Forecasting the quality of water-suppressed (1) H MR spectra based on a single-shot water scan.

PURPOSE To investigate whether an initial non-water-suppressed acquisition that provides information about the signal-to-noise ratio (SNR) and linewidth is enough to forecast the maximally achievable final spectral quality and thus inform the operator whether the foreseen number of averages and achieved field homogeneity is adequate. METHODS A large range of spectra with varying SNR and linewidth was simulated and fitted with popular fitting programs to determine the dependence of fitting errors on linewidth and SNR. A tool to forecast variance based on a single acquisition was developed and its performance evaluated on simulated and in vivo data obtained at 3 Tesla from various brain regions and acquisition settings. RESULTS A strong correlation to real uncertainties in estimated metabolite contents was found for the forecast values and the Cramer-Rao lower bounds obtained from the water-suppressed spectra. CONCLUSION It appears to be possible to forecast the best-case errors associated with specific metabolites to be found in model fits of water-suppressed spectra based on a single water scan. Thus, nonspecialist operators will be able to judge ahead of time whether the planned acquisition can possibly be of sufficient quality to answer the targeted clinical question or whether it needs more averages or improved shimming. Magn Reson Med, 2016. © 2016 International Society for Magnetic Resonance in Medicine

Deep learning approaches for detection and removal of ghosting artifacts in MR spectroscopy.

PURPOSE To make use of deep learning (DL) methods to detect and remove ghosting artifacts in clinical magnetic resonance spectra of human brain. METHODS Deep learning algorithms, including fully connected neural networks, deep-convolutional neural networks, and stacked what-where auto encoders, were implemented to detect and correct MR spectra containing spurious echo ghost signals. The DL methods were trained on a huge database of simulated spectra with and without ghosting artifacts that represent complex variations of ghost-ridden spectra, transformed to time-frequency spectrograms. The trained model was tested on simulated and in vivo spectra. RESULTS The preliminary results for ghost detection are very promising, reaching almost 100% accuracy, and the DL ghost removal methods show potential in simulated and in vivo spectra, but need further refinement and quantitative testing. CONCLUSIONS Ghosting artifacts in spectroscopy are problematic, as they superimpose with metabolites and lead to inaccurate quantification. Detection and removal of ghosting artifacts using traditional machine learning approaches with feature extraction/selection is difficult, as ghosts appear at different frequencies. Here, we show that DL methods perform extremely well for ghost detection if the spectra are treated as images in the form of time-frequency representations. Further optimization for in vivo spectra will hopefully confirm their "ghostbusting" capacity. Magn Reson Med, 2018. © 2018 International Society for Magnetic Resonance in Medicine

A realistic framework for investigating decision-making in the brain with high spatio-temporal resolution using simultaneous EEG/fMRI and joint ICA.

Human decision-making is a multidimensional construct, driven by a complex interplay between external factors, internal biases and computational capacity constraints. Here we propose a layered approach to experimental design in which multiple tasks - from simple to complex - with additional layers of complexity introduced at each stage, are incorporated for investigating decision-making. This is demonstrated using tasks involving intertemporal choice between immediate and future prospects. Previous functional magnetic resonance imaging (fMRI) and electroencephalographic (EEG) studies have separately investigated the spatial and temporal neural substrates, respectively, of specific factors underlying decision making. In contrast, we performed simultaneous acquisition of EEG/fMRI data and fusion of both modalities using joint independent component analysis (jICA) such that: (i) the native temporal/spatial resolutions of either modality is not compromised and (ii) fast temporal dynamics of decision-making as well as involved deeper striatal structures can be characterized. We show that spatio-temporal neural substrates underlying our proposed complex intertemporal task simultaneously incorporating rewards, costs and uncertainty of future outcomes can be predicted (using a linear model) from neural substrates of each of these factors, which were separately obtained by simpler tasks. This was not the case for spatial and temporal features obtained separately from fMRI and EEG respectively. However, certain prefrontal activations in the complex task could not be predicted from activations in simpler tasks, indicating that the assumption of pure insertion has limited validity. Overall, our approach provides a realistic and novel framework for investigating the neural substrates of decision making with high spatio-temporal resolution

Fitting interrelated datasets: metabolite diffusion and general lineshapes.

OBJECTIVE Simultaneous modeling of true 2-D spectroscopy data, or more generally, interrelated spectral datasets has been described previously and is useful for quantitative magnetic resonance spectroscopy applications. In this study, a combined method of reference-lineshape enhanced model fitting and two-dimensional prior-knowledge fitting for the case of diffusion weighted MR spectroscopy is presented. MATERIALS AND METHODS Time-dependent field distortions determined from a water reference are applied to the spectral bases used in linear-combination modeling of interrelated spectra. This was implemented together with a simultaneous spectral and diffusion model fitting in the previously described Fitting Tool for Arrays of Interrelated Datasets (FiTAID), where prior knowledge conditions and restraints can be enforced in two dimensions. RESULTS The benefit in terms of increased accuracy and precision of parameters is illustrated with examples from Monte Carlo simulations, in vitro and in vivo human brain scans for one- and two-dimensional datasets from 2-D separation, inversion recovery and diffusion-weighted spectroscopy (DWS). For DWS, it was found that acquisitions could be substantially shortened. CONCLUSION It is shown that inclusion of a measured lineshape into modeling of interrelated MR spectra is beneficial and can be combined also with simultaneous spectral and diffusion modeling

Quality of clinical brain tumor MR spectra judged by humans and machine learning tools.

PURPOSE To investigate and compare human judgment and machine learning tools for quality assessment of clinical MR spectra of brain tumors. METHODS A very large set of 2574 single voxel spectra with short and long echo time from the eTUMOUR and INTERPRET databases were used for this analysis. Original human quality ratings from these studies as well as new human guidelines were used to train different machine learning algorithms for automatic quality control (AQC) based on various feature extraction methods and classification tools. The performance was compared with variance in human judgment. RESULTS AQC built using the RUSBoost classifier that combats imbalanced training data performed best. When furnished with a large range of spectral and derived features where the most crucial ones had been selected by the TreeBagger algorithm it showed better specificity (98%) in judging spectra from an independent test-set than previously published methods. Optimal performance was reached with a virtual three-class ranking system. CONCLUSION Our results suggest that feature space should be relatively large for the case of MR tumor spectra and that three-class labels may be beneficial for AQC. The best AQC algorithm showed a performance in rejecting spectra that was comparable to that of a panel of human expert spectroscopists. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine