Biomedical subjects

Bradley P Sutton

Publications and source records attributed to Bradley P Sutton.

5 recordsLinked to original sources

PubMed2006

Classification of functional brain images with a spatio-temporal dissimilarity map.

Classification of subjects into predefined groups, such as patient vs. control, based on their functional MRI data is a potentially useful procedure for clinical diagnostic purposes. This paper presents an automated method for classifying subjects into groups based on their functional MRI data. The proposed methodology provides general framework using preprocessed time series for the whole brain volume. Using a training set of two groups of subjects, the new methodology identifies spatio-temporal features that distinguish the groups and uses these features to categorize new subjects. We demonstrate the method using simulations and a clinical application that classifies individuals into schizotypy and control groups.

Algorithms↗

PubMed2005

Parallel imaging for NMR microscopy at 14.1 Tesla.

Parallel imaging techniques using arrays of mutually decoupled coils have become standard on almost all clinical imaging systems. Such techniques also have great potential for high-field magnetic resonance (MR) microscopy, where measurement times are usually long and susceptibility artifacts can be severe. However, it is technically very challenging to design efficient high-frequency phased arrays for small-diameter, vertical-bore magnets, especially since standard decoupling methods, such as impedance mismatched preamplifiers, cannot be easily integrated. A four-coil phased array was constructed for microimaging at 600 MHz, and sensitivity encoding (SENSE) and generalized autocalibrating partially parallel acquisitions (GRAPPA) reconstructions of spin-echo and echo-planar images of the mouse brain were performed to reduce imaging time and susceptibility artifacts, respectively.

Algorithms↗

PubMed2005

Conjugate phase MRI reconstruction with spatially variant sample density correction.

A new image reconstruction method to correct for the effects of magnetic field inhomogeneity in non-Cartesian sampled magnetic resonance imaging (MRI) is proposed. The conjugate phase reconstruction method, which corrects for phase accumulation due to applied gradients and magnetic field inhomogeneity, has been commonly used for this case. This can lead to incomplete correction, in part, due to the presence of gradients in the field inhomogeneity function. Based on local distortions to the k-space trajectory from these gradients, a spatially variant sample density compensation function is introduced as part of the conjugate phase reconstruction. This method was applied to both simulated and experimental spiral imaging data and shown to produce more accurate image reconstructions. Two approaches for fast implementation that allow the use of fast Fourier transforms are also described. The proposed method is shown to produce fast and accurate image reconstructions for spiral sampled MRI.

Algorithms↗

PubMed2004

Dynamic field map estimation using a spiral-in/spiral-out acquisition.

The long readout times of single-shot acquisitions and the high field strengths desired for functional MRI (fMRI) using blood oxygenation level-dependent (BOLD) contrast make functional scans sensitive to magnetic field inhomogeneity. If it is not corrected during image reconstruction, field inhomogeneity can cause geometric distortions in the images when Cartesian k-space trajectories are used or blurring with spiral acquisitions. Many traditional methods to correct for field inhomogeneity distortions rely on a static field map measured with the use of images that are themselves distorted. In this work, we employ a regularized least-squares approach to jointly estimate both the undistorted image and field map at each acquisition using a spiral-in/spiral-out pulse sequence. Simulation and phantom studies show that this method is accurate and stable over a time series. Human functional studies show that the jointly estimated field map may be more accurate than standard field map estimates in the presence of respiration-induced phase oscillations, leading to better detection of functional activation. The proposed method measures a dynamic field map that accurately tracks magnetic field drift and respiration-induced phase oscillations during the course of a functional study.

Brain↗

PubMed2003

Fast, iterative image reconstruction for MRI in the presence of field inhomogeneities.

In magnetic resonance imaging, magnetic field inhomogeneities cause distortions in images that are reconstructed by conventional fast Fourier trasform (FFT) methods. Several noniterative image reconstruction methods are used currently to compensate for field inhomogeneities, but these methods assume that the field map that characterizes the off-resonance frequencies is spatially smooth. Recently, iterative methods have been proposed that can circumvent this assumption and provide improved compensation for off-resonance effects. However, straightforward implementations of such iterative methods suffer from inconveniently long computation times. This paper describes a tool for accelerating iterative reconstruction of field-corrected MR images: a novel time-segmented approximation to the MR signal equation. We use a min-max formulation to derive the temporal interpolator. Speedups of around 60 were achieved by combining this temporal interpolator with a nonuniform fast Fourier transform with normalized root mean squared approximation errors of 0.07%. The proposed method provides fast, accurate, field-corrected image reconstruction even when the field map is not smooth.

Algorithms↗