Magnetic resonance spectroscopy (MRS) is a non-invasive imaging tool for detecting and quantifying metabolites in vivo. However, the spectral data often suffer from poor signal-to-noise ratio (SNR) due to low concentrations of metabolites and limited acquisition time. In this work, we introduce a deep learning-based method for denoising MRS spectra. By identifying and characterizing the sparse representations in the feature vector domain of the noise in MRS data using the deep learning network, the non-signal components can be removed to improve the SNR of spectral data. We used a stack auto-encoder (SAE) network to train the deep learning-based model based on high SNR data collected from a brain phantom using a high number of signal average (NSA=192). To overcome overfitting, the SAE network is trained in a patch-based manner. The network and denoising method were then tested using the noisy data collected in the same sample volume but using low NSA (NSA=8) with much shorter acquisition time. The denoising performance of the reported method was then evaluated using statistical comparison analysis of spectra before and after being denoised and the paired “ground truth” high NSA spectra based on the measurements of SNR, and mean squared error (MSE) and known metabolite levels. for the results of testing and evaluating the reported method showed that the SNR improved by 40% and the MSE reduced by 72% in the data collected from the brain phantom, while, the SNR improved by 47% and the MSE reduced by 27% in the data collected from the human subjects. In summary, the reported deep-learning method demonstrated a model-free approach to enhance SNR of noisy MRS data that otherwise cannot be used for quantitative analysis. The potential applications of include using low NSA or short data collection time to accelerate MRS exams while maintaining adequate spectroscopic information for detection and quantification of the metabolites of interest when a little time is available for an MRS exam in the clinical setting.
A classification method that integrates delta-radiomic features, DSC MRI, and random forest approach on the glioblastoma classification task is proposed. 25 patients, 13 high and 12 low-grade gliomas, who underwent the standard brain tumor MRI protocol, including DSC MRI, were included. Tumor regions on all DSC MRI images were registered to and contoured in T2-weighted fluid-attenuated-inversion-recovery (FLAIR) images. These contours and its contralateral regions of the normal tissue were used to extract delta-radiomic features before applying feature selection. The most informative and non-redundant features were selected to train a random forest to differentiate high-grade (HG) and low-grade (LG) gliomas. These were then fed into a leaveone- out cross-validation random forest to classify these tumors for grading. Finally, a majority-voting method was applied to reduce binarization bias and to combine the results of various feature lists. Analysis of the predictions showed that the reported method consistently predicted the tumor grade of 24 out of 25 patients correctly (0.96). Finally, the mean prediction accuracy was 0.95±0.10 for HG and 0.85±0.25 for LG. The area under the receiver operating characteristic curve (AUC) was 0.94. This study shows that delta-radiomic features derived from DSC MRI data can be used to characterize and determine the tumor grades. The radiomic features from DSC MRI may be used to elucidate the underlying tumor biology and response to therapy.