Selective MRI Sampling for High-Resolution Tissue Texture Analysis
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Solution Overview
Problem
Current diagnostic techniques struggle to accurately and non-invasively assess fine tissue textures in biological systems due to motion-induced blurring, limiting the resolution and accuracy of disease diagnosis and monitoring, particularly in bone, fibrotic, and neurologic diseases.
Innovation Solution
A method for selective sampling using magnetic resonance imaging (MRI) that applies a contrast mechanism to enhance tissue contrast, allowing for rapid acquisition of specific k-values to reduce motion effects, enabling high-resolution measurement of tissue textures without the need for image formation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MRI sequences sample k-space uniformly, then comprehensive structural information is captured, but acquisition time and data complexity increase significantly
Solution Approach 1:
The patent segments k-space into different spatial frequency regions (central low-frequency region and peripheral high-frequency region) and applies different sampling strategies to each segment. Central k-space lines are sampled densely to capture essential contrast information, while peripheral k-space lines are sampled sparsely or skipped to reduce acquisition time, resolving the contradiction between comprehensive structural information and acquisition time.
Solution Approach 2:
The patent applies partial sampling by acquiring only a subset of k-space lines rather than the full k-space matrix. By selectively sampling only the most informative central lines and using interpolation or compression techniques for the remaining peripheral lines, the method achieves acceptable image quality with reduced acquisition time.
2Reliability
If all k-space lines are acquired to maintain image quality, then diagnostic accuracy is preserved, but data processing complexity and storage requirements increase
Solution Approach 1:
The patent extracts and prioritizes the most diagnostically relevant information by selectively acquiring only the central k-space lines that contain essential contrast and structural information. This extraction approach maintains diagnostic accuracy for key features while reducing overall data volume and processing complexity.
Solution Approach 2:
The patent changes the sampling parameter distribution across k-space, transitioning from uniform sampling to non-uniform sampling where the sampling density varies by spatial frequency. This parameter change optimizes the balance between diagnostic accuracy and data complexity by allocating sampling resources to the most informative regions.
3Manufacturing precision
If high-resolution imaging is performed with full k-space sampling, then fine structural details are resolved, but scan time and patient comfort deteriorate due to longer acquisition
Solution Approach 1:
The patent segments the k-space acquisition into priority regions, allocating full sampling resources to the central region for contrast information and reduced sampling to peripheral regions for fine detail. This segmentation enables resolution of the contradiction by maintaining acceptable resolution through intelligent resource allocation rather than uniform high-cost sampling.
Solution Approach 2:
The patent employs periodic or interleaved sampling patterns where different k-space lines are acquired in alternating sequences or blocks. This periodic approach allows efficient utilization of acquisition time while maintaining the ability to reconstruct high-resolution images through appropriate k-space completion techniques.
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.