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This paper presents a new approach to inverse consistent image registration. A uni-directional algorithm is developed using symmetric cost functionals and regularizers. Instead of enforcing inverse consistency using an additional penalty that penalizes inconsistency error, the new algorithm directly models the backward mapping by inverting the forward(More)
An unbiased algorithm of generalized linear least squares (GLLS) for parameter estimation of nonuniformly sampled biomedical systems is proposed. The basic theory and detailed derivation of the algorithm are given. This algorithm removes the initial values required and computational burden of nonlinear least regression and achieves a comparable estimation(More)
Peroxisome proliferator-activated receptor gamma (PPARγ) is the master regulator of adipogenesis, and has been indicated as a potential therapeutic target to promote osteoblast differentiation. However, recent studies suggest that suppression of PPARγ inhibits adipogenesis, but does not promote osteogenic differentiation in human bone marrow-derived(More)
Physiological changes in dynamic PET images can be quantitatively estimated by kinetic modeling technique. The process of PET quantification usually requires an input function in the form of a plasma-time activity curve (PTAC), which is generally obtained by invasive arterial blood sampling. However, invasive arterial blood sampling poses many challenges(More)
With the advent of positron emission tomography (PET), a variety of techniques have been developed to measure local cerebral blood flow (LCBF) noninvasively in humans. A potential class of techniques, which includes linear least squares (LS), linear weighted least squares (WLS), linear generalized least squares (GLS), and linear generalized weighted least(More)
A major drawback of statistical iterative image reconstruction for emission computed tomography is its high computational cost. The ill-posed nature of tomography leads to slow convergence for standard gradient-based iterative approaches such as the steepest descent or the conjugate gradient algorithm. In this paper new theory and methods for a class of(More)
In this paper, a new framework for brain warping via landmark matching is proposed using implicit representations or the level set method. We demonstrate this powerful technique by matching landmark curves identified on brain surfaces. Each landmark curve to be matched is represented by the intersection of the zero level sets of two level set functions. A(More)
OBJECTIVES To assess quantitatively the cortical pattern profile of regional FDDNP binding to beta-amyloid and neurofibrillary tangles on MR derived cortical maps, FDDNP PET images were corrected for movement and partial volume (PV), and optimized for kernel size. METHODS FDDNP DVR PET images from 23 subjects (7 with Alzheimer's disease (AD), 6 with mild(More)
OBJECTIVES A cross-sectional study to establish whether a subject's cognitive state can be predicted based on regional values obtained from brain cortical maps of FDDNP Distribution Volume Ratio (DVR), which shows the pattern of beta amyloid and neurofibrillary binding, along with those of early summed FDDNP PET images (reflecting the pattern of perfusion)(More)