Quantification of Metabolites in Magnetic Resonance Spectroscopic Imaging Using Machine Learning

Das, Dhritiman; Coello, Eduardo; Schulte, Rolf F.; Menze, Bjoern

doi:10.1007/978-3-319-66179-7_53

Cited by 20 publications

(32 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The potential applications of machine learning for metabolite quantification have also been explored by a few research groups. Using a random forest regression Das et al reported the utility of machine learning for the quantification of major metabolite peaks such as Cho, Cr, Glx, mI, and NAA in the simulated and in vivo 1 H‐MRS spectra. Using deep learning Hatami et al reported a CNN that was trained on the time‐domain dataset and capable of quantifying individual metabolites in simulated spectra.…”

Section: Discussionmentioning

confidence: 99%

“…Given the recent accomplishment of deep learning in a variety of different tasks and its potential application in 1 H‐MRS, we developed a CNN that maps degraded generic in vivo brain spectra at short TE into noise‐free, line‐narrowed, baseline‐removed, metabolite‐only spectra (intact metabolite spectra). The subsequent metabolite quantification from such intact metabolite spectra is achieved by solving a simple inverse problem using a metabolite basis set.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intact metabolite spectrum mining by deep learning in proton magnetic resonance spectroscopy of the brain

Lee

Kim

2019

Magnetic Resonance in Med

109

View full text Add to dashboard Cite

Purpose: To develop a robust method for brain metabolite quantification in proton magnetic resonance spectroscopy ( 1 H-MRS) using a convolutional neural network (CNN) that maps in vivo brain spectra that are typically degraded by low SNR, line broadening, and spectral baseline into noise-free, line-narrowed, baseline-removed intact metabolite spectra. Methods: A CNN was trained (n = 40 000) and tested (n = 5000) on simulated brain spectra with wide ranges of SNR (6.90-20.74) and linewidth (10-20 Hz). The CNN was further tested on in vivo spectra (n = 40) from five healthy volunteers with substantially different SNR, and the results were compared with those from the LCModel analysis. A Student t test was performed for the comparison. Results: Using the proposed method the mean-absolute-percent-errors (MAPEs) in the estimated metabolite concentrations were 12.49% ± 4.35% for aspartate, creatine (Cr), γ-aminobutyric acid (GABA), glucose, glutamine, glutamate, glutathione (GSH), myo-Inositol (mI), N-acetylaspartate, phosphocreatine (PCr), phosphorylethanolamine, and taurine over the whole simulated spectra in the test set. The metabolite concentrations estimated from in vivo spectra were close to the reported ranges for the proposed method and the LCModel analysis except mI, GSH, and especially Cr/PCr for the LCModel analysis, and phosphorylcholine to glycerophosphorylcholine ratio (PC/GPC) for both methods. The metabolite concentrations estimated across the in vivo spectra with different SNR were less variable with the proposed method (~10% or less) than with the LCModel analysis. Conclusion: The robust performance of the proposed method against low SNR may allow a subminute 1 H-MRS of human brain, which is an important technical development for clinical studies. K E Y W O R D S brain, convolutional neural network, deep learning, metabolite quantification, proton magnetic resonance spectroscopy

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intact metabolite spectrum mining by deep learning in proton magnetic resonance spectroscopy of the brain

Lee

Kim

2019

Magnetic Resonance in Med

109

View full text Add to dashboard Cite

show abstract

“…We also compare our results with the only machine learning approach applied on MRS quantification i.e. random forest regression algorithm [5]. However, since the full details on the features used for the random forest is not given, we applied it on the raw data (no traditional hand-crafted feature extraction used).…”

