Fast quantification of proton magnetic resonance spectroscopic imaging with artificial neural networks

Himanshu Bhat, Balasrinivasa Rao Sajja, Ponnada A. Narayana

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Accurate quantification of the MRSI-observed regional distribution of metabolites involves relatively long processing times. This is particularly true in dealing with large amount of data that is typically acquired in multi-center clinical studies. To significantly shorten the processing time, an artificial neural network (ANN)-based approach was explored for quantifying the phase corrected (as opposed to magnitude) spectra. Specifically, in these studies radial basis function neural network (RBFNN) was used. This method was tested on simulated and normal human brain data acquired at 3T. The N-acetyl aspartate (NAA)/creatine (Cr), choline (Cho)/Cr, glutamate + glutamine (Glx)/Cr, and myo-inositol (mI)/Cr ratios in normal subjects were compared with the line fitting (LF) technique and jMRUI-AMARES analysis, and published values. The average NAA/Cr, Cho/Cr, Glx/Cr and mI/Cr ratios in normal controls were found to be 1.58 ± 0.13, 0.9 ± 0.08, 0.7 ± 0.17 and 0.42 ± 0.07, respectively. The corresponding ratios using the LF and jMRUI-AMARES methods were 1.6 ± 0.11, 0.95 ± 0.08, 0.78 ± 0.18, 0.49 ± 0.1 and 1.61 ± 0.15, 0.78 ± 0.07, 0.61 ± 0.18, 0.42 ± 0.13, respectively. These results agree with those published in literature. Bland-Altman analysis indicated an excellent agreement and minimal bias between the results obtained with RBFNN and other methods. The computational time for the current method was 15 s compared to approximately 10 min for the LF-based analysis.

Original languageEnglish (US)
Pages (from-to)110-122
Number of pages13
JournalJournal of Magnetic Resonance
Volume183
Issue number1
DOIs
StatePublished - Nov 2006

Keywords

  • Artificial neural networks
  • Magnetic resonance spectroscopic imaging
  • Parametric spectral analysis
  • Radial basis function neural network

ASJC Scopus subject areas

  • Biophysics
  • Biochemistry
  • Nuclear and High Energy Physics
  • Condensed Matter Physics

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