Biomedical subjects

Refaat E Gabr

Publications and source records attributed to Refaat E Gabr.

3 recordsLinked to original sources

PubMed2006

On restoring motion-induced signal loss in single-voxel magnetic resonance spectra.

Destructive interference from phase fluctuations caused by motion during (1)H magnetic resonance spectroscopy (MRS) stimulated-echo acquisition mode (STEAM) and point-resolved spectroscopy (PRESS) acquisitions can significantly diminish the traditional radicalN-gain in signal-to-noise ratio (SNR) afforded by averaging N signals, especially in the torso. The SNR loss is highly variable among individuals, even when identical acquisition protocols are used. This paper presents a theory for the SNR loss, assuming that the phase fluctuates randomly. It is shown that SNR in conventional averaging is reduced by the factor sinc(sigma(phi) radical3/pi), where sigma(phi) is the standard deviation (SD) of the phase. "Constructive averaging," whereby each individual acquisition is phase-corrected using the phase of a high-SNR peak before averaging, reverses the SNR loss from motion-induced dephasing, resulting in a {1/sinc(sigma(phi) radical3/pi)}-fold SNR improvement. It is also shown that basing phase corrections on an average of radicalN adjacent points both improves correction accuracy and effectively eliminates false signal artifacts when corrections are based on low-SNR peaks. The theory is validated over a sevenfold range of variation in signal loss due to motion observed in (1)H STEAM and PRESS data acquired from 17 human subjects (heart: N = 16; leg: N = 1). Constructive averaging should be incorporated as a routine tool for in vivo (1)H MRS.

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PubMed2006

Deconvolution-interpolation gridding (DING): accurate reconstruction for arbitrary k-space trajectories.

A simple iterative algorithm, termed deconvolution-interpolation gridding (DING), is presented to address the problem of reconstructing images from arbitrarily-sampled k-space. The new algorithm solves a sparse system of linear equations that is equivalent to a deconvolution of the k-space with a small window. The deconvolution operation results in increased reconstruction accuracy without grid subsampling, at some cost to computational load. By avoiding grid oversampling, the new solution saves memory, which is critical for 3D trajectories. The DING algorithm does not require the calculation of a sampling density compensation function, which is often problematic. DING's sparse linear system is inverted efficiently using the conjugate gradient (CG) method. The reconstruction of the gridding system matrix is simple and fast, and no regularization is needed. This feature renders DING suitable for situations where the k-space trajectory is changed often or is not known a priori, such as when patient motion occurs during the scan. DING was compared with conventional gridding and an iterative reconstruction method in computer simulations and in vivo spiral MRI experiments. The results demonstrate a stable performance and reduced root mean square (RMS) error for DING in different k-space trajectories.

Algorithms↗

PubMed2005

Quantifying in vivo MR spectra with circles.

Accurate and robust quantification of in vivo magnetic resonance spectroscopy (MRS) data is essential to its application in research and medicine. The performance of existing analysis methods is problematic for in vivo studies where low signal-to-noise ratio, overlapping peaks and intense artefacts are endemic. Here, a new frequency-domain technique for MRS data analysis is introduced wherein the circular trajectories which result when spectral peaks are projected onto the complex plane, are fitted with active circle models. The use of active contour strategies naturally allows incorporation of prior knowledge as constraint energy terms. The problem of phasing spectra is eliminated, and baseline artefacts are dealt with using active contours-snakes. The stability and accuracy of the new technique, CFIT, is compared with a standard time-domain fitting tool, using simulated 31P data with varying amounts of noise and 98 real human chest and heart 31P MRS data sets. The real data were also analyzed by our standard frequency-domain absorption-mode technique. On the real data, CFIT demonstrated the least fitting failures of all methods and an accuracy similar to the latter method, with both these techniques outperforming the time-domain approach. Contrasting results from simulations argue that performance relative to Cramer-Rao Bounds may not be a suitable indicator of fitting performance with typical in vivo data such as these. We conclude that CFIT is a stable, accurate alternative to the best existing methods of fitting in vivo data.

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