Automatic frequency and phase alignment of in vivo J-difference-edited MR spectra by frequency domain correlation

Wiegers, Evita C; Philips, Bart W. J.; Heerschap, Arend

doi:10.1007/s10334-017-0627-y

Cited by 20 publications

(31 citation statements)

References 18 publications

(22 reference statements)

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“…Significant frequency offsets (>10 Hz) can be problematic for spectral registration 11,18 for two main reasons. First, the starting values in the optimization algorithm will have an influence on whether the algorithm finds a local or global minimum.…”

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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“…Wiegers et al used correlation of spectral windows to perform simultaneous phase and frequency correction of edited MRS data in order to reduce the subtraction artifacts in the difference spectra [68]. Their proposed method performed equally well as spectral registration [69].…”

Section: Correlation Of Spectral Windowsmentioning

confidence: 99%

Neurotransmitter Imaging of the Human Brain : Detecting γ-Aminobutyric Acid (GABA) Using Magnetic Resonance Spectroscopy

Tapper

2019

Linköping University Medical Dissertations

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Introduction: In this thesis, MEGA-edited Magnetic Resonance Spectroscopy (MRS) has been used for the purpose of non-invasive detection of !aminobutyric acid (GABA) within the brain. GABA is the main inhibitory neurotransmitter in the human central nervous system, and glutamate is the corresponding main excitatory neurotransmitter. A balance between GABA and glutamate is crucial for healthy neurotransmission within the brain, and regional altered concentrations have been linked to certain neurological disorders. However, it is challenging to measure GABA, and special editing approaches are needed in order to allow reliable quantification. In addition, the GABA measurement is further complicated due to disturbances such as movements during the acquisition that may lead to artifacts in the resulting spectrum. This thesis can be divided into two sections, where the first section focuses on three clinical applications (narcolepsy, irritable bowel syndrome (IBS), and essential tremor (ET)), which were all investigated using MEGA-edited singlevoxel spectroscopy (SVS). The second section focuses on method development, where two statistical retrospective approaches were investigated for the purpose of improving MEGA-edited data. In addition, a new MRS imaging (MRSI) pulse sequence with the purpose of GABA detection using a high spatial resolution, short acquisition time, and full brain coverage was also investigated. Materials and Methods: In total, 164 participants were included and 272 MRS measurements were performed with the voxel placed in the medial prefrontal cortex (mPFC, 136), thalamus (32), and cerebellum (104) using two different but "identical" MR systems. Nineteen narcolepsy patients and 21 matched healthy controls performed an fMRI working memory task using a simultaneous EEG followed by an mPFC GABA-edited MRS measurement. Sixty-four IBS patients and 32 matched healthy controls underwent an mPFC GABA-edited MRS measurement followed by resting state fMRI. In addition, psychological symptoms were assessed using questionnaires. Ten ET patients and six matched healthy controls underwent four GABA-edited MRS measurements with the voxels placed in the thalamus and cerebellum. In this study, the symptom severity was investigated using the essential tremor rating scale (ETRS). All clinical MRS datasets were analyzed using conventional methods for post-processing and quantification. Furthermore, 12 volunteers were recruited for the purpose of investigating statistical retrospective approaches for artifact detection and elimination of MRS data. Each participant underwent three reference measurements and three measurements with induced head Neurotransmitter Imaging of the Human Brain ii movements conducted according to a movement paradigm. Order statistic filtering (OSF) and jackknife correlation (JKC) were investigated as regards to the elimination of artifact-influenced spectra and reliability of the resulting concentrations. Finally, phantom measurements were performed for the purpose of investigating MEGA-edited M...

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“…Inspired by a previous study that used correlation of spectral windows for phase and frequency alignment of edited MRS data, 24 we implemented a technique based on an approach that combines jackknife analyses and correlation of spectral windows (Figure 1), which we termed "JKC." 25 The jackknife resampling technique is a known method for estimating the variance and bias of a data set.…”

Section: Jackknife and Correlation Analysesmentioning

confidence: 99%

“…Inspired by a previous study that used correlation of spectral windows for phase and frequency alignment of edited MRS data, we implemented a technique based on an approach that combines jackknife analyses and correlation of spectral windows (Figure ), which we termed “JKC.” The jackknife resampling technique is a known method for estimating the variance and bias of a data set. This jackknife estimator is calculated by systematically leaving out 1 observation from a data set, then computing 1 estimation using the remaining observations, and finally averaging over all estimations.…”

Section: Introductionmentioning

confidence: 99%

Retrospective artifact elimination in MEGA‐PRESS using a correlation approach

Tapper

Tisell

Helms

et al. 2018

Magnetic Resonance in Med

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Purpose To develop a method for retrospective artifact elimination of MRS data. This retrospective method was based on an approach that combines jackknife analyses with the correlation of spectral windows, and therefore termed “JKC.” Methods Twelve healthy volunteers performed 3 separate measurement protocols using a 3T MR system. One protocol consisted of 2 cerebellar MEGA‐PRESS measurements: 1 reference and 1 measurement including head movements. One‐third of the artifact‐influenced datasets were treated as training data for the implementation the JKC method, and the rest were used for validation. Results The implemented JKC method correctly characterized most of the validation data. Additionally, after elimination of the detected artifacts, the resulting concentrations were much closer to those computed for the reference datasets. Moreover, when the JKC method was applied to the reference data, the estimated concentrations were not affected, compared with standard averaging. Conclusion The implemented JKC method can be applied without any extra cost to MRS data, regardless of whether the dataset has been contaminated by artifacts. Furthermore, the results indicate that the JKC method could be used as a quality control of a dataset, or as an indication of whether a shift in voxel placement has occurred during the measurement.

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Automatic frequency and phase alignment of in vivo J-difference-edited MR spectra by frequency domain correlation

Cited by 20 publications

References 18 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

Neurotransmitter Imaging of the Human Brain : Detecting γ-Aminobutyric Acid (GABA) Using Magnetic Resonance Spectroscopy

Retrospective artifact elimination in MEGA‐PRESS using a correlation approach

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