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Machine learning-based multi-pool Voigt fitting of CEST, rNOE, and MTC in Z-spectra

Mohammed Ali, Sajad LU ; van Zijl, Peter C M ; Prasuhn, Jannik ; Wirestam, Ronnie LU orcid ; Knutsson, Linda LU orcid and Yadav, Nirbhay N (2025) In Magnetic Resonance in Medicine p.1-16
Abstract

PURPOSE: Four-pool Voigt (FPV) machine learning (ML)-based fitting for Z-spectra was developed to reduce fitting times for clinical feasibility in terms of on-scanner analysis and to promote larger cohort studies. The approach was compared to four-pool Lorentzian (FPL)-ML-based modeling to empirically verify the advantage of Voigt models for Z-spectra.

METHODS: Voigt and Lorentzian models were fitted to human 3 T Z-spectral data using least squares (LS) to generate training data for the corresponding ML versions. Gradient boosting decision trees were trained, resulting in one Voigt and one Lorentzian ML model. Modeling accuracy was tested, and the fitting times of the ML models and LS versions were evaluated. The goodness of fits... (More)

PURPOSE: Four-pool Voigt (FPV) machine learning (ML)-based fitting for Z-spectra was developed to reduce fitting times for clinical feasibility in terms of on-scanner analysis and to promote larger cohort studies. The approach was compared to four-pool Lorentzian (FPL)-ML-based modeling to empirically verify the advantage of Voigt models for Z-spectra.

METHODS: Voigt and Lorentzian models were fitted to human 3 T Z-spectral data using least squares (LS) to generate training data for the corresponding ML versions. Gradient boosting decision trees were trained, resulting in one Voigt and one Lorentzian ML model. Modeling accuracy was tested, and the fitting times of the ML models and LS versions were evaluated. The goodness of fits of Voigt and Lorentzian ML models were compared.

RESULTS: The training time for each ML model (Voigt and Lorentzian) was less than 1 min, and the modeling accuracy compared to the corresponding LS versions was excellent, as indicated by a nonsignificant difference between the parameters obtained by LS and corresponding ML versions. The average fitting time was 20 μs/spectrum for both ML models compared to 0.27 and 0.82 s/spectrum for LS with FPL and FPV, respectively. The goodness of fits of FPV-ML and FPL-ML differed significantly (p < 0.005), with FPV-ML showing an improvement for all tested data.

CONCLUSION: Gradient boosting decision trees fitting of multi-pool Z-spectra significantly reduces fitting times compared to traditional LS approaches, allowing fast data processing while upholding fitting quality. Along with the short training times, this makes the method suitable for clinical settings and for large cohort research applications. The FPV-ML approach provides a significant improvement of goodness of fit compared to FPL-ML.

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author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
epub
subject
in
Magnetic Resonance in Medicine
pages
1 - 16
publisher
John Wiley & Sons Inc.
external identifiers
  • pmid:39963087
  • scopus:85219713634
ISSN
1522-2594
DOI
10.1002/mrm.30460
language
English
LU publication?
yes
additional info
© 2025 International Society for Magnetic Resonance in Medicine.
id
d082b6af-7a0e-4579-a927-28df6e304f95
date added to LUP
2025-02-19 19:16:36
date last changed
2025-04-26 07:43:38
@article{d082b6af-7a0e-4579-a927-28df6e304f95,
  abstract     = {{<p>PURPOSE: Four-pool Voigt (FPV) machine learning (ML)-based fitting for Z-spectra was developed to reduce fitting times for clinical feasibility in terms of on-scanner analysis and to promote larger cohort studies. The approach was compared to four-pool Lorentzian (FPL)-ML-based modeling to empirically verify the advantage of Voigt models for Z-spectra.</p><p>METHODS: Voigt and Lorentzian models were fitted to human 3 T Z-spectral data using least squares (LS) to generate training data for the corresponding ML versions. Gradient boosting decision trees were trained, resulting in one Voigt and one Lorentzian ML model. Modeling accuracy was tested, and the fitting times of the ML models and LS versions were evaluated. The goodness of fits of Voigt and Lorentzian ML models were compared.</p><p>RESULTS: The training time for each ML model (Voigt and Lorentzian) was less than 1 min, and the modeling accuracy compared to the corresponding LS versions was excellent, as indicated by a nonsignificant difference between the parameters obtained by LS and corresponding ML versions. The average fitting time was 20 μs/spectrum for both ML models compared to 0.27 and 0.82 s/spectrum for LS with FPL and FPV, respectively. The goodness of fits of FPV-ML and FPL-ML differed significantly (p &lt; 0.005), with FPV-ML showing an improvement for all tested data.</p><p>CONCLUSION: Gradient boosting decision trees fitting of multi-pool Z-spectra significantly reduces fitting times compared to traditional LS approaches, allowing fast data processing while upholding fitting quality. Along with the short training times, this makes the method suitable for clinical settings and for large cohort research applications. The FPV-ML approach provides a significant improvement of goodness of fit compared to FPL-ML.</p>}},
  author       = {{Mohammed Ali, Sajad and van Zijl, Peter C M and Prasuhn, Jannik and Wirestam, Ronnie and Knutsson, Linda and Yadav, Nirbhay N}},
  issn         = {{1522-2594}},
  language     = {{eng}},
  month        = {{02}},
  pages        = {{1--16}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{Magnetic Resonance in Medicine}},
  title        = {{Machine learning-based multi-pool Voigt fitting of CEST, rNOE, and MTC in Z-spectra}},
  url          = {{http://dx.doi.org/10.1002/mrm.30460}},
  doi          = {{10.1002/mrm.30460}},
  year         = {{2025}},
}