Natterer F, Wubbeling F. Mathematical Methods in Image Reconstruction. Mathematical Modeling and Computation. SIAM. 2001. pp. 95–99.
Herman GT, Meyer LB. Algebraic reconstruction techniques can be made computationally efficient. IEEE Trans Med Imaging. 1993;12:600–9.
Article CAS PubMed Google Scholar
Kaufman L. Maximum likelihood, least squares, and penalized least squares for PET. IEEE Trans Med Imaging. 1993;12:200–14.
Article CAS PubMed Google Scholar
Shepp LA, Vardi Y. Maximum likelihood reconstruction for emission tomography. IEEE Trans Med Imaging. 1982;1:113–22.
Article CAS PubMed Google Scholar
Hudson HM, Larkin RS. Accelerated image reconstruction using ordered subsets of projection data. IEEE Trans Med Imaging. 1994. https://doi.org/10.1109/42.363108.
De Pierro AR, Yamagishi MEB. Fast EM-like methods for maximum ‘a posteriori’ estimates in emission tomography. IEEE Trans Med Imaging. 2001;20:280–8.
Ahn S, Fessler JA. Globally convergent image reconstruction for emission tomography using relaxed ordered subsets algorithms. IEEE Tans Med Imaging. 2003;22:613–26.
Green PJ. Bayesian reconstruction from emission tomography data using a modified EM algorithm. IEEE Trans Med Imaging. 1990;9:84–93.
Article CAS PubMed Google Scholar
Rudin L, Osher S, Fatemi E. Non-linear total variation noise removal algorithm. Phys D. 1992;60:259–68.
Nuyts J, Bequé D, Dupont P, Mortelmans L. A concave prior penalizing relative differences for maximum-a-posteriori reconstruction in emission tomography. IEEE Trans Nucl Sci. 2002;49:56–60.
Shinohara H, Hori K, Hashimoto T. Deep learning study on the mechanism of edge artifacts in point spread function reconstruction for numerical brain images. Ann Nucl Med. 2023;37:596–604.
Bouman C, Sauer K. A generalized Gaussian image model for edge-preserving MAP estimation. IEEE Trans Image Process. 1993;2:296–310.
Article CAS PubMed Google Scholar
Wagatsuma K, Miwa K, Kamitaka Y, Koike E, Yamao T, Yoshii T, et al. Determination of optimal regularization factor in Bayesian penalized likelihood reconstruction of brain PET images using [18F] FDG and [11C] PIB. Med Phys. 2022;49:2995–3005.
Wilson DW, Tsui BMW. Spatial resolution properties of FBP and ML-EM reconstruction methods. IEEE Nucl Sci Symp Med Imaging Conference. 1993;2:1189–93.
Fessler JA, Rogers WL. Spatial resolution properties of penalized-likelihood image reconstruction: space-invariant tomographs. IEEE Trans Image Processing. 1996;5:1346–58.
Gong K, Cherry SR, Qi J. On the assessment of spatial resolution of PET systems with iterative image reconstruction. Phys Med Biol. 2016;61:N193–202.
Article CAS PubMed PubMed Central Google Scholar
Ahn A, Leahy RM. Analysis of resolution and noise properties on nonquadratically regularized image reconstruction methods for PET. IEEE Trans Med Imaging. 2008;27:413–24.
Zeng GL. Medical image reconstruction. Springer. 2010. pp. 165–167.
Shinohara H, Hashimoto T. Object-dependent spatial resolution characteristics of OSEM (ordered subset expectation maximization) regularized with relative difference prior. Med Imag Tech. 2024;42:117–29.
NEMA NU 2–2012 Performance measurement for Positron Emission Tomographs (PET). National Electrical Manufacturer Association. 2013; VA: 9–11.
Cocosco CA, Kollokian V, Kwan RK-S, et al. Brainweb: Online interface to a 3D MRI simulated brain database. Neuroimage. 1997;5:425.
Hashimoto F, Ote K, Onishi Y. Pet image reconstruction incorporating deep image prior and a forward projection model. IEEE Trans Radiat Plasma Med Sci. 2022;6:841–6.
Shinohara H, Hashimoto T. Mechanism of edge artifacts in PSF reconstruction. Med Imaging Technol. 2022;40:261–72.
Sidky EY, Pan X. Image reconstruction in circular cone-beam computed tomography by constrained total variation minimization. Phys Med Biol. 2008;53:4777–807.
Article PubMed PubMed Central Google Scholar
Bilgic B, Goyal VK, Adalsteinsson E. Multi-contrast reconstruction with Bayesian compressed sensing. Magn Reson Med. 2011;66:1601–15.
Article PubMed PubMed Central Google Scholar
Hutton BF, Nuyts J, Zaidi H. Iterative reconstruction methods. In: Zaidi H (ed) Quantitative analysis in nuclear medicine imaging. Springer. 2006. pp.112–24,
Ibaraki M, Matsubara K, Shinohara Y, Shaidahara M, Sato K, Yamamoto H, et al. Brain partial volume correction with point spreading function reconstruction in high-resolution digital PET: comparison with an MR-based method in FDG imaging. Ann Nucl Med. 2022;36:717–26.
Article CAS PubMed PubMed Central Google Scholar
Wilson DW, Tsui BMW, Barrett HH. Noise properties of the EM algorithm: II monte carlo simulations. Phys Med Biol. 1994;39:847–71.
Article CAS PubMed Google Scholar
Rahmin A, Qi J, Sossi V. Resolution modeling in PET imaging; theory, practice, benefits, and pitfalls. Med Phys. 2015;40: 064301.
Comments (0)