Nonlinear amplitude compression in magnetic resonance imaging: quantization noise reduction and data memory saving

Kose, Katsumi; Endoh, Kaori; Inouye, T.

doi:10.1109/62.54645

Cited by 16 publications

(20 citation statements)

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“…Let be a Lipschitz continuous, memoryless and invertible mapping. If is sampled with the sampling functions (11) and the generator satisfies (12) for all , then can be reconstructed from the generalized samples (6). The reconstruction is given by (13) where is the convolutional inverse of , and is the continuous-time Fourier transform of .…”

Section: Problem Formulationmentioning

confidence: 99%

“…1(b), where a functional describes the input-output relation of the nonlinearity. Our measurements are modelled as the generalized samples of , with the th sample given by (6) Here, is the th sampling function. We define to be the sampling space, which is the closure of and to be the set transformation corresponding to .…”

Section: Problem Formulationmentioning

confidence: 99%

“…Memoryless nonlinear distortions also appear in the areas of power electronics [3] and radiometric photography [4], [5]. In some cases, nonlinearity is introduced deliberately in order to increase the possible dynamic range of the signal while avoiding amplitude clipping, or damage to the ADC [6]. The goal is then to process the samples in order to recover the original continuous-time function.…”

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confidence: 99%

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Nonlinear and Nonideal Sampling: Theory and Methods

Dvorkind

Eldar

Matusiak³

2008

IEEE Trans. Signal Process.

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Abstract-We study a sampling setup where a continuous-time signal is mapped by a memoryless, invertible and nonlinear transformation, and then sampled in a nonideal manner. Such scenarios appear, for example, in acquisition systems where a sensor introduces static nonlinearity, before the signal is sampled by a practical analog-to-digital converter. We develop the theory and a concrete algorithm to perfectly recover a signal within a subspace, from its nonlinear and nonideal samples. Three alternative formulations of the algorithm are described that provide different insights into the structure of the solution: A series of oblique projections, approximated projections onto convex sets, and quasi-Newton iterations. Using classical analysis techniques of descent-based methods, and recent results on frame perturbation theory, we prove convergence of our algorithm to the true input signal. We demonstrate our method by simulations, and explain the applicability of our theory to Wiener-Hammerstein analog-to-digital hybrid systems.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

mentioning

confidence: 99%

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Nonlinear and Nonideal Sampling: Theory and Methods

Dvorkind

Eldar

Matusiak³

2008

IEEE Trans. Signal Process.

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“…We call this strategy as APodization after Receiver gain InCrement during Ongoing sequence with Time (APRICOT). The idea of dynamic receiver gain has been examined in magnetic resonance imaging (MRI), where the dynamic range is demanding [2][3][4]. Due to restrictions of the hardware of the existing spectrometer and the control software, these works resorted to several separate measurements with various receiver gains followed by data reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Noise reduction by dynamic signal preemphasis

Takeda

Takegoshi

2011

Journal of Magnetic Resonance

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In this work we propose an approach to reduce the digitization noise for a given dynamic range, i.e., the number of bits, of an analog to digital converter used in an NMR receiver. In this approach, the receiver gain is dynamically increased so that the free induction decay is recorded in such an emphasized way that the decaying signal is digitized using as many number of bits as possible, and at the stage of data processing, the original signal profile is restored by applying the apodization that compensates the effect of the preemphasis. This approach, which we call APodization after Receiver gain InCrement during Ongoing sequence with Time (APRICOT), is performed in a solid-state system containing a pair of (13)C spins, one of which is fully isotopically labeled and the other is naturally abundant. It is demonstrated that the exceedingly smaller peak buried in the digitization noise in the conventional approach can be revealed by employing APRICOT.

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“…20,24 Therefore, although a correct gain setting is indispensable, a 16-bit digital resolution is sufficient for most MR imaging.…”

Section: Receiver Dynamic Rangementioning

confidence: 99%

Comparison of Analog and Digital Transceiver Systems for MR Imaging

Hashimoto

Kose

Haishi³

2014

magn reson med sci

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We critically evaluated analog and digital transceivers for magnetic resonance (MR) imaging systems under identical experimental conditions to identify and compare their advantages and disadvantages. MR imaging experiments were performed using a 4.74-tesla vertical-bore superconducting magnet and a high sensitivity gradient coil probe. We acquired 3-dimensional spin echo images of a kumquat with and without using a gain-stepping scan technique to extend the dynamic range of the receiver systems. The acquired MR images clearly demonstrated nearly identical image quality for both transceiver systems, but DC and ghosting artifacts were obtained for the analog transceiver system. We therefore concluded that digital transceivers have several advantages over the analog transceivers.

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Nonlinear amplitude compression in magnetic resonance imaging: quantization noise reduction and data memory saving

Cited by 16 publications

References 1 publication

Nonlinear and Nonideal Sampling: Theory and Methods

Nonlinear and Nonideal Sampling: Theory and Methods

Noise reduction by dynamic signal preemphasis

Comparison of Analog and Digital Transceiver Systems for MR Imaging

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