of commercially available imaging systems. Curr Med Imaging Rev 2:187–192. https://doi.org/10.2174/157340506776930610

Article  Google Scholar 

Kuntner C, Stout D (2014) Quantitative preclinical PET imaging: opportunities and challenges. Front Phys. https://doi.org/10.3389/fphy.2014.00012

Article  Google Scholar 

Meikle SR, Kench P, Kassiou M, Banati RB (2005) Small animal SPECT and its place in the matrix of molecular imaging technologies. Phys Med Biol 50:R45–R61. https://doi.org/10.1088/0031-9155/50/22/R01

Article  CAS  PubMed  Google Scholar 

Khalil MM, Tremoleda JL, Bayomy TB, Gsell W (2011) Molecular SPECT imaging: an overview. Int J Mol Imaging 2011:1–15. https://doi.org/10.1155/2011/796025

Article  Google Scholar 

Hogan JP, Gonsalves RA, Krieger AS (1991) Fluorescence computer tomography: a model for correction of X-ray absorption. IEEE Trans Nucl Sci 38:1721–1727. https://doi.org/10.1109/23.124168

Article  CAS  Google Scholar 

Yuasa T, Akiba M, Takeda T, Kazama M, Hoshino A, Watanabe Y, Hyodo K, Dilmanian FA, Akatsuka T, Itai Y (1997) Reconstruction method for flurescent X-ray computed tomography by least-squares method using singular value decomposition. IEEE Trans Nucl Sci 44:54–62. https://doi.org/10.1109/23.554824

Article  CAS  Google Scholar 

Takeda T, Wu J, Thet-Thet-Lwin, Huo Q, Yuasa T, Hyodo K, Dilmanian FA, Akatsuka T (2009) X-ray fluorescent CT imaging of cerebral uptake of stable-iodine perfusion agent iodoamphetamine anolog IMP in mice. J Synchrotron Radiat 16:57–62. https://doi.org/10.1107/S0909049508031853

Article  CAS  PubMed  Google Scholar 

Sasaya T, Sunaguchi N, Hyodo K, Zeniya T, Yuasa T (2017) Multi-pinhole fluorescent x-ray computed tomography for molecular imaging. Sci Rep. https://doi.org/10.1038/s41598-017-05179-2

Article  PubMed  PubMed Central  Google Scholar 

Sasaya T, Sunaguchi N, Seo SJ, Hyodo K, Zeniya T, Kim JK, Yuasa T (2018) Preliminary study on X-ray fluorescence computed tomography imaging of gold nanoparticles: acceleration of data acquisition by multiple pinholes scheme. Nucl Instrum Meth A 886:71–76. https://doi.org/10.1016/j.nima.2017.12.055

Article  CAS  Google Scholar 

Manohar N, Reynoso FJ, Diagaradjane P, Krishnan S, Cho SH (2016) Quantitative imaging of gold nanoparticle distribution in a tumor-bearing mouse using benchtop x-ray fluorescence computed tomography. Sci Rep. https://doi.org/10.1038/srep22079

Article  PubMed  PubMed Central  Google Scholar 

Jung S, Kim T, Lee W, Kim H, Kim HS, Im HJ, Ye SJ (2020) Dynamic in vivo X-ray fluorescence imaging of gold in living mice exposed to gold nanoparticles. IEEE Trans Med Imaging 39:526–533. https://doi.org/10.1109/TMI.2019.2932014

Article  PubMed  Google Scholar 

Shaker K, Vogt C, Katsu-Jimenez Y, Kuiper RV, Andersson K, Li Y, Larsson JC, Rodriguez-Garcia A, Toprak MS, Arsenian-Henriksson M, Hertz HM (2020) Longitudinal in-vivo X-Ray fluorescence computed tomography with molybdenum nanoparticles. IEEE Trans Med Imaging 39:3910–3919. https://doi.org/10.1109/TMI.2020.3007165

Article  PubMed  Google Scholar 

Li L, Zhang S, Zhang W, Lu H (2023) Full-field in vivo imaging of nanoparticles using benchtop cone-beam XFCT system with pixelated photon counting detector. Phys Med Biol 68:035020. https://doi.org/10.1088/1361-6560/acb3aa

