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ABSTRACT: In this paper a minimax methodology is presented for combining
information from two imaging modalities having different intrinsic
spatial resolutions. The focus application is emission computed
tomography (ECT), a low-resolution modality for reconstruction of
radionuclide tracer density, when supplemented by high-resolution
anatomical boundary information extracted from a magnetic resonance
image (MRI) of the same imaging volume. The MRI boundary within the
two-dimensional (2-D) slice of interest is parameterized by a closed
planar curve. The Cramer-Rao (CR) lower bound is used to analyze
estimation errors for different boundary shapes. Under a spatially
inhomogeneous Gibbs field model for the tracer density a representation
for the minimax MRI-enhanced tracer density estimator is obtained. It is
shown that the estimator is asymptotically equivalent to a penalized
maximum likelihood (PML) estimator with resolution-selective Gibbs
penalty. Quantitative comparisons are presented using the iterative
space alternating generalized expectation maximization (SAGE-FM)
algorithm to implement the PML estimator with and without minimax weight
averaging
IEEE Transactions on Information Theory 05/1999; · 3.01 Impact Factor
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ABSTRACT: In this paper, a method is introduced for incorporating perfectly
registered MRI boundary information into a penalized likelihood emission
reconstruction scheme. The boundary curve is modeled as a periodic
spline whose coefficients are estimated from the MRI image. The
resulting boundary estimate is mapped to a spatially variant set of
Gibbs weights. When incorporated into a quadratic roughness penalty,
these weights improve emission reconstruction bias/variance performance
by preventing smoothing across the estimated boundary. Finally, we
derive a new penalty function that accounts for the uncertainty inherent
in the boundary estimates
Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on; 05/1997 · 4.63 Impact Factor
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ABSTRACT: This paper presents a method for incorporating anatomical NMR
boundary side information into penalized maximum likelihood (PML)
emission image reconstructions. The NMR boundary is parameterized as a
periodic spline curve of fixed order and number of knots that is known a
priori. Maximum likelihood (ML) estimation of the spline coefficients
yields an “extracted” boundary, which is used to define a
set of Gibbs weights on the emission image space. These weights, when
coupled with a quadratic penalty function, create an edge-preserving
penalty that incorporates our prior knowledge effectively. Qualitative
analysis demonstrates that our method results in smooth images that do
not suffer loss of edge contrast, while quantitative estimates of bias
and variance for various values of the smoothing parameter show an
improvement over standard quadratically penalized maximum likelihood
Image Processing, 1996. Proceedings., International Conference on; 10/1996
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ABSTRACT: We give estimation error bounds and specify optimal estimators for continuous, closed boundary curves in an NMR image. The boundary is parameterized using periodic B-splines. A Cramer-Rao lower bound on mean-square-estimate error in the presence of system smoothing and Gaussian noise is derived, and the performance of maximum likelihood and penalized maximum likelihood estimators is compared to this bound. Finally, we comment on the usefulness of estimates of the boundary for providing anatomical side information in the reconstruction of functional tomographic images like those of a PET or SPECT system
Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on; 06/1995
