An ordered-subsets proximal preconditioned gradient algorithm for edge-preserving PET image reconstruction

Purpose: In iterative positron emission tomography (PET) image reconstruction, the statistical variability of the PET data precorrected for random coincidences or acquired in sufficiently high count rates can be properly approximated by a Gaussian distribution, which can lead to a penalized weighted...

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Veröffentlicht in:	Medical physics (Lancaster) Jg. 40; H. 5; S. 052503 - n/a
Hauptverfasser:	Mehranian, Abolfazl, Rahmim, Arman, Ay, Mohammad Reza, Kotasidis, Fotis, Zaidi, Habib
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States American Association of Physicists in Medicine 01.05.2013
Schlagworte:	Algorithms Brain Brain - diagnostic imaging Computed tomography convergence of numerical methods Digital computing or data processing equipment or methods, specially adapted for specific applications Fluorodeoxyglucose F18 Gaussian distribution gradient methods Hessian matrices Humans Image data processing or generation, in general Image Processing, Computer-Assisted - methods image reconstruction Image sensors least squares approximations Logic and set theory Matrix theory Medical image noise medical image processing Medical image reconstruction Medical imaging Medical magnetic resonance imaging Noise Numerical optimization optimisation Optimization phantoms positron emission tomography Positron emission tomography (PET) Positron-Emission Tomography - methods preconditioning proximal gradient Reconstruction set theory total variation regularization total variation regularization image reconstruction preconditioning proximal gradient
ISSN:	0094-2405, 2473-4209, 2473-4209
Online-Zugang:	Volltext
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