Recurrent neural network-aided processing of incomplete free induction decays in 1H-MRS of the brain

Abstract

In the case of limited sampling windows or truncation of free induction decays (FIDs) for artifact removal in proton magnetic resonance spectroscopy (¹H‐MRS) and spectroscopic imaging (¹H‐MRSI), metabolite quantification needs to be performed on incomplete FIDs. Given that FIDs are naturally time-domain sequential data, we investigated the potential of recurrent neural network (RNN)-types of neural networks (NNs) in the processing of incomplete human brain FIDs with or without FID restoration prior to quantitative analysis at 3.0T.

First, we employed an RNN encoder-decoder and developed it to restore incomplete FIDs (rRNN) with different amounts of sampled data. The quantification of metabolites from the rRNN-restored FIDs was achieved by using LCModel. Second, we modified the RNN encoder-decoder and developed it to convert incomplete brain FIDs into incomplete metabolite-only FIDs without restoration, followed by linear regression using a metabolite basis set for quantitative analysis (cRNN). In consideration of the practical benefit of the FID restoration with respect to pure zero-filling, development and analysis of the NNs were focused particularly on the incomplete FIDs with only the first 64 data points retained. All NNs were trained on simulated data and tested mainly on in vivo data acquired from healthy volunteers (n = 27).

Strong correlations were obtained between the NN-derived and ground truth metabolite content (LCModel-derived content on fully sampled FIDs) for myo‐inositol, total choline, and total creatine (normalized to total N-acetylaspartate) on the in vivo data using both rRNN (R = 0.83–0.94; p ≤ 0.05) and cRNN (R = 0.86–0.91; p ≤ 0.05).

RNN-types of NNs have potential in the quantification of the major brain metabolites from the FIDs with substantially reduced sampled data points. For the metabolites with low to medium SNR, the performance of the NNs needs to be further improved, for which development of more elaborate and advanced simulation techniques would be of help, but remains challenging.