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HyperFitS -- Hypernetwork Fitting Spectra for metabolic quantification of ${}^1$H MR spectroscopic imaging

Paul J. Weiser, Gulnur Ungan, Amirmohammad Shamaei, Georg Langs, Wolfgang Bogner, Malte Hoffmann, Antoine Klauser, Ovidiu C. Andronesi

Abstract

Purpose: Proton magnetic resonance spectroscopic imaging ($^1$H MRSI) enables the mapping of whole-brain metabolites concentrations in-vivo. However, a long-standing problem for its clinical applicability is the metabolic quantification, which can require extensive time for spectral fitting. Recently, deep learning methods have been able to provide whole-brain metabolic quantification in only a few seconds. However, neural network implementations often lack configurability and require retraining to change predefined parameter settings. Methods: We introduce HyperFitS, a hypernetwork for spectral fitting for metabolite quantification in whole-brain $^1$H MRSI that flexibly adapts to a broad range of baseline corrections and water suppression factors. Metabolite maps of human subjects acquired at 3T and 7T with isotropic resolutions of 10 mm, 3.4 mm and 2 mm by water-suppressed and water-unsuppressed MRSI were quantified with HyperFitS and compared to conventional LCModel fitting. Results: Metabolic maps show a substantial agreement between the new and gold-standard methods, with significantly faster fitting times by HyperFitS. Quantitative results further highlight the impact of baseline parametrization on metabolic quantification, which can alter results by up to 30%. Conclusion: HyperFitS shows strong agreement with state-of-the-art conventional methods, while reducing processing times from hours to a few seconds. Compared to prior deep learning based spectral fitting methods, HyperFitS enables a wide range of configurability and can adapt to data quality acquired with multiple protocols and field strengths without retraining.

HyperFitS -- Hypernetwork Fitting Spectra for metabolic quantification of ${}^1$H MR spectroscopic imaging

Abstract

Purpose: Proton magnetic resonance spectroscopic imaging (H MRSI) enables the mapping of whole-brain metabolites concentrations in-vivo. However, a long-standing problem for its clinical applicability is the metabolic quantification, which can require extensive time for spectral fitting. Recently, deep learning methods have been able to provide whole-brain metabolic quantification in only a few seconds. However, neural network implementations often lack configurability and require retraining to change predefined parameter settings. Methods: We introduce HyperFitS, a hypernetwork for spectral fitting for metabolite quantification in whole-brain H MRSI that flexibly adapts to a broad range of baseline corrections and water suppression factors. Metabolite maps of human subjects acquired at 3T and 7T with isotropic resolutions of 10 mm, 3.4 mm and 2 mm by water-suppressed and water-unsuppressed MRSI were quantified with HyperFitS and compared to conventional LCModel fitting. Results: Metabolic maps show a substantial agreement between the new and gold-standard methods, with significantly faster fitting times by HyperFitS. Quantitative results further highlight the impact of baseline parametrization on metabolic quantification, which can alter results by up to 30%. Conclusion: HyperFitS shows strong agreement with state-of-the-art conventional methods, while reducing processing times from hours to a few seconds. Compared to prior deep learning based spectral fitting methods, HyperFitS enables a wide range of configurability and can adapt to data quality acquired with multiple protocols and field strengths without retraining.

Paper Structure

This paper contains 15 sections, 10 equations, 9 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods
  3. Strategy
  4. Network Architecture
  5. Physical Model
  6. Baseline construction
  7. Water suppression correction
  8. Uncertainty estimation
  9. Training details
  10. Data acquisition
  11. Processing pipeline
  12. Monte Carlo analysis
  13. Results
  14. Discussion
  15. Conclusion

Figures (9)

  • Figure 1: HyperFitS: Top: The HyperFitS strategy showing a hypernetwork taking baseline and water suppression correction parameters and predicting weights for a quantification network with a physics-based encoder. Bottom: The physical model combining metabolite and macromolecule basis set, baseline spline functions, and water suppression correction factor used to predict the spectral fit.
  • Figure 1: Top: Correlation matrix of Monte Carlo analysis for subjects acquired at 3T. Each subject was evaluated for 9 different baseline configurations. Bottom: Boxplots showing correlation values of metabolite pairs that are less than -0.2 or larger than 0.2 in all baseline configurations. Glu & Ins and Glu & Gln are plotted separately as diamond/star. The color selection is based on the median values.
  • Figure 2: A qualitative comparison showing metabolic maps of NAA+NAAG, Cr+PCr, GPC+PCh, inositol, and Glu+Gln, at 7T quantified with HyperFitS and LCModel. Individual representative spectra are displayed below. Processing times for a single MRSI volume of HyperFitS and LCModel are 10sec vs $\sim$1h for 3.4mm, and 90sec vs $\sim$8h for 2mm
  • Figure 2: Top: Correlation matrix of Monte Carlo analysis for subjects acquired at 7T. Each subject was evaluated for 9 different baseline configurations. Bottom: Boxplots showing correlation values of metabolite pairs that are less than -0.2 or larger than 0.2 in all baseline configurations. Glu & Ins and Glu & Gln are plotted separately as diamond/star. The color selection is based on the median values.
  • Figure 3: Metabolic maps of NAA+NAAG, Cr+PCr, GPC+PCh, Inositol, and Glu+Gln quantified with HyperFitS and LCModel from 3D MRSI acquired at 3T in a glioma patient. Maps are compared for different baseline modeling. Individual representative spectra are displayed below.
  • ...and 4 more figures