NMR Tissue Texture Measurement via K-Space Segmentation
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Solution Overview
Problem
Current clinical imaging techniques, such as MRI, struggle to accurately assess fine tissue texture changes due to limited spatial resolution and motion-induced blurring, which hinders early disease diagnosis and monitoring, especially in conditions like cancer and fibrotic diseases.
Innovation Solution
A method using NMR spectroscopy to selectively excite a volume of interest and apply k-encode gradient pulses to measure tissue texture at specific k-values, allowing for high-resolution chemical and spatial characterization of tissue texture, tolerant to patient motion and enabling enhanced diagnostic sensitivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If MRI is used to provide tunable tissue contrast non-invasively, then diagnostic information is improved, but spatial resolution is limited by patient motion and acquisition time
Solution Approach 1:
The patent segments the continuous k-space acquisition into discrete, selectively sampled k-values. Instead of acquiring the entire image matrix, the method selectively samples specific k-values corresponding to desired spatial frequencies, thereby reducing acquisition time and motion sensitivity while maintaining diagnostic information.
Solution Approach 2:
The patent applies partial action by acquiring only the necessary portion of k-space data required for texture assessment rather than the full image matrix. This selective sampling approach reduces acquisition time and motion-induced blurring while providing sufficient diagnostic information for tissue characterization.
2Manufacturing precision
If spatial coherence is maintained throughout the entire image data acquisition, then image reconstruction is enabled, but acquisition time increases and motion blurring worsens
Solution Approach 1:
The patent segments the k-space acquisition into discrete, selectively sampled points rather than continuous acquisition. This allows the system to maintain spatial coherence only at the sampled k-values, reducing the total acquisition time while enabling sufficient image reconstruction for texture analysis.
Solution Approach 2:
The patent uses partial action by acquiring only the specific k-values needed for texture assessment rather than the complete image matrix. This reduces acquisition time and motion blurring while providing adequate data for reconstructing texture information.
3Measurement precision
If reregistration techniques are used to correct for motion, then image quality is improved, but the technique is not possible at high k-values due to low signal
Solution Approach 1:
The patent applies preliminary action by pre-selecting the specific k-values to be sampled based on the desired spatial frequencies for texture assessment. This allows the system to optimize signal acquisition at these predetermined points, avoiding the need for post-acquisition reregistration techniques that fail at high k-values.
4Measurement precision
If the data matrix size is increased to image fine tissue texture, then spatial resolution is improved, but acquisition time increases and motion blurring worsens
Solution Approach 1:
The patent segments the k-space acquisition to selectively sample only the specific k-values corresponding to the spatial frequencies of interest for fine tissue texture. This segmented approach provides high spatial resolution for texture assessment without requiring a large complete data matrix, thereby reducing acquisition time and motion blurring.
Solution Approach 2:
The patent applies partial action by acquiring only the necessary k-values for fine texture assessment rather than the complete high-resolution image matrix. This provides sufficient spatial resolution for texture analysis while significantly reducing acquisition time and motion-induced blurring.
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
This approach provides high-resolution, motion-tolerant measurement of tissue texture, enabling accurate detection of early disease markers and monitoring of disease progression, with improved signal-to-noise ratio and ability to differentiate chemical species, thus enhancing diagnostic sensitivity and specificity.
Implementation Method 1
nuclear magnetic resonance (NMR) spectroscopy
Implementation Method 2
a k-encode gradient pulse is applied to induce phase wrap to create a spatial encode for a specific k-value and orientation
Implementation Method 3
The Fourier Transform of the acquired signal data is then taken, wherein for each k-encode, the signal recorded is indicative of the spatial frequency power density at that point in k-space
Implementation Method 4
identify the chemical species of component textural elements in a targeted region of tissue
Data Source
AI summary
A method for identifying the chemical species of various textural elements in a targeted region of tissue wherein a volume of interest (VOI) is selectively excited and a k-encode gradient pulse is applied to induce phase wrap to create a spatial encode for a specific k-value and orientation. The specific k-value is selected based on anticipated texture within the VOI. Multiple sequential samples of the NMR RF signal encoded with the specific k-value are recorded as signal data. The Fourier Transform of the acquired signal data is then taken, wherein for each k-encode, the signal recorded is indicative of the spatial frequency power density at that point in k-space. Each peak in the NMR spectrum is then evaluated, whereby the relative contribution to the texture of tissue in the VOI at a k-value for each chemical species is determined.