MR Selective Sampling for Tissue Texture Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current diagnostic techniques face challenges in accurately assessing fine tissue textures in vivo due to motion-induced blurring, particularly in bone, fibrotic, and neurologic diseases, limiting early detection and monitoring of disease progression.
Innovation Solution
A magnetic resonance (MR)-based method that involves selective sampling by exciting a volume of interest with all gradients turned off, allowing multiple samples of an RF signal encoded at specific k-values to be recorded, and using hybrid addition of measurements at low and high signal-to-noise ratio regions to enhance data, thereby reducing the impact of patient motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MRI imaging is used to assess tissue texture, then diagnostic information can be obtained, but motion-induced blurring degrades measurement precision
Solution Approach 1:
The patent extracts only the essential k-values needed for texture assessment from the full k-space, rather than acquiring complete images. By selectively sampling specific k-values and combining them through hybrid addition, the method obtains texture information without requiring full image acquisition, thereby reducing motion-induced blurring while maintaining diagnostic precision.
Solution Approach 2:
The patent applies preliminary gradient encoding to select specific k-values before signal acquisition. By pre-positioning the gradient fields to encode only the necessary spatial frequency information, the method prepares the measurement setup in advance to capture texture-relevant data while minimizing acquisition time and motion impact.
2Measurement precision
If gradient encoding is used to improve spatial resolution, then measurement precision increases, but signal-to-noise ratio decreases in certain regions
Solution Approach 1:
The patent applies different gradient strengths locally across different k-value regions. Low gradients are used for very low SNR regions to maximize signal detection, while higher gradients are applied to high SNR regions to enhance spatial frequency resolution. This localized gradient strategy optimizes both SNR and measurement precision in different parts of k-space.
Solution Approach 2:
The patent combines measurements from multiple gradient conditions through hybrid addition. By merging data acquired with different gradient strengths and encoding conditions, the method reconstructs texture information with improved SNR while preserving the high-resolution spatial frequency data obtained from regions with higher gradient encoding.
3Measurement precision
If multiple k-values are sampled to improve texture characterization, then measurement precision increases, but acquisition time increases leading to more patient motion
Solution Approach 1:
The patent extracts only the specific k-values that contribute most to texture characterization, rather than sampling the entire k-space uniformly. By identifying and sampling only the critical spatial frequency components needed for texture assessment, the method achieves accurate texture characterization with significantly reduced acquisition time, minimizing patient motion effects.
Solution Approach 2:
The patent samples more k-values than the minimum required for basic imaging, but focuses specifically on the subset most relevant to texture analysis. This partial over-sampling of critical regions provides robust texture characterization while keeping total acquisition time limited, balancing measurement precision with motion mitigation.
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 high-resolution, non-invasive assessment of fine biologic textures, allowing for early detection and monitoring of disease progression without radiation, improving diagnostic accuracy and reducing motion-induced noise.
Implementation Method 1
A method for selective sampling to assess texture using magnetic resonance (MR) is accomplished by exciting a volume of interest
Implementation Method 2
multiple samples of an RF signal encoded at a specific k-value are recorded
Data Source
AI summary
The disclosed embodiments provide a method for acquiring MR data at resolutions down to tens of microns for application in in-vivo diagnosis and monitoring of pathology for which changes in fine tissue textures can be used as markers of disease onset and progression. Bone diseases, tumors, neurologic diseases, and diseases involving fibrotic growth and/or destruction are all target pathologies. Further the technique can be used in any biologic or physical system for which very high-resolution characterization of fine scale morphology is needed. The method provides rapid acquisition of selected values in k-space, with multiple successive acquisitions of individual k-values taken on a time scale on the order of microseconds, within a defined tissue volume, and subsequent combination of the multiple measurements in such a way as to maximize SNR. The reduced acquisition volume, and acquisition of only select values in k-space along selected directions, enables much higher in-vivo resolution than is obtainable with current MRI techniques.