Blind estimation of compartmental model parameters.
暂无分享,去创建一个
[1] I Kanno,et al. A New Approach of Weighted Integration Technique Based on Accumulated Images Using Dynamic PET and H152O , 1991, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.
[2] T. Jones,et al. Spectral Analysis of Dynamic PET Studies , 1993, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.
[3] G. Hutchins,et al. Sampling requirements for dynamic cardiac PET studies using image-derived input functions. , 1993, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.
[4] F. Miraldi,et al. Noninvasive arterial monitor for quantitative oxygen-15-water blood flow studies. , 1993, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.
[5] W. Rogers,et al. Compartmental analysis of technetium-99m-teboroxime kinetics employing fast dynamic SPECT at rest and stress. , 1994, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.
[6] T. Kailath,et al. A least-squares approach to blind channel identification , 1995, IEEE Trans. Signal Process..
[7] G. Gullberg,et al. An investigation of the effect of finite system resolution and photon noise on the bias and precision of dynamic cardiac SPECT parameters. , 1995, Medical physics.
[8] Chrysostomos L. Nikias,et al. EVAM: an eigenvector-based algorithm for multichannel blind deconvolution of input colored signals , 1995, IEEE Trans. Signal Process..
[9] M E Phelps,et al. Factor analysis for extraction of blood time-activity curves in dynamic FDG-PET studies. , 1995, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.
[10] G. Gullberg,et al. Experimental verification of technetium 99m-labeled teboroxime kinetic parameters the the myocardium with dynamic single-photon emission computed tomography: Reproducibility, correlation to flow, and susceptibility to extravascular contamination , 1996, Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology.
[11] Yingbo Hua,et al. Previously Published Works Uc Riverside Title: Fast Maximum Likelihood for Blind Identification of Multiple Fir Channels Fast Maximum Likelihood for Blind Identification of Multiple Fir Channels , 2022 .
[12] F. O’Sullivan,et al. Reducing blood sampling requirements for quantitative PET: some results for cerebral studies with FDG , 1996, 1996 IEEE Nuclear Science Symposium. Conference Record.
[13] Karim Abed-Meraim,et al. Blind system identification , 1997, Proc. IEEE.
[14] E.V.R. Di Bella,et al. Automated region selection for analysis of dynamic cardiac SPECT data , 1997 .
[15] R. Huesman,et al. An investigation into the effect of input function shape and image acquisition interval on estimates of washin for dynamic cardiac SPECT. , 1997, Physics in medicine and biology.
[16] David Dagan Feng,et al. Non-invasive quantification of physiological processes with dynamic PET using blind deconvolution , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).
[17] J. Votaw,et al. Performance evaluation of the Pico-Count flow-through detector for use in cerebral blood flow PET studies. , 1998, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.
[18] G. Gullberg,et al. Factor analysis with a priori knowledge--application in dynamic cardiac SPECT. , 2000, Physics in medicine and biology.