An automated algorithm for combining multivoxel MRS data acquired with phased‐array coils

Maril, Nimrod; Lenkinski, Robert E.

doi:10.1002/jmri.20261

Cited by 27 publications

(63 citation statements)

References 8 publications

(20 reference statements)

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“…24 A comparison of the performance of many of these CSI methods has been provided by Pohmann et al 43 Barker and Lin 26 have an excellent discussion of methods that use multireceiver arrays (e.g., an eightchannel head array) to accelerate CSI acquisitions, similar to the methods used in parallel imaging. An added complication is that, although there have been descriptions of algorithms 44,45 used in combining CSI spectra obtained from each individual coil element, few of these are routinely available on commercial whole-body clinical scanners. This situation is in contrast to multicoil MR imaging and parallel imaging, in which this combination (or acceleration, or both) is performed routinely.…”

Section: Spatial Localizationmentioning

confidence: 99%

Recent Advances in Magnetic Resonance Neurospectroscopy

Rosen

Lenkinski

2007

Neurotherapeutics

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Summary:Over the past two decades, proton magnetic resonance spectroscopy (proton MRS) of the brain has made the transition from research tool to a clinically useful modality. In this review, we first describe the localization methods currently used in MRS studies of the brain and discuss the technical and practical factors that determine the applicability of the methods to particular clinical studies. We also describe each of the resonances detected by localized solvent-suppressed proton MRS of the brain and discuss the metabolic and biochemical information that can be derived from an analysis of their concentrations. We discuss spectral quantitation and summarize the reproducibility of both single-voxel and multivoxel methods at 1.5 and 3-4 T. We have selected three clinical neurologic applications in which there has been a consensus as to the diagnostic value of MRS and summarize the information relevant to clinical applications. Finally, we speculate about some of the potential technical developments, either in progress or in the future, that may lead to improvements in the performance of proton MRS.

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Section: Spatial Localizationmentioning

confidence: 99%

Recent Advances in Magnetic Resonance Neurospectroscopy

Rosen

Lenkinski

2007

Neurotherapeutics

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“…The simplest ad hoc approach is to phase the spectra in each voxel manually and add them to give the combined spectrum for that voxel (5). This can be automated by fitting or integrating over a reference peak, using this to phase the spectra (3).…”

Section: Existing Approaches To Receive Array Mr Spectroscopymentioning

confidence: 99%

Receive array magnetic resonance spectroscopy: Whitened singular value decomposition (WSVD) gives optimal Bayesian solution

2010

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Receive array coils play a pivotal role in modern MRI. MR spectroscopy can also benefit from the enhanced signal-tonoise ratio and field of view provided by a receive array. In any experiment using an n-element array, n different complex spectra will be recorded and each spectrum unavoidably contains an undesired noise contribution. Previous algorithms for combining spectra have ignored the fact that the noise detected by different array elements is correlated. We introduce here an algorithm for efficiently, robustly, and automatically combining these n spectra using noise whitening and the singular value decomposition to provide the single combined spectrum that has maximum likelihood in the presence of this correlated noise. Simulations are performed that demonstrate the superiority of this approach to previous methods. Experiments in phantoms and in vivo on the brain, heart, and liver of normal volunteers, at 1.5 T and 3 T, using array coils from eight to 32 elements and with 1 H and 31 P nuclei, validate our approach, which provides signal-to-noise ratio improvements of up to 60% in our tests. The whitening and the singular value decomposition algorithm become most advantageous for large arrays, when the noise is markedly correlated, and when the signal-to-noise ratio is low. Key words: MR spectroscopy; array; coil combination; WSVD; SVD; noise whitening MR spectroscopy provides unique insights into the chemical composition of a subject in a completely noninvasive manner. For example, chemical shift imaging (CSI) and single voxel spectroscopy allow measurement of the concentrations of metabolites containing nuclei such as 1 H and 31 P, offering a window into biochemical processes occurring in vivo. Nevertheless, constraints on the signalto-noise ratio (SNR) obtainable with current technology are one of the primary factors limiting clinical applications of MR spectroscopy. We report our recent work to alleviate this SNR limitation through an improved method for combining the data gathered from receive array coils.Receive array coils have a long history in the MRI community following the work of Hyde et al. (1) and Roemer et al. (2). By replacing the single radiofrequency receive coil in an MR scanner with an array of smaller coils (''array elements''), improvements in SNR are obtained. The smaller size of each of these elements makes them couple more strongly with the magnetization in the subject under examination, giving stronger signals and hence a larger SNR than with a single element coil. Furthermore, although each element has a smaller field of view, the field of view covered by at least one coil may be very large. Thus, when the signals from each element are combined appropriately, a receive array provides data of significantly enhanced SNR that cover a wider field of view. The Roemer et al. (2) founding paper on receive array MRI introduced a widely accepted model of the array detection process, which we use here to allow a unified description of existing methods for receive array combination in MR sp...

