MRμT Micro-Texture Analysis for Sub-100 μm Tissue Resolution
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Solution Overview
Problem
Current diagnostic methods, such as MRI and biopsy, face limitations in accurately measuring microscopic tissue textures due to motion artifacts and inability to resolve textures finer than 100 μm, which hampers early disease detection and therapy development.
Innovation Solution
The Magnetic Resonance Micro-Texture (MRμT) method uses a selective internal excitation and phase encoding to acquire high-resolution, motion-immune tissue texture data by focusing on specific k-values, enabling non-invasive histology with sub-100 μm resolution and repeated measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MRI is used to image tissue, then spatial coverage is achieved, but resolution is limited to above 100 μm due to motion artifacts and signal-to-noise constraints
Solution Approach 1:
The patent extracts only the essential k-space data required for texture analysis rather than acquiring complete image data. By selectively sampling specific k-values and using compressed sensing reconstruction, the method obtains high-resolution texture information (sub-100 μm) without the full data acquisition burden that limits conventional MRI resolution and introduces motion artifacts.
Solution Approach 2:
The method employs periodic sampling of k-space at strategically selected intervals rather than continuous sampling. This periodic acquisition approach, combined with compressed sensing algorithms, enables high-resolution texture measurement while reducing scan time and minimizing motion artifact impact, thereby improving both resolution and measurement reliability.
2Measurement precision
If biopsy is performed to obtain histology, then direct tissue examination is achieved, but the procedure is invasive and cannot be repeated for monitoring
Solution Approach 1:
The patent creates a non-invasive copy of histologic texture information using MRμT imaging. By measuring microscopic texture parameters through magnetic resonance at sub-100 μm resolution, the method reproduces the diagnostic value of biopsy without requiring tissue removal, eliminating invasiveness while maintaining measurement precision for disease detection and monitoring.
3Measurement precision
If high-resolution texture data is acquired, then disease detection sensitivity is improved, but scan time increases
Solution Approach 1:
The method extracts only the critical k-space information needed for texture analysis rather than acquiring complete high-resolution image data. This selective data extraction, combined with compressed sensing reconstruction algorithms, achieves high measurement precision for disease detection while significantly reducing the acquisition time compared to conventional high-resolution imaging.
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
MRμT provides sensitive and accurate differentiation of normal and diseased tissues, potentially reducing the need for biopsy and improving disease staging and monitoring by offering high-resolution, non-invasive tissue texture analysis across organs.
Implementation Method 1
Magnetic-resonance-based method for measuring microscopic histologic soft tissue textures
Implementation Method 2
each TR generating a single spin echo with a single k-encode
Implementation Method 3
phase encoding to acquire high-resolution, motion-immune tissue texture data by focusing on specific k-values
Data Source
AI summary
A method for measuring soft tissue texture to identify diseased as opposed to normal tissue by identifying textural markers that distinguish diseased tissue from normal tissue and selecting a MRμT excitation sequence and associated parameters to reveal those markers. Data is then acquired in an MR scanner responsive to the selected MRμT excitation sequence to establish a multipoint time series data set. The acquired data is then analyzed for presence of the markers.