DocumentCode :
3602081
Title :
Direct Parametric Reconstruction Using Anatomical Regularization for Simultaneous PET/MRI Data
Author :
Loeb, Rebekka ; Navab, Nassir ; Ziegler, Sibylle I.
Author_Institution :
Dept. of Nuklearmedizin, Tech. Univ. Munchen, Munich, Germany
Volume :
34
Issue :
11
fYear :
2015
Firstpage :
2233
Lastpage :
2247
Abstract :
Pharmacokinetic analysis of dynamic positron emission tomography (PET) imaging data maps the measured time activity curves to a set of model-specific pharmacokinetic parameters. Voxel-based parameter estimation via curve fitting is conventionally performed indirectly on a sequence of independently reconstructed PET images, leading to high variance and bias in the parametric images. We propose a direct parametric reconstruction algorithm with raw projection data as input that leverages high-resolution anatomical information simultaneously obtained from magnetic resonance (MR) imaging in a PET/MRI scanner for regularization. The reconstruction problem is formulated in a flexible Bayesian framework with Gaussian Markov Random field modeling of activity, parameters, or both simultaneously. MR information is incorporated through a Bowsher-like prior function. Optimization transfer using an expectation-maximization surrogate and a new Bowsher-like penalty surrogate is applied to obtain a voxel-separable algorithm that interleaves a reconstruction with a fitting step. An analytical input function model is used. The algorithm is evaluated on simulated [ 18 F]FDG and clinical [ 18 F]FET brain data acquired with a Biograph mMR. The results indicate that direct and simultaneously regularized parametric reconstruction increases image quality. Anatomical regularization leads to higher contrast than conventional distance-weighted regularization.
Keywords :
biomedical MRI; brain; expectation-maximisation algorithm; image reconstruction; image sequences; medical image processing; neurophysiology; optimisation; parameter estimation; positron emission tomography; Bowsher-like penalty surrogate; Bowsher-like prior function; Gaussian Markov random field modeling; PET-MRI scanner; analytical input function model; anatomical regularization; biograph mMR; clinical [18F]FET brain data; curve fitting; direct parametric reconstruction algorithm; dynamic positron emission tomography imaging data maps; expectation-maximization surrogate; flexible Bayesian framework; high-resolution anatomical information; magnetic resonance imaging; model-specific pharmacokinetic parameters; optimization transfer; raw projection data; reconstructed PET image sequence; simulated [18F]FDG brain data; simultaneous PET-MRI data; simultaneously regularized parametric reconstruction; time activity curves; voxel-based parameter estimation; voxel-separable algorithm; Algorithm design and analysis; Image reconstruction; Kinetic theory; Magnetic resonance imaging; Optimization; Positron emission tomography; Anatomical prior; PET/MRI; brain; magnetic resonance imaging (MRI); optimization; parametric reconstruction; positron emission tomography (PET);
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/TMI.2015.2427777
Filename :
7097704
Link To Document :
بازگشت