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“…A multi-channel coil consists of an array of mutually decoupled, geometrically optimized surface coils, which receive MR signal from the brain simultaneously [1][2][3][4][5][6][7]. These multiple signals are then combined mathematically to yield a single spectroscopic signal at each voxel.…”

Section: Introductionmentioning

confidence: 99%

“…One recent approach to phase alignment used the signal in the time domain as a reference to calculate and eliminate phase differences between signals from differing coil elements [2]. Another approach used spectral fitting software to identify in the frequency domain phase differences in metabolite signals that originated in different coil elements [3]. To calculate the WFs required for signal summation, some approaches have employed background noise as a reference to optimize SNR [2,4], whereas others have used as a reference the SNR of a spectrum [5] or the peak area of a particular metabolite [3,6] to calculate the optimal WFs.…”

Section: Introductionmentioning

confidence: 99%

“…Another approach used spectral fitting software to identify in the frequency domain phase differences in metabolite signals that originated in different coil elements [3]. To calculate the WFs required for signal summation, some approaches have employed background noise as a reference to optimize SNR [2,4], whereas others have used as a reference the SNR of a spectrum [5] or the peak area of a particular metabolite [3,6] to calculate the optimal WFs. Each of these approaches used water suppression (WS) during signal acquisition to reduce the dynamic range of the signal, thereby avoiding introduction of noise from the analog-to-digital conversion, and simplifying the postprocessing of the MR signal.…”

Section: Introductionmentioning

confidence: 99%

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The rapid and automatic combination of proton MRSI data using multi-channel coils without water suppression

Dong

Peterson

2007

Magnetic Resonance Imaging

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The use of multi-channel coils can efficiently increase the signal-to-noise ratio (SNR) of magnetic resonance spectroscopy data if the signals from multiple channels are optimally combined. Combining multi-channel signals requires proper alignment of the phases of the signals from each of the elements of the coil and then accurately weighting the summation of those signals. We present a procedure for acquiring proton magnetic resonance spectroscopic imaging (MRSI) data using an eight-channel coil without water suppression and a rapid and robust method that uses unsuppressed water signal as a reference both for aligning the phases and for weighting the summation of signals that originate in the multiple coil elements. We use both computer simulation and in vivo proton MRSI data to demonstrate the advantages of our method for optimizing the SNR of the combined signal compared with the SNRs of signals that were acquired either using a standard volume head coil or using an eight-channel coil with a metabolite signal as the reference for combination.

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An automated algorithm for combining multivoxel MRS data acquired with phased‐array coils

Cited by 27 publications

References 8 publications

Recent Advances in Magnetic Resonance Neurospectroscopy

Recent Advances in Magnetic Resonance Neurospectroscopy

Receive array magnetic resonance spectroscopy: Whitened singular value decomposition (WSVD) gives optimal Bayesian solution

The rapid and automatic combination of proton MRSI data using multi-channel coils without water suppression

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