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Model‐based frequency‐and‐phase correction of 1H MRS data with 2D linear‐combination modeling.

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Title: Model‐based frequency‐and‐phase correction of 1H MRS data with 2D linear‐combination modeling.
Authors: Simicic, Dunja1,2 (AUTHOR), Zöllner, Helge J.1,2 (AUTHOR), Davies‐Jenkins, Christopher W.1,2 (AUTHOR), Hupfeld, Kathleen E.1,2 (AUTHOR), Edden, Richard A. E.1,2 (AUTHOR), Oeltzschner, Georg1,2 (AUTHOR) goeltzs1@jhmi.edu
Source: Magnetic Resonance in Medicine. Nov2024, Vol. 92 Issue 5, p2222-2236. 15p.
Subjects: Nuclear magnetic resonance spectroscopy, Amplitude estimation, Test methods, Recording & registration
Abstract: Purpose: Retrospective frequency‐and‐phase correction (FPC) methods attempt to remove frequency‐and‐phase variations between transients to improve the quality of the averaged MR spectrum. However, traditional FPC methods like spectral registration struggle at low SNR. Here, we propose a method that directly integrates FPC into a 2D linear‐combination model (2D‐LCM) of individual transients ("model‐based FPC"). We investigated how model‐based FPC performs compared to the traditional approach, i.e., spectral registration followed by 1D‐LCM in estimating frequency‐and‐phase drifts and, consequentially, metabolite level estimates. Methods: We created synthetic in‐vivo‐like 64‐transient short‐TE sLASER datasets with 100 noise realizations at 5 SNR levels and added randomly sampled frequency and phase variations. We then used this synthetic dataset to compare the performance of 2D‐LCM with the traditional approach (spectral registration, averaging, then 1D‐LCM). Outcome measures were the frequency/phase/amplitude errors, the SD of those ground‐truth errors, and amplitude Cramér Rao lower bounds (CRLBs). We further tested the proposed method on publicly available in‐vivo short‐TE PRESS data. Results: 2D‐LCM estimates (and accounts for) frequency‐and‐phase variations directly from uncorrected data with equivalent or better fidelity than the conventional approach. Furthermore, 2D‐LCM metabolite amplitude estimates were at least as accurate, precise, and certain as the conventionally derived estimates. 2D‐LCM estimation of FPC and amplitudes performed substantially better at low‐to‐very‐low SNR. Conclusion: Model‐based FPC with 2D linear‐combination modeling is feasible and has great potential to improve metabolite level estimation for conventional and dynamic MRS data, especially for low‐SNR conditions, for example, long TEs or strong diffusion weighting. [ABSTRACT FROM AUTHOR]
Copyright of Magnetic Resonance in Medicine is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Model‐based frequency‐and‐phase correction of <superscript>1</superscript>H MRS data with 2D linear‐combination modeling.
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  Data: <searchLink fieldCode="AR" term="%22Simicic%2C+Dunja%22">Simicic, Dunja</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zöllner%2C+Helge+J%2E%22">Zöllner, Helge J.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Davies‐Jenkins%2C+Christopher+W%2E%22">Davies‐Jenkins, Christopher W.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hupfeld%2C+Kathleen+E%2E%22">Hupfeld, Kathleen E.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Edden%2C+Richard+A%2E+E%2E%22">Edden, Richard A. E.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Oeltzschner%2C+Georg%22">Oeltzschner, Georg</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> goeltzs1@jhmi.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Magnetic+Resonance+in+Medicine%22">Magnetic Resonance in Medicine</searchLink>. Nov2024, Vol. 92 Issue 5, p2222-2236. 15p.
– Name: Subject
  Label: Subjects
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  Data: <searchLink fieldCode="DE" term="%22Nuclear+magnetic+resonance+spectroscopy%22">Nuclear magnetic resonance spectroscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Amplitude+estimation%22">Amplitude estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Test+methods%22">Test methods</searchLink><br /><searchLink fieldCode="DE" term="%22Recording+%26+registration%22">Recording & registration</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose: Retrospective frequency‐and‐phase correction (FPC) methods attempt to remove frequency‐and‐phase variations between transients to improve the quality of the averaged MR spectrum. However, traditional FPC methods like spectral registration struggle at low SNR. Here, we propose a method that directly integrates FPC into a 2D linear‐combination model (2D‐LCM) of individual transients ("model‐based FPC"). We investigated how model‐based FPC performs compared to the traditional approach, i.e., spectral registration followed by 1D‐LCM in estimating frequency‐and‐phase drifts and, consequentially, metabolite level estimates. Methods: We created synthetic in‐vivo‐like 64‐transient short‐TE sLASER datasets with 100 noise realizations at 5 SNR levels and added randomly sampled frequency and phase variations. We then used this synthetic dataset to compare the performance of 2D‐LCM with the traditional approach (spectral registration, averaging, then 1D‐LCM). Outcome measures were the frequency/phase/amplitude errors, the SD of those ground‐truth errors, and amplitude Cramér Rao lower bounds (CRLBs). We further tested the proposed method on publicly available in‐vivo short‐TE PRESS data. Results: 2D‐LCM estimates (and accounts for) frequency‐and‐phase variations directly from uncorrected data with equivalent or better fidelity than the conventional approach. Furthermore, 2D‐LCM metabolite amplitude estimates were at least as accurate, precise, and certain as the conventionally derived estimates. 2D‐LCM estimation of FPC and amplitudes performed substantially better at low‐to‐very‐low SNR. Conclusion: Model‐based FPC with 2D linear‐combination modeling is feasible and has great potential to improve metabolite level estimation for conventional and dynamic MRS data, especially for low‐SNR conditions, for example, long TEs or strong diffusion weighting. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Magnetic Resonance in Medicine is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/mrm.30209
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        Text: English
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      – SubjectFull: Test methods
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      – SubjectFull: Recording & registration
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      – TitleFull: Model‐based frequency‐and‐phase correction of 1H MRS data with 2D linear‐combination modeling.
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              M: 11
              Text: Nov2024
              Type: published
              Y: 2024
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