Difference optimization: Automatic correction of relative frequency and phase for mean non-edited and edited GABA 1 H MEGA-PRESS spectra

Cleve, Marianne; Krämer, Martin; Gussew, Alexander; Reichenbach, Jürgen R.

doi:10.1016/j.jmr.2017.04.004

Cited by 8 publications

(13 citation statements)

References 26 publications

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“…One proposed approach is to align the averaged edit-OFF spectrum to the averaged edit-ON spectrum by minimizing the L 1 norm of the difference so that the smalleramplitude subtraction artifacts are given more weight in the optimization. 19 The solution used in rSR is to focus specifically on the portion of signal where subtraction artifacts arise in the difference spectrum when subspectra are misaligned. A similar approach has been demonstrated previously for correcting offsets in GSH-edited MEGA-PRESS data.…”

Section: Discussionmentioning

confidence: 99%

“…16 Additional challenges that make FPC difficult include instabilities in the residual water, lipid, and baseline signals 11,17 and low SNR of individual transients. 18,19 This paper describes a novel FPC algorithm built upon spectral registration, which is a hybrid method using information from both the time and frequency domains. It has been designed to overcome some of the common challenges for correcting frequency and phase errors in in vivo MRS data.…”

Section: Introductionmentioning

confidence: 99%

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Correcting frequency and phase offsets in MRS data using robust spectral registration

et al. 2020

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An algorithm for retrospective correction of frequency and phase offsets in MRS data is presented. The algorithm, termed robust spectral registration (rSR), contains a set of subroutines designed to robustly align individual transients in a given dataset even in cases of significant frequency and phase offsets or unstable lipid contamination and residual water signals. Data acquired by complex multiplexed editing approaches with distinct subspectral profiles are also accurately aligned. Automated removal of unstable lipid contamination and residual water signals is applied first, when needed. Frequency and phase offsets are corrected in the time domain by aligning each transient to a weighted average reference in a statistically optimal order using nonlinear least‐squares optimization. The alignment of subspectra in edited datasets is performed using an approach that specifically targets subtraction artifacts in the frequency domain. Weighted averaging is then used for signal averaging to down‐weight poorer‐quality transients. Algorithm performance was assessed on one simulated and 67 in vivo pediatric GABA‐/GSH‐edited HERMES datasets and compared with the performance of a multistep correction method previously developed for aligning HERMES data. The performance of the novel approach was quantitatively assessed by comparing the estimated frequency/phase offsets against the known values for the simulated dataset or by examining the presence of subtraction artifacts in the in vivo data. Spectral quality was improved following robust alignment, especially in cases of significant spectral distortion. rSR reduced more subtraction artifacts than the multistep method in 64% of the GABA difference spectra and 75% of the GSH difference spectra. rSR overcomes the major challenges of frequency and phase correction.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Correcting frequency and phase offsets in MRS data using robust spectral registration

et al. 2020

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“…Recent developments in FPC of edited data demonstrate the need for algorithms that can robustly handle editing‐derived differences in spectral signals. Multiplexed editing presents a particular challenge for FPC, as it requires alignment of four or more, rather than two, subspectra, and generates more than one DIFF spectrum in which subtraction artifacts can be revealed.…”

Section: Discussionmentioning

confidence: 99%

“…Although not the main objective of our study, we propose the possibility of using “competitive” FPC, in which a series of FPC routines are used and the best outcome selected on a dataset‐by‐dataset basis. Indeed, previous studies that have compared conventional SR with either SR‐based variants or non‐SR‐based methods have shown that one method may perform better than, or just as well as, another in certain cases , such as when SNR is low or when the dominant signal in the spectrum is unstable . One could even argue that the prime advantage of the msFPC approach is that it exploits the strengths of several of the previously described FPC algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, Near et al introduced spectral registration, a time‐domain method that uses nonlinear regression to align individual free induction decays (FIDs) to a reference FID. For edited data, alignment approaches have included adaptive spectral registration in the frequency domain , basis‐set‐driven correction , power minimization in the difference spectrum , and frequency‐domain correlation . In general, frequency and phase correction (FPC) for editing is more challenging than for nonedited acquisitions, as the signal characteristics of ON and OFF scans are different.…”

Section: Introductionmentioning

confidence: 99%

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Frequency and phase correction for multiplexed edited MRS of GABA and glutathione

Mikkelsen

Saleh

Near

et al. 2017

Magnetic Resonance in Med

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Frequency and phase correction of J‐difference edited MR spectra using deep learning

Tapper

Mikkelsen

Zöllner

et al. 2020

Magnetic Resonance in Med

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To investigate whether a deep learning-based (DL) approach can be used for frequency-and-phase correction (FPC) of MEGA-edited MRS data. Methods: Two neural networks (1 for frequency, 1 for phase) consisting of fully connected layers were trained and validated using simulated MEGA-edited MRS data. This DL-FPC was subsequently tested and compared to a conventional approach (spectral registration [SR]) and to a model-based SR implementation (mSR) using in vivo MEGA-edited MRS datasets. Additional artificial offsets were added to these datasets to further investigate performance. Results: The validation showed that DL-based FPC was capable of correcting within 0.03 Hz of frequency and 0.4°of phase offset for unseen simulated data. DL-based FPC performed similarly to SR for the unmanipulated in vivo test datasets. When additional offsets were added to these datasets, the networks still performed well. However, although SR accurately corrected for smaller offsets, it often failed for larger offsets. The mSR algorithm performed well for larger offsets, which was because the model was generated from the in vivo datasets. In addition, the computation times were much shorter using DL-based FPC or mSR compared to SR for heavily distorted spectra. Conclusion: These results represent a proof of principle for the use of DL for preprocessing MRS data.

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Difference optimization: Automatic correction of relative frequency and phase for mean non-edited and edited GABA 1 H MEGA-PRESS spectra

Cited by 8 publications

References 26 publications

Correcting frequency and phase offsets in MRS data using robust spectral registration

Correcting frequency and phase offsets in MRS data using robust spectral registration

Frequency and phase correction for multiplexed edited MRS of GABA and glutathione

Frequency and phase correction of J‐difference edited MR spectra using deep learning

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