Resolution and noise properties of MAP reconstruction for fully 3-D PET.

We derive approximate analytical expressions for the local impulse response and covariance of images reconstructed from fully three-dimensional (3-D) positron emission tomography (PET) data using maximum a posteriori (MAP) estimation. These expressions explicitly account for the spatially variant detector response and sensitivity of a 3-D tomograph. The resulting spatially variant impulse response and covariance are computed using 3-D Fourier transforms.

Paired MEG data set source localization using recursively applied and projected (RAP) MUSIC.

An important class of experiments in functional brain mapping involves collecting pairs of data corresponding to separate "Task" and "Control" conditions. The data are then analyzed to determine what activity occurs during the Task experiment but not in the Control. Here we describe a new method for processing paired magnetoencephalographic (MEG) data sets using our recursively applied and projected multiple signal classification (RAP-MUSIC) algorithm.

EEG source localization and imaging using multiple signal classification approaches.

Equivalent current dipoles are a powerful tool for modeling focal sources. The dipole is often sufficient to adequately represent sources of measured scalp potentials, even when the area of activation exceeds 1 cm2 of cortex. Traditional least-squares fitting techniques involve minimization of an error function with respect to the location and orientation of the dipoles.

A theoretical study of the contrast recovery and variance of MAP reconstructions from PET data.

We examine the spatial resolution and variance properties of PET images reconstructed using maximum a posteriori (MAP) or penalized-likelihood methods. Resolution is characterized by the contrast recovery coefficient (CRC) of the local impulse response. Simplified approximate expressions are derived for the local impulse response CRC's and variances for each voxel.

EEG and MEG: forward solutions for inverse methods.

A solution of the forward problem is an important component of any method for computing the spatio-temporal activity of the neural sources of magnetoencephalography (MEG) and electroencephalography (EEG) data. The forward problem involves computing the scalp potentials or external magnetic field at a finite set of sensor locations for a putative source configuration. We present a unified treatment of analytical and numerical solutions of the forward problem in a form suitable for use in inverse methods.

A sensor-weighted overlapping-sphere head model and exhaustive head model comparison for MEG.

The spherical head model has been used in magnetoencephalography (MEG) as a simple forward model for calculating the external magnetic fields resulting from neural activity. For more realistic head shapes, the boundary element method (BEM) or similar numerical methods are used, but at greatly increased computational cost. We introduce a sensor-weighted overlapping-sphere (OS) head model for rapid calculation of more realistic head shapes.

Recursive MUSIC: a framework for EEG and MEG source localization.

The multiple signal classification (MUSIC) algorithm can be used to locate multiple asynchronous dipolar sources from electroencephalography (EEG) and magnetoencephalography (MEG) data. The algorithm scans a single-dipole model through a three-dimensional (3-D) head volume and computes projections onto an estimated signal subspace. To locate the sources, the user must search the head volume for multiple local peaks in the projection metric.

A study of dipole localization accuracy for MEG and EEG using a human skull phantom.

Objective: To investigate the accuracy of forward and inverse techniques for EEG and MEG dipole localization.

Design And Methods: A human skull phantom was constructed with brain, skull and scalp layers and realistic relative conductivities. Thirty two independent current dipoles were distributed within the 'brain' region and EEG and MEG data collected separately for each dipole.

High-resolution 3D Bayesian image reconstruction using the microPET small-animal scanner.

A Bayesian method is described for reconstruction of high-resolution 3D images from the microPET small-animal scanner. Resolution recovery is achieved by explicitly modelling the depth dependent geometric sensitivity for each voxel in combination with an accurate detector response model that includes factors due to photon pair non-collinearity and inter-crystal scatter and penetration. To reduce storage and computational costs we use a factored matrix in which the detector response is modelled using a sinogram blurring kernel.

First Report of Aristastoma Leaf Spot on Desert Rose.

In the spring of 1995, a species of Aristastoma was isolated from foliar lesions of Adenium obesum that originated in a commercial nursery in Dade County, FL, where 100% of the crop was affected. Plant foliage had irregular, oval to circular, rusty brown, necrotic lesions 5 to 15 mm in diameter. Large leaf spots developed tan centers.

MEG-based imaging of focal neuronal current sources.

We describe a new approach to imaging neural current sources from measurements of the magnetoencephalogram (MEG) associated with sensory, motor, or cognitive brain activation. Many previous approaches to this problem have concentrated on the use of weighted minimum norm (WMN) inverse methods. While these methods ensure a unique solution, they do not introduce information specific to the MEG inverse problem, often producing overly smoothed solutions and exhibiting severe sensitivity to noise.

Real-time non-invasive measurement of heart rate in working dogs: a technique with potential applications in the objective assessment of welfare problems.

