Automatic Multi-Voxel Brain Spectroscopy Volume Selection With Lesion Masks
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Solution Overview
Problem
The challenge in multi-voxel brain proton spectroscopy is planning the volume of interest to include both the lesion and adjacent normal brain tissues while avoiding bone and air-tissue interfaces, requiring high expertise to prevent magnetic field inhomogeneity and data degradation.
Innovation Solution
A computer-implemented method using deep learning-based segmentation models to automatically select a volume of interest in brain MRI data, excluding bone and air-tissue interfaces, by generating lesion and skull-stripped masks, and calculating voxel volumes to avoid aliasing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multi-voxel spectroscopy is performed to acquire large volume of interest, then metabolic information from normal brain area can be obtained, but additional post processing is required and expertise is needed to avoid bone and air-tissue interfaces
Solution Approach 1:
The system automatically performs skull stripping, lesion segmentation, and volume of interest selection without requiring manual intervention. The deep learning model processes structural MRI data autonomously to generate masks and identify appropriate voxels for spectroscopy, eliminating the need for expert operator involvement in these tasks
Solution Approach 2:
The system performs preliminary processing steps including skull stripping, lesion segmentation, and determination of optimal slice location before the actual spectroscopy acquisition. This pre-processing automation ensures that all necessary preparations are completed automatically, reducing the complexity during the main acquisition process
2Measurement precision
If manual planning of volume of interest is performed by operator, then expertise can be applied to select appropriate slices, but high level of expertise is required and planning time is consumed
Solution Approach 1:
The patent replaces the manual mechanical process of operator-based planning with an automated computational system. Deep learning models process structural MRI data to automatically identify lesions, generate segmentation masks, and select optimal voxels, substituting human expertise with algorithmic processing that achieves comparable or superior precision without time loss
Solution Approach 2:
The system creates digital copies of structural MRI data through automated processing to generate virtual masks and segmented representations of brain anatomy. These computational models serve as substitutes for manual interpretation, allowing the system to 'see' and select appropriate voxels just as an expert operator would, but instantaneously
3Area of stationary object
If volume of interest includes bone or air-tissue interface, then larger coverage is achieved, but magnetic field inhomogeneity occurs and spectroscopy data degradation results
Solution Approach 1:
The system extracts and removes bone and air-tissue interface regions from the volume of interest through automated skull stripping and segmentation. By identifying and excluding these problematic regions, the system maintains data quality while still achieving comprehensive coverage of the brain parenchyma through intelligent voxel selection
Solution Approach 2:
The system applies different processing characteristics to different regions of the brain. Through localized segmentation and quality assessment, it identifies regions suitable for spectroscopy versus regions that should be excluded, allowing optimal coverage of quality-appropriate areas while maintaining overall data integrity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Reduces the need for expert planning, minimizes data degradation, and ensures accurate metabolite analysis by automatically selecting appropriate voxels for multi-voxel spectroscopy.
Implementation Method 1
During magnetic resonance imaging (MRI), when a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the spins in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency.
Implementation Method 2
performing skull stripping on the structural magnetic resonance imaging data to generate a skull stripped brain image. utilizing a trained deep learning-based segmentation model to generate a lesion core mask from the skull stripped brain image
Data Source
AI summary
A method for performing multi-voxel spectroscopy includes obtaining structural magnetic resonance imaging data of a brain of a subject acquired with a magnetic resonance imaging scanner. The method also includes performing skull stripping on the structural magnetic resonance imaging data to generate a skull stripped brain image. The method further includes utilizing a trained deep learning-based segmentation model to generate a lesion core mask from the brain image. The method also includes locating a slice with largest volume of lesion present in the lesion core mask. The method includes calculating a voxel volume that avoids aliasing from the slice based on a field of view. The method includes automatically selecting a volume of interest in the brain having both the lesion and normal brain tissue for a multi-voxel spectroscopy scan by the magnetic resonance imaging scanner based on the brain image, the lesion core mask, and the voxel volume.