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Optimizing the MagnetizationPrepared Rapid GradientEcho (MPRAGE) Sequence
, 2014
"... The threedimension (3D) magnetizationprepared rapid gradientecho (MPRAGE) sequence is one of the most popular sequences for structural brain imaging in clinical and research settings. The sequence captures high tissue contrast and provides high spatial resolution with whole brain coverage in a s ..."
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Cited by 1 (0 self)
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short scan time. In this paper, we first computed the optimal kspace sampling by optimizing the contrast of simulated images acquired with the MPRAGE sequence at 3.0 Tesla using computer simulations. Because the software of our scanner has only limited settings for kspace sampling, we then determined
Revised calibration of the geomagnetic polarity timescale for the late Cretaceous and Cenozoic
 Journal of Geophysical Research
, 1995
"... Abstract. Recently reported radioisotopic dates and magnetic anomaly spacings have made it evident hat modification is required for the age calibrations for the geomagnetic polarity timescale of Cande and Kent (1992) at the Cretaceous/Paleogene boundary and in the Pliocene. An adjusted geomagnetic r ..."
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Cited by 401 (7 self)
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Abstract. Recently reported radioisotopic dates and magnetic anomaly spacings have made it evident hat modification is required for the age calibrations for the geomagnetic polarity timescale of Cande and Kent (1992) at the Cretaceous/Paleogene boundary and in the Pliocene. An adjusted geomagnetic
From variable density sampling to continuous sampling using Markov chains
, 2013
"... Abstract—Since its discovery over the last decade, Compressed Sensing (CS) has been successfully applied to Magnetic Resonance Imaging (MRI). It has been shown to be a powerful way to reduce scanning time without sacrificing image quality. MR images are actually strongly compressible in a wavelet ba ..."
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Cited by 4 (3 self)
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basis, the latter being largely incoherent with the kspace or spatial Fourier domain where acquisition is performed. Nevertheless, since its first application to MRI [1], the theoretical justification of actual kspace sampling strategies is questionable. Indeed, the vast majority of kspace sampling
Lattice Sampling of kSpace for Parallel Imaging
"... Reconstruction of nonCartesian SENSitivityEncoded (SENSE) data often involves solving a large system of equations for the amplitudes of the voxel functions used to model the desired image function [1]. While solving this system has been made faster by utilizing Conjugate Gradient (CG) methods and ..."
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Reconstruction of nonCartesian SENSitivityEncoded (SENSE) data often involves solving a large system of equations for the amplitudes of the voxel functions used to model the desired image function [1]. While solving this system has been made faster by utilizing Conjugate Gradient (CG) methods and by clever use of the Fast Fourier Transform (FFT) to implement some of the matrix multiplications, the reconstruction is still too slow for routine clinical applications. This paper proposes a class of
Simple Constructions of Almost kwise Independent Random Variables
, 1992
"... We present three alternative simple constructions of small probability spaces on n bits for which any k bits are almost independent. The number of bits used to specify a point in the sample space is (2 + o(1))(log log n + k/2 + log k + log 1 ɛ), where ɛ is the statistical difference between the dist ..."
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Cited by 303 (40 self)
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We present three alternative simple constructions of small probability spaces on n bits for which any k bits are almost independent. The number of bits used to specify a point in the sample space is (2 + o(1))(log log n + k/2 + log k + log 1 ɛ), where ɛ is the statistical difference between
kSpace Data Preprocessing for Artifact Reduction
"... Fourier transforms are ubiquitous in nature; magnetic resonance (MR) imaging is just one of many examples. Music is perhaps the bestknown example. Standard scores represent pitch in the Fourier, or frequency, domain but leave duration in the time domain (Fig 1). If we were to Fourier transform with ..."
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), but clinical imagers are often within just a few “parts per million ” of sampling the true Fourier transform. In MR lingo, Fourier space is often referred to as kspace, and we use the terms interchangeably. The imaging chain consists of many small steps grouped into four main steps: acquisition, preprocessing
[1] Moran PR. MRI 1:197, 1982.
"... Figure 2: Variablewidth sinc interpolation diagram. Highresolution components are more sparsely sampled then lowresolution ones, resulting in a smaller unaliased velocity FOV. Using a sinc kernel of appropriate width for each kspace sample, the resulting apodization function filters the correspo ..."
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Figure 2: Variablewidth sinc interpolation diagram. Highresolution components are more sparsely sampled then lowresolution ones, resulting in a smaller unaliased velocity FOV. Using a sinc kernel of appropriate width for each kspace sample, the resulting apodization function filters
THE FAST SINC TRANSFORM AND IMAGE RECONSTRUCTION FROM NONUNIFORM SAMPLES IN kSPACE
"... A number of problems in image reconstruction and image processing can be addressed, in principle, using the sinc kernel. Since the sinc kernel decays slowly, however, it is generally avoided in favor of some more local but less precise choice. In this paper, we describe the fast sinc transform, an a ..."
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, an algorithm which computes the convolution of arbitrarily spaced data with the sinc kernel in O.N log N / operations, where N denotes the number of data points. We briefly discuss its application to the construction of optimal density compensation weights for Fourier reconstruction and to the iterative
THE FAST SINC TRANSFORM AND IMAGE RECONSTRUCTION FROM NONUNIFORM SAMPLES IN kSPACE
"... A number of problems in image reconstruction and image processing can be addressed, in principle, using the sinc kernel. Since the sinc kernel decays slowly, however, it is generally avoided in favor of some more local but less precise choice. In this paper, we describe the fast sinc transform, an a ..."
Abstract
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, an algorithm which computes the convolution of arbitrarily spaced data with the sinc kernel in O.N logN / operations, where N denotes the number of data points. We briefly discuss its application to the construction of optimal density compensation weights for Fourier reconstruction and to the iterative
Variable density Random Radial 5Is This UnderSampled Kspace?
, 2011
"... I have the following relevant financial interest or relationship to disclose with regard to the subject matter of this presentation: ..."
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I have the following relevant financial interest or relationship to disclose with regard to the subject matter of this presentation:
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