A Novel Detection–Estimation Scheme for Noisy NMR Signals: Applications to Delayed Acquisition Data

Lin, Yung‐Ya; Hodgkinson, Paul; Ernst, Matthias; Pines, Alexander

doi:10.1006/jmre.1997.1215

Cited by 108 publications

(120 citation statements)

References 2 publications

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“…In our implementation, the unsuppressed water signals are fitted and then separated from the metabolite signals (9,11) using the time-domain MPM method (18). The simulation results from Fig.…”

Section: Discussionmentioning

confidence: 99%

“…The complex amplitude A H2O e i͑H2O ϩ ⌬͒ is then obtained by least square fitting to derive ⌬. For a high-SNR and exponentially decaying reference signal, the biases of phase/frequency estimators have been shown to be both near zero (18). As a consequence, this time-domain approach not only evaluates the phase shift ⌬ but also estimates the frequency shift, even if ⌬f is less than the spectral resolution.…”

Section: Estimation Of Phase and Frequency Shift In The Time Domainmentioning

confidence: 99%

“…The MPM algorithm was used to fit the first 64 points, with rank set to 1 (18). Then the phase/frequency shifts were reversed.…”

Section: Simulationmentioning

confidence: 99%

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Quantification of non–water‐suppressed MR spectra with correction for motion‐induced signal reduction

Lin

Tsai

Liu

et al. 2009

Magnetic Resonance in Med

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Intrascan subject movement in clinical MR spectroscopic examinations may result in inconsistent water suppression that distorts the metabolite signals, frame-to-frame variations in spectral phase and frequency, and consequent reductions in the signal-to-noise ratio due to destructive averaging. Frameto-frame phase/frequency corrections, although reported to be successful in achieving constructive averaging, rely on consistent water suppression, which may be difficult in the presence of intrascan motion. In this study, motion correction using nonwater-suppressed data acquisition is proposed to overcome the above difficulties. The time-domain matrix-pencil postprocessing method was used to extract water signals from the non-water-suppressed spectroscopic data, followed by phase and frequency Inevitable subject motions during successive data acquisition in magnetic resonance spectroscopy (MRS) may cause signal loss because of two major mechanisms (1-4). First, motion-induced magnetic field drifts may lead to frequency shift over multiple MRS acquisition frames. Second, subject movements in the presence of the spoiler gradients may result in inconsistent phase variations among acquisition frames. In conventional MRS studies, data from all acquisition frames are simply averaged, and therefore the destructively averaged spectrum would be affected by signal-to-noise ratio (SNR) attenuation and spectral shape distortion because of frame-to-frame phase/ frequency variations. It has been shown that, by performing frame-by-frame corrections for the intrascan phase/ frequency variations, the constructively averaged MRS data exhibit significantly improved SNR and spectral quality (1-8). The search for an effective intrascan phase/frequency correction method is thus an active field of research for in vivo MRS.Over the past decades, several methods have been proposed to measure the intrascan phase/frequency variations in MRS scans in order to perform constructive averaging (1,2,4-6). In one of these methods, the non-water-suppressed (NWS) free induction decay and water-suppressed (WS) MRS data were acquired in an interleaved manner (2). Intrascan phase/frequency variations estimated from the NWS free induction decay were used to correct the WS MRS data. In this approach, however, the phase variations in WS MRS data are corrected by values estimated at different time points. Therefore, the effectiveness of the phase correction might be suboptimal, particularly in the presence of abrupt intrascan movements.Alternatively, the phase variations induced by intrascan motion could be measured on a frame-by-frame basis directly from the residual water signals of WS MRS data (1,4). However, even though the information estimated directly from the WS MRS data better reflects the instant phase variation in comparison to the interleaved free induction decay-based measurements, this approach has two main limitations: First, the residual water signals in WS MRS data have low SNR that limits the accuracy of frameby-frame phase estimation (1). ...

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Section: Discussionmentioning

confidence: 99%

Section: Estimation Of Phase and Frequency Shift In The Time Domainmentioning

confidence: 99%

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Quantification of non–water‐suppressed MR spectra with correction for motion‐induced signal reduction

Lin

Tsai

Liu

et al. 2009

Magnetic Resonance in Med

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“…The Cadzow method or the minimum variance technique can evidently also be used to preprocess the data to improve the basic HSVD and HTLS algorithms. 45 The underlying principles of the matrix pencil (MP) method 50 applied to MRS data in Lin et al, 51 are very similar to the ideas used in the state-space formalism. Polynomial rooting and root selection are avoided since similar to the state-space methods, the method finds the estimates of the signal poles as eigenvalues of a matrix.…”

Section: Quantitation Algorithmsmentioning

confidence: 99%

MR spectroscopy quantitation: a review of time‐domain methods

et al. 2001

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In this article an overview of time-domain quantitation methods is given. Advantages of processing the data in the measurement domain are discussed. The basic underlying principles of the methods are outlined and from them the situations under which these algorithms perform well are derived. Also an overview of methods to preprocess the data is given. In that respect, signal-to-noise and/or resolution enhancement, the removal of unwanted components and corrections for model imperfections are discussed.

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“…To facilitate the subsequent processing of metabolite signals, we modeled the water components of the combined signal using the Matrix-Pencil Method (MPM) [16]. Signal components with frequencies in the range of −0.5 to 0.5 ppm were classified as water signal and were recombined together to form the total water signal, which was then subtracted from the full signal to yield the retained signal from metabolites.…”

Section: Extraction Of Water Signalmentioning

confidence: 99%

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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A Novel Detection–Estimation Scheme for Noisy NMR Signals: Applications to Delayed Acquisition Data

Cited by 108 publications

References 2 publications

Quantification of non–water‐suppressed MR spectra with correction for motion‐induced signal reduction

Quantification of non–water‐suppressed MR spectra with correction for motion‐induced signal reduction

MR spectroscopy quantitation: a review of time‐domain methods

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

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