Magnetic Resonance Spectroscopy (MRS) is a specialized non-invasive technique associated with magnetic resonance imaging (MRI) that quantifies the metabolic activity and biochemical composition of cellular metabolism in real-time. In the last few years, research has shown that many of these metabolites can be used as indicators of disease risk and can be used as biochemical markers for the prognosis of various diseases. Furthermore, as our understanding of the biochemical pathways that generate these compounds grows, it is likely that they will be incorporated into new diagnostic and therapeutic protocols in the future.
MRS is a promising tool for studying neurological disorders, as it can provide valuable insights into the brain's metabolic activity. However, there are some limitations that need to be considered, such as poor spectral resolution, residual water resonance, and inter-scanner variability. To address these limitations, we explore machine learning methods to improve the spectral quality of MRS data and propose an interpretable model to identify metabolite spectral patterns.
We begin with single-voxel non-water suppressed MRS data as it has the potential to provide an internal reference for inter-and intra-subject comparisons. We develop an autoencoder model to reconstruct metabolite spectra and learn latent vector representation of non-water suppressed MRS data. The reconstructed metabolite spectra can be quantified using standard software. We extend this approach to support data from multiple echo times and multiple voxels while preserving the diagnostic value of MRS.
We evaluate the data representation of the autoencoder model using two case studies. The first case study is the diagnosis of low-grade gliomas by detecting 2-hydroxyglutarate (2HG), a biomarker for isocitrate dehydrogenase mutations. We quantitatively compare the autoencoder reconstructed metabolite spectra with those acquired with water suppression.
The Pearson correlation R2 value is 0.40 - 0.91 between the metabolites from the two approaches.
These results suggest that our autoencoder-based metabolite spectrum reconstruction approach provides a good representation of metabolite spectra from non-water suppressed MRS data and can be used for diagnostic purposes.
In the second case study, we use the generated latent vector representation of the autoencoder model to understand long-term neurological difficulties after repetitive brain trauma experienced by individuals in contact sports. Athletes with multiple concussions have the potential to develop Chronic Traumatic Encephalopathy (CTE), a neurodegenerative disease that is currently diagnosed only postmortem by tau protein deposition in the brain. We map the latent vector representation of MRS data to neuropsychological evaluation using a support vector machine model. The support vector machine model has a cross-validated score of 0.72 (0.052), which is higher than the previous prediction model's cross-validated score of 0.65 (0.026) for CTE diagnosis. The results suggest that the latent vector representation of MRS data can be used to identify individuals at risk for developing CTE after repetitive brain trauma.
To promote more clinical usage, we propose an interpretable machine learning pipeline to identify the metabolic spectral pattern to predict outcomes after cardiac arrest. Targeted Temperature Management (TTM) has improved the outcome in patients resuscitated after cardiac arrest, but 45-70% of these patients still die or have a poor neurological outcome at hospital discharge, and 50% of survivors have long-term neurocognitive deficits. MRS has been to be highly sensitive to changes in the brain after TTM after cardiac arrest namely showing significant reductions in N-acetylaspartate (NAA), a neuronal marker, and lactate (Lac), a marker of hypoxia. Initial findings show that a lactate/creatine ratio above 0.23 would prognose poor outcome with good sensitivity and specificity, however, if all metabolites could be utilized, a much greater accuracy could be achieved. The proposed pipeline utilizes a machine-learning algorithm to predict the outcome for these individuals, based on their metabolic patterns with 80% accuracy. This would allow for better TTM interventions for these individuals and could improve their long-term neurological outcomes.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/45072 |
| Date | 30 August 2022 |
| Creators | Sahaya Louis, Marcia |
| Contributors | Joshi, Ajay J., Lin, Alexander P. |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution-NonCommercial-ShareAlike 4.0 International, http://creativecommons.org/licenses/by-nc-sa/4.0/ |
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