Section: Experiments and Resultsmentioning

confidence: 99%

Magnetic Resonance Spectroscopy Quantification Using Deep Learning

Hatami

Sdika

Ratiney

2018

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

View full text Add to dashboard Cite

Magnetic resonance spectroscopy (MRS) is an important technique in biomedical research and it has the unique capability to give a non-invasive access to the biochemical content (metabolites) of scanned organs. In the literature, the quantification (the extraction of the potential biomarkers from the MRS signals) involves the resolution of an inverse problem based on a parametric model of the metabolite signal. However, poor signal-to-noise ratio (SNR), presence of the macromolecule signal or high correlation between metabolite spectral patterns can cause high uncertainties for most of the metabolites, which is one of the main reasons that prevents use of MRS in clinical routine. In this paper, quantification of metabolites in MR Spectroscopic imaging using deep learning is proposed. A regression framework based on the Convolutional Neural Networks (CNN) is introduced for an accurate estimation of spectral parameters. The proposed model learns the spectral features from a large-scale simulated data set with different variations of human brain spectra and SNRs. Experimental results demonstrate the accuracy of the proposed method, compared to state of the art standard quantification method (QUEST), on concentration of 20 metabolites and the macromolecule.

show abstract

“…For tFID 1024 (null truncation), the CNN returns the input spectrum as it is. Therefore, our CNN may be combined with another CNN unit that takes part in metabolite quantification, such that those spectra from truncated FIDs are recovered prior to entering the next CNN, whereas those fully acquired spectra simply pass through spec CNN spec . As previously discussed, screening of the quality of the input data is important, such as for minimizing the mentioned possibility of processing the data contaminated by unwanted signal.…”

Section: Discussionmentioning

confidence: 99%

Reconstruction of spectra from truncated free induction decays by deep learning in proton magnetic resonance spectroscopy

Lee

Kim

2020

Magnetic Resonance in Med

View full text Add to dashboard Cite

Purpose:To explore the applicability of convolutional neural networks (CNNs) in the reconstruction of spectra from truncated FIDs (tFIDs) in 1 H-MRS, which can be valuable in situations in which data sampling is highly limited, such as spectroscopic magnetic resonance fingerprinting. Methods: Rat brain FIDs were simulated at 9.4 T based on in vivo data (N = 11) and randomly truncated by retaining 8,16,32, 64, 128, 256, 512, and 1024 (null truncation) points (denoted as tFID 8 , tFID 16 , … tFID 1024 ). Using a U-net, 3 CNNs were individually trained (N = 40 000) in time domain only (FID to FID [ FID CNN FID ]), in frequency domain only (spectrum to spectrum [ spec CNN spec ]), and across the domains (FID to spectrum [ FID CNN spec ]) to map the truncated data to their fully sampled versions. The CNNs were tested on the simulated data (N = 5000), and the CNN with the best performance was further tested on the in vivo data, for which the CNNpredicted fully sampled data were analyzed using the LCModel and the results were compared with those from the original, fully sampled data. Results: The best result on the simulated data was obtained with spec CNN spec , which effectively recovered the spectral details even for those input spectra that appear as a hump due to substantial FID truncation (spectra from tFID 16 and tFID 32 ). Overall, its performance was significantly degraded on the in vivo data. Nonetheless, using spec CNN spec , several coupled spins in addition to the major singlets can be quantified from tFID 128 with the error no larger than 10%. Conclusion: Upon the availability of more realistically simulated training data, CNNs can also be used in the reconstruction of spectra from truncated FIDs. K E Y W O R D Sconvolutional neural network, deep learning, free induction decay, proton magnetic resonance spectroscopy, truncation

show abstract

Quantification of Metabolites in Magnetic Resonance Spectroscopic Imaging Using Machine Learning

Cited by 20 publications

References 8 publications

Intact metabolite spectrum mining by deep learning in proton magnetic resonance spectroscopy of the brain

Intact metabolite spectrum mining by deep learning in proton magnetic resonance spectroscopy of the brain

Magnetic Resonance Spectroscopy Quantification Using Deep Learning

Reconstruction of spectra from truncated free induction decays by deep learning in proton magnetic resonance spectroscopy

Contact Info

Product

Resources

About