Magnetic resonance imaging
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A 3 T MLEV-point-resolved spectroscopy (PRESS) sequence employing optimized spectral-spatial and very selective outer-voxel suppression pulses was tested in 25 prostate cancer patients. At an echo time of 85 ms, the MLEV-PRESS sequence resulted in maximally upright inner resonances and minimal outer resonances of the citrate doublet of doublets. Magnetic resonance spectroscopic imaging (MRSI) exams performed at both 3 and 1.5 T for 10 patients demonstrated a 2.08+/-0.36-fold increase in signal-to-noise ratio (SNR) at 3 T as compared with 1.5 T for the center citrate resonances. ⋯ Due to the twofold increase in spectral resolution at 3 T and the improved magnetic field homogeneity provided by susceptibility-matched endorectal coils, the choline resonance was better resolved from polyamine and creatine resonances as compared with 1.5 T spectra. In prostate cancer patients, the elevation of choline and the reduction of polyamines were more clearly observed at 3 T, as compared with 1.5 T MRSI. The increased SNR and corresponding spatial resolution obtainable at 3 T reduced partial volume effects and allowed improved detection of the presence and extent of abnormal metabolite levels in prostate cancer patients, as compared with 1.5 T MRSI.
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Arterial spin labeling (ASL) perfusion measurements allow the follow-up of muscle perfusion with high temporal resolution during a stress test. Automated image processing is proposed to estimate perfusion maps from ASL images. It is based on two successive analyses: at first, automated rejection of the image pairs between which a large displacement is detected is performed, followed by factor analysis of the dynamic data and cluster analysis to classify pixels with large signal variation characteristic of vessels. ⋯ Data from 10 subjects (five normal volunteers and five elite sportsmen) had been analyzed. Resulting time perfusion curves from a region of interest (ROI) in active muscles show a good accordance whether extracted with automated processing or with manual processing. This method of functional segmentation allows automated suppression of vessels and fast visualization of muscles with high, medium or low perfusion, without any a priori knowledge.
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The feasibility of a k-space trajectory that samples data on a set of 3D shells is demonstrated with phantom and volunteer experiments. Details of an interleaved multi-shot, helical spiral pulse sequence and a gridding reconstruction algorithm that uses Voronoi diagrams are provided. The motion-correction properties of the shells k-space trajectory are described. ⋯ Retrospective motion correction is demonstrated with controlled phantom experiments and with seven healthy human volunteers. The motion correction is shown to improve the images, both qualitatively and quantitatively with a metric calculated from image entropy. Advantages and challenges of the shells trajectory are discussed, with particular attention to acquisition efficiency.
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In this article, a generalized likelihood ratio test is proposed to assess the correlation between multisubject functional MRI (fMRI) time series and bases of a signal subspace for detecting the existence of group activation in each voxel of the brain. The signal subspace is generated by a design matrix using the time series of the desired effects. The proposed method leads to testing the product of eigenvalues of a specific matrix. ⋯ The proposed methods are applied on simulated and experimental fMRI data, and the results are compared with those of the general linear model (GLM; using the SPM and FMRISTAT toolboxes). The proposed methods showed higher detection sensitivity as compared with the GLM for activation detection in simulated data. Similarly, they detected more activated regions than did the GLM from multisubject experimental fMRI data on a visual (sensorimotor) event-related task.
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In independent component analysis (ICA), principal component analysis (PCA) is generally used to reduce the raw data to a few principal components (PCs) through eigenvector decomposition (EVD) on the data covariance matrix. Although this works for spatial ICA (sICA) on moderately sized fMRI data, it is intractable for temporal ICA (tICA), since typical fMRI data have a high spatial dimension, resulting in an unmanageable data covariance matrix. To solve this problem, two practical data reduction methods are presented in this paper. ⋯ Without the need to calculate the covariance matrix, CRLS extracts PCs directly from the raw data, and the PC extraction can be terminated after computing an arbitrary number of PCs without the need to estimate the whole set of PCs. Moreover, when the whole data set becomes too large to be loaded into the machine memory, CRLS-PCA can save data retrieval time by reading the data once, while the conventional PCA requires numerous data retrieval steps for both covariance matrix calculation and PC extractions. Real fMRI data were used to evaluate the PC extraction precision, computational expense, and memory usage of the presented methods.