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Noise Amplification (noise + amplification)
Selected AbstractsMaximum likelihood scaling (MALS)JOURNAL OF CHEMOMETRICS, Issue 3-4 2006Huub C. J. Hoefsloot Abstract A filtering procedure is introduced for multivariate data that does not suffer from noise amplification by scaling. A maximum likelihood principal component analysis (MLPCA) step is used as a filter that partly removes noise. This filtering can be used prior to any subsequent scaling and multivariate analysis of the data and is especially useful for data with moderate and low signal-to-noise ratio's, such as metabolomics, proteomics and transcriptomics data. Copyright © 2007 John Wiley & Sons, Ltd. [source] 96-Channel receive-only head coil for 3 Tesla: Design optimization and evaluationMAGNETIC RESONANCE IN MEDICINE, Issue 3 2009Graham C. Wiggins Abstract The benefits and challenges of highly parallel array coils for head imaging were investigated through the development of a 3T receive-only phased-array head coil with 96 receive elements constructed on a close-fitting helmet-shaped former. We evaluated several designs for the coil elements and matching circuitry, with particular attention to sources of signal-to-noise ratio (SNR) loss, including various sources of coil loading and coupling between the array elements. The SNR and noise amplification (g -factor) in accelerated imaging were quantitatively evaluated in phantom and human imaging and compared to a 32-channel array built on an identical helmet-shaped former and to a larger commercial 12-channel head coil. The 96-channel coil provided substantial SNR gains in the distal cortex compared to the 12- and 32-channel coils. The central SNR for the 96-channel coil was similar to the 32-channel coil for optimum SNR combination and 20% lower for root-sum-of-squares combination. There was a significant reduction in the maximum g -factor for 96 channels compared to 32; for example, the 96-channel maximum g -factor was 65% of the 32-channel value for acceleration rate 4. The performance of the array is demonstrated in highly accelerated brain images. Magn Reson Med, 2009. © 2009 Wiley-Liss, Inc. [source] x-f choice: Reconstruction of undersampled dynamic MRI by data-driven alias rejection applied to contrast-enhanced angiographyMAGNETIC RESONANCE IN MEDICINE, Issue 4 2006Shaihan J. Malik Abstract A technique for reconstructing dynamic undersampled MRI data, termed "x-f choice," was developed and applied to dynamic contrast-enhanced MR angiography (DCE-MRA). Regular undersampling in k-t space (a hybrid of k -space and time) creates aliasing in the conjugate x-f space that must be resolved. When regions in the object containing fast dynamic change are sparse, as in DCE-MRA, signal overlap caused by aliasing is often much less than the undersample factor would imply. x-f Choice reconstruction identifies overlapping signals using a model of the full non-aliased x-f space that is automatically generated from the undersampled data, and applies parallel imaging (PI) to separate them. No extra reference scans are required to generate either the model or the coil sensitivity maps. At each location in the reconstructed images, g -factor noise amplification is compared with predicted reconstruction errors to obtain an optimized solution. Acceleration factors greater than the number of receiver coils are possible, but are limited by the sparseness of the dynamic content and the signal-to-noise ratio (SNR) (in DCE-MRA the latter is dominant). Temporal fidelity was validated for up to a factor 10 speed-up using retrospectively undersampled data from a six-coil array. The method was tested on volunteers using fivefold prospective undersampling. Magn Reson Med, 2006. © 2006 Wiley-Liss, Inc. [source] 32-channel 3 Tesla receive-only phased-array head coil with soccer-ball element geometryMAGNETIC RESONANCE IN MEDICINE, Issue 1 2006G.C. Wiggins Abstract A 32-channel 3T receive-only phased-array head coil was developed for human brain imaging. The helmet-shaped array was designed to closely fit the head with individual overlapping circular elements arranged in patterns of hexagonal and pentagonal symmetry similar to that of a soccer ball. The signal-to-noise ratio (SNR) and noise amplification (g -factor) in accelerated imaging applications were quantitatively evaluated in phantom and human images and compared with commercially available head coils. The 32-channel coil showed SNR gains of up to 3.5-fold in the cortex and 1.4-fold in the corpus callosum compared to a (larger) commercial eight-channel head coil. The experimentally measured g -factor performance of the helmet array showed significant improvement compared to the eight-channel array (peak g -factor 59% and 26% of the eight-channel values for four- and fivefold acceleration). The performance of the arrays is demonstrated in high-resolution and highly accelerated brain images. Magn Reson Med, 2006. © 2006 Wiley-Liss, Inc. [source] Resolution deconvolution method applied to 2D-ACAR measurementsPHYSICA STATUS SOLIDI (C) - CURRENT TOPICS IN SOLID STATE PHYSICS, Issue 10 2007T. Chiba Abstract An inexpensive way to achieve high resolution 2D-ACAR measurements is to utilize resolution deconvolution techniques. We developed a resolution deconvolution method which avoids noise amplification and is applicable to the 3D reconstruction method using Fourier-Bessel transforms. (© 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] An introduction to coil array design for parallel MRINMR IN BIOMEDICINE, Issue 3 2006Michael A. Ohliger Abstract The basic principles of radiofrequency coil array design for parallel MRI are described from both theoretical and practical perspectives. Because parallel MRI techniques rely on coil array sensitivities to provide spatial information about the sample, a careful choice of array design is essential. The concepts of coil array spatial encoding are first discussed from four qualitative perspectives. These qualitative descriptions include using coil arrays to emulate spatial harmonics, choosing coils with selective sensitivities to aliased pixels, using coil sensitivities with broad k -space reception profiles, and relying on detector coils to provide a set of generalized projections of the sample. This qualitative discussion is followed by a quantitative analysis of coil arrays, which is discussed in terms of the baseline SNR of the received images as well as the noise amplifications (g -factor) in the reconstructed data. The complications encountered during the experimental evaluation of coil array SNR are discussed, and solutions are proposed. A series of specific array designs are reviewed, with an emphasis on the general design considerations that motivate each approach. Finally, a set of special topics is discussed, which reflect issues that have become important, especially as arrays are being designed for more high-performance applications of parallel MRI. These topics include concerns about the depth penetration of arrays composed of small elements, the use of adaptive arrays for systems with limited receiver channels, the management of inductive coupling between array elements, and special considerations required at high field strengths. The fundamental limits of spatial encoding using coil arrays are discussed, with a primary emphasis on how the determination of these limits impacts the design of optimized arrays. This review is intended to provide insight into how arrays are currently used for parallel MRI and to place into context the new innovations that are to come. Copyright © 2006 John Wiley & Sons, Ltd. [source] | ||||||||||