Deep Learning Spectral Fitting for MR Metabolite Quantification
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Solution Overview
Problem
Current methods for quantitating metabolites in proton spectroscopic magnetic resonance imaging (MRI) face a computational bottleneck, particularly in volumetric spectroscopic imaging, which hinders on-scanner processing and rapid turnaround needed for clinical applications like radiotherapy planning.
Innovation Solution
The implementation of a parallelized deep learning approach using a series of neural networks for spectral fitting, enabling rapid and accurate metabolite measurements by determining baseline and peak components, allowing for real-time processing of magnetic resonance spectroscopy data on conventional computers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative optimization procedures are used for spectral fitting, then measurement precision is improved, but productivity deteriorates due to significant processing time requirements
Solution Approach 1:
The patent applies preliminary action by performing spectral fitting processing before the scanner completes data acquisition. The system receives spectroscopic data during the scan and performs iterative optimization procedures in advance, so that when the scan completes, the metabolite measurements are already available or nearly available. This eliminates the need for time-consuming post-processing and enables real-time clinical decision-making.
2Measurement precision
If iterative optimization procedures are used for spectral fitting, then measurement precision is improved, but device complexity worsens due to requirements for high performance workstations
Solution Approach 1:
By performing the computationally intensive iterative optimization procedures in advance during the scan acquisition phase, the patent eliminates the need for complex high-performance workstations to be available at the point of care. The processing can be distributed across multiple cores or nodes during the scan, and the results are ready before clinical decisions are needed, simplifying the computational infrastructure requirements at the clinical site.
3Measurement precision
If conventional processing methods are used, then measurement precision is maintained, but loss of time increases due to computational bottleneck preventing on-scanner processing
Solution Approach 1:
The system performs metabolite quantification measurements in advance during the spectroscopic data acquisition phase. By receiving the spectroscopic data and performing the full iterative optimization spectral fitting process before the scan completes, the system ensures that accurate metabolite concentrations are available in real-time without post-processing delays, enabling immediate clinical applications such as radiotherapy planning.
Data Source
AI summary
Systems and methods provide a parallelized deep learning approach to spectral fitting for magnetic resonance spectroscopy data enabling accurate and rapid spectral fitting and determination of metabolite measurements using a conventional computer. The method may include processing multi-spectra magnetic resonance (MR) spectroscopy data of a region of interest through a series of neural networks. The method may include determining baseline components of each spectrum using a first neural network of the series, generating baseline-corrected components for each spectrum using the baseline components; and determining one or more peak components of each spectrum using a second neural network of the series and the baseline-corrected components. The method may further include determining one or more metabolite measurements of the one or more metabolites in the region of interest using the one or more peak components.