The Polar Sport Tester (Polar Electro OY) is a telemetric heart rate monitor designed for use in humans. Its usefulness as a monitor during training of guide dogs is assessed in this paper. Heart rates from six representative dogs at a similar stage of early training were recorded at 5-s intervals during a 15-20 min work session.

Surface-based labeling of cortical anatomy using a deformable atlas.

We describe a computerized method to automatically find and label the cortical surface in three-dimensional (3-D) magnetic resonance (MR) brain images. The approach we take is to model a prelabeled brain atlas as a physical object and give it elastic properties, allowing it to warp itself onto regions in a preprocessed image. Preprocessing consists of boundary-finding and a morphological procedure which automatically extracts the brain and sulci from an MR image and provides a smoothed representation of the brain surface to which the deformable model can rapidly converge.

Approximate maximum likelihood hyperparameter estimation for Gibbs priors.

The parameters of the prior, the hyperparameters, play an important role in Bayesian image estimation. Of particular importance for the case of Gibbs priors is the global hyperparameter, beta, which multiplies the Hamiltonian. Here we consider maximum likelihood (ML) estimation of beta from incomplete data, i.

Bayesian reconstruction of PET images: methodology and performance analysis.

We describe a practical statistical methodology for the reconstruction of PET images. Our approach is based on a Bayesian formulation of the imaging problem. The data are modelled as independent Poisson random variables and the image is modelled using a Markov random field smoothing prior.

Fast gradient-based methods for Bayesian reconstruction of transmission and emission PET images.

The authors describe conjugate gradient algorithms for reconstruction of transmission and emission PET images. The reconstructions are based on a Bayesian formulation, where the data are modeled as a collection of independent Poisson random variables and the image is modeled using a Markov random field. A conjugate gradient algorithm is used to compute a maximum a posteriori (MAP) estimate of the image by maximizing over the posterior density.

Determining cardiac velocity fields and intraventricular pressure distribution from a sequence of ultrafast CT cardiac images.

A method of computing the velocity field and pressure distribution from a sequence of ultrafast CT (UFCT) cardiac images is demonstrated. UFCT multi-slice cine imaging gives a series of tomographic slices covering the volume of the heart at a rate of 17 frames per second. The complete volume data set can be modeled using equations of continuum theory and through regularization, velocity vectors of both blood and tissue can be determined at each voxel in the volume.

Error bounds for EEG and MEG dipole source localization.

General formulas are presented for computing a lower bound on localization and moment error for electroencephalographic (EEG) or magnetoencephalographic (MEG) current source dipole models with arbitrary sensor array geometry. Specific EEG and MEG formulas are presented for multiple dipoles in a head model with 4 spherical shells. Localization error bounds are presented for both EEG and MEG for several different sensor configurations.

Non-invasive measurement of arterial blood pressure in dogs: a potential indicator for the identification of stress.

The reproducibility of indirect measurements of arterial pressure in dogs was assessed using the Dinamap 1846 SX oscillometric monitor and tail cuffs. Measurements on different days correlated excellently in 20 working dogs, with indistinguishable group means for systolic, diastolic and mean arterial pressure, whereas heart rates differed, though not significantly. The similarity of the means did not simply result from 'cancelling out' of individual variations; the rank correlation was also highly significant (P < 0.

An approximate method of evaluating the joint likelihood for first-order GMRFs.

A highly accurate approximation is proposed for computing the joint likelihood for first-order Gauss-Markov random fields (GMRFs) defined on irregularly shaped lattices. The problem in computing the likelihood lies in evaluating the determinant of a very large matrix B. Its exact evaluation is limited to either very small irregular regions or a few regularly shaped regions.

Multiple dipole modeling and localization from spatio-temporal MEG data.

An array of biomagnetometers may be used to measure the spatio-temporal neuromagnetic field or magnetoencephalogram (MEG) produced by neural activity in the brain. A popular model for the neural activity produced in response to a given sensory stimulus is a set of current dipoles, where each dipole represents the primary current associated with the combined activation of a large number of neurons located in a small volume of the brain. An important problem in the interpretation of MEG data from evoked response experiments is the localization of these neural current dipoles.

Derivation and analysis of a filtered backprojection algorithm for cone beam projection data.

The authors address the problem of three-dimensional image reconstruction from cone beam projections. Modifying a result due to A.A.

Computation of 3-D velocity fields from 3-D cine CT images of a human heart.

A method of computing the three-dimensional (3-D) velocity field from 3-D cine computer tomographs (CTs) of a beating heart is proposed. Using continuum theory, the authors develop two constraints on the 3-D velocity field generated by a beating heart. With these constraints, the computation of the 3-D velocity field is formulated as an optimization problem and a solution to the optimization problem is developed using the Euler-Lagrange method.