MRI Cell Quantification via Machine Learning Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current MRI systems lack the ability to accurately and non-invasively track and quantify the number and location of transplanted cells, such as stem cells, after injection, which hinders the understanding of their behavior and effectiveness in cell-based therapies.
Innovation Solution
A system and method utilizing an MRI system with a machine learning module to process imaging data, segmenting it into superpixels, identifying candidate patches, and training the module to detect labeled substances, allowing for the accurate detection and quantification of labeled cells within the imaging data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional MRI systems are used to image labeled cells, then imaging capability is maintained, but the ability to accurately track and quantify cell numbers and locations is insufficient
Solution Approach 1:
The patent introduces a machine learning module as an intermediary between the traditional MRI system and the cell quantification task. This module processes MRI images to automatically detect, segment, and count labeled cells, transforming the raw imaging data into quantitative cell information without requiring fundamental changes to the MRI hardware itself.
Solution Approach 2:
The patent applies image segmentation techniques to divide the MRI image into distinct regions, separating labeled cells from background tissue. By segmenting the image data and analyzing intensity profiles of different regions, the system can identify and quantify individual cells based on their magnetic labeling characteristics.
2Measurement precision
If machine learning modules are added to improve cell detection accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent performs preliminary processing of MRI images by dividing them into multiple regions or patches before applying machine learning algorithms. This preprocessing step reduces the computational complexity of the main detection task by breaking down large images into manageable segments that can be processed independently and efficiently.
3Measurement precision
If detailed image analysis is performed to locate individual cells, then measurement precision improves, but processing time increases
Solution Approach 1:
The patent segments the MRI image into multiple regions and processes each region independently to identify cell locations. This segmentation approach allows parallel processing of different image portions, reducing the overall processing time while maintaining the ability to precisely locate individual cells within each segment.
Solution Approach 2:
The patent applies machine learning algorithms selectively to specific regions of the image where labeled cells are likely to be present, rather than analyzing every pixel uniformly. This partial action approach focuses computational resources on relevant areas, improving processing efficiency while maintaining detection accuracy.
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
Enables precise tracking and quantification of labeled cells, improving the understanding of cell behavior and therapy effectiveness by providing a visual representation of cell presence and location within the subject.
Implementation Method 1
MRI is based on the principle that nuclei possess a magnetic moment that attempts to align itself with the direction of the magnetic field in which it is located. In doing so, however, the nuclei precess around this direction at a characteristic angular frequency (Larmor frequency)
Implementation Method 2
This is accomplished by employing magnetic fields (Gx, Gy, and Gz) which have the same direction as the polarizing field B0, but which have a gradient along the respective x, y and z axes. By controlling the strength of these gradients during each NMR cycle, the spatial distribution of spin excitation can be controlled and the location of the resulting NMR signals can be identified.
Data Source
AI summary
A system and method are provided for tracking magnetically-labeled substances, such as transplanted cells, in subjects using magnetic resonance imaging (MRI). The method includes obtaining a quantity of a substance that comprises an MRI contrast compound or is otherwise magnetically-labeled for purposes of an MRI scan, administering the substance into a region of interest of a subject, performing an imaging scan of a portion of the subject comprising the region of interest, obtaining an imaging data set from the scan, reducing the dataset into pixel groupings based on intensity profiles, where the pixel groupings have a pixel size larger than the expected pixel size of a unit of the MRI contrast compound or magnetically-labeled substance, extracting candidate pixel matrices from the imaging data, training a machine learning (ML) module by using the candidate pixel matrices, quantifying the presence, number and/or location of units of the substance within the subject by using the ML module, and displaying a visual representation of an identification of the substances within the subject as a result of using the ML module.