Article  CAS  Google Scholar 

Iida A, Gohshi Y (1991) Tracer element analysis by X-ray fluorescent. In: Ebashi S, Koch M, Rubenstein E (eds) Handbook on Synchrotron Radiation, vol 4. North Holland, pp 307–438

Google Scholar 

Sawatzky A, Brune C, Wubbeling F, Kosters T, Schafers K, Burger M (2008) Accurate EM-TV algorithm in PET with low SNR. IEEE Nuclear Sci Sympos Conf Record. https://doi.org/10.1109/NSSMIC.2008.4774392

Article  Google Scholar 

Matsubara K, Ibaraki M, Nemoto M, Watabe H, Kimura Y (2022) A review on AI in PET imaging. Ann Nucl Med 36:133–143. https://doi.org/10.1007/s12149-021-01710-8

Article  PubMed  Google Scholar 

Hashimoto F, Onishi Y, Ote K, Tashima H, Reader AJ, Yamaya T (2024) Deep learning-based PET image denoising and reconstruction: a review. Radiol Phys Technol 17:24–46. https://doi.org/10.1007/s12194-024-00780-3

Article  PubMed  PubMed Central  Google Scholar 

Ulyanov D, Vedaldi A, Lempitsky V (2020) Deep Image Prior. Int J Comput Vis 2020:1867–1888. https://doi.org/10.1007/s11263-020-01303-4

Article  Google Scholar 

Gong K, Catane C, Qi J, Li Q (2019) PET image reconstruction using deep image prior. IEEE Trans Med Imaging 38:1655–1665. https://doi.org/10.1109/TMI.2018.2888491

Article  PubMed  Google Scholar 

Hashimoto F, Ohba H, Ote K, Teramoto A, Tsukada H (2019) Dynamic PET image denoising using deep convolutional neural networks without prior training datasets. IEEE Access 7:96594–96603. https://doi.org/10.1109/ACCESS.2019.2929230

Article  Google Scholar 

Gong K, Kim K, Wu D, Kalra MK, Li Q (2019) Low-dose dual energy CT image reconstruction using non-local deep image prior. In: Proceedings of 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Manchester, UK, pp 1–2. https://doi.org/10.1109/NSS/MIC42101.2019.9060001

Zhu Y, Pan X, Lv T, Liu Y, Li L (2021) DESN: an unsupervised MR image denoising network with deep image prior. Theor Comput Sci 880:97–110. https://doi.org/10.1016/j.tcs.2021.06.005

Article  Google Scholar 

Zhou KC, Horstmeyer R (2020) Diffraction tomography with a deep image prior. Opt Express 28:12872–12896. https://doi.org/10.1364/OE.379200

Article  PubMed  PubMed Central  Google Scholar 

Cesareo R, Mascarenhas S (1989) A new tomographic device based on the detection of fluorescent x-rays. Nucl Instrum Meth A 277:669–672. https://doi.org/10.1016/0168-9002(89)90802-4

Article  Google Scholar 

Siddon RL (1985) Fast calculation of the exact radiological path for a three-dimensional CT array. Med Phys 12:252–255. https://doi.org/10.1118/1.595715

Article  CAS  PubMed  Google Scholar 

Hudson HM, Larkin RS (1994) Accelerated image reconstruction using ordered subsets of projection data. IEEE Trans Med Imaging 13:601–609. https://doi.org/10.1109/42.363108

Article  CAS  PubMed  Google Scholar 

Shepp LA, Vardi Y (1982) Maximum likelihood reconstruction for emission tomography. IEEE Trans Med Imaging 1:113–122. https://doi.org/10.1109/TMI.1982.4307558

Article  CAS  PubMed  Google Scholar 

Lange K, Carson R (1984) EM reconstruction algorithms for emission and transmission tomography. J Comput Assist Tomogr 8:306–316

CAS  PubMed  Google Scholar 

Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of the 32nd International Conference on Machine Learning (ICML), Lille, France, pp 448–456

He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of 2015 IEEE International Conference on Cumputer Vision (ICCV), Santiago, Chile, pp 1026–1034. https://doi.org/10.1109/ICCV.2015.123

Currie LA (1968) Limits for qualitative detection and quantitative determination. Appl Radiochem Anal Chem 40:586–593. https://doi.org/10.1021/ac60259a007

Article  CAS  Google Scholar 

留言 (0)

沒有登入
gif