Compressed MRF Dictionary via rSVD for MRI Memory Reduction
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Solution Overview
Problem
Conventional magnetic resonance fingerprinting (MRF) dictionaries are excessively large, posing challenges in memory requirements, processing, and storage, especially when fine dictionaries or multiple components are considered, making it difficult to generate, store, and process tissue parameter maps effectively.
Innovation Solution
The use of a compressed MRF dictionary generated through randomized singular value decomposition (rSVD) and polynomial fitting methods reduces memory requirements significantly, allowing for high-resolution tissue parameter maps to be produced with much less memory, avoiding the need to store the entire dictionary in computer memory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fine MRF dictionary with multiple components is used to improve tissue characterization accuracy, then measurement precision is improved, but the quantity of data increases to billions of elements, making storage and processing difficult
Solution Approach 1:
The patent extracts only the most significant components from the full MRF dictionary by identifying and retaining top-ranked signal evolutions that contribute most to tissue characterization accuracy. This extraction process removes redundant data while preserving the essential information needed for accurate tissue property mapping.
Solution Approach 2:
The patent transforms the dictionary representation by changing parameters such as signal evolution sampling rates, dictionary resolution levels, and component ranking thresholds. These parameter changes enable the dictionary to maintain accuracy while reducing overall data size through optimized sampling and selective retention of critical signal characteristics.
2Measurement precision
If a large MRF dictionary is stored in computer memory to maintain high resolution tissue parameter maps, then measurement precision is improved, but device complexity and memory requirements increase significantly
Solution Approach 1:
The patent segments the large MRF dictionary into multiple smaller subsets or blocks that can be stored and processed separately. This segmentation allows the system to load only necessary portions of the dictionary into memory at any given time, reducing peak memory requirements while maintaining the ability to access high-resolution tissue parameter information through systematic processing of segmented data.
3Measurement precision
If the entire MRF dictionary is processed to generate tissue parameter maps, then measurement precision is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-ranking and pre-selecting the most informative signal evolutions before actual tissue parameter mapping is performed. This preliminary ranking and selection process creates a compressed, prioritized dictionary structure that enables faster processing during actual use while preserving the accuracy needed for precise tissue property identification.
Data Source
AI summary
A system and method is provided for generating a map of a tissue property in a subject using magnetic resonance fingerprinting (MRF) and a compressed MRF dictionary, where the compressed MRF dictionary has a significantly reduced memory requirement relative to a standard MRF dictionary. The method includes performing a randomized singular value decomposition (rSVD) on a MRF dictionary to produce the compressed MRF dictionary. MRF data is then acquired and compared to the MRF dictionary to identify the tissue property from the region of interest in the subject. A tissue property map is then generated based on the tissue in the region of interest of the subject.