Convolutional Filter for MRI K-Space Data Processing
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Solution Overview
Problem
Current magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) techniques face challenges in increasing data acquisition speed and enhancing the signal-to-noise ratio (SNR) to improve image quality and precision.
Innovation Solution
A method involving the use of convolutional filters applied to k-space datasets, which include weighting factors, to process and modify data points within specific application regions, thereby enhancing SNR and image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data acquisition speed is increased by reducing the number of sampled data points, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
A convolutional filter is introduced as an intermediary processing step between the under-sampled k-space data and the final image reconstruction. The filter processes multiple under-sampled datasets in parallel, using weighting factors to combine information from surrounding data points, thereby recovering precision that would otherwise be lost due to under-sampling.
Solution Approach 2:
The patent changes the sampling parameters by acquiring fewer data points (under-sampling) while compensating through post-processing. By adjusting the weighting factors in the convolutional filter and processing multiple datasets with different sampling patterns, the system maintains measurement precision despite reduced sampling density.
2Loss of time
If the number of sampled data points is reduced to accelerate acquisition, then loss of time is reduced, but loss of information increases
Solution Approach 1:
Multiple under-sampled k-space datasets are merged through parallel processing and convolutional filtering. By combining information from multiple datasets acquired with different sampling patterns, the system recovers signal information that would be lost in any single under-sampled dataset, thereby reducing overall information loss despite faster acquisition.
Solution Approach 2:
The convolutional filter continuously processes multiple under-sampled datasets to reconstruct the full k-space information. This continuous processing ensures that no useful signal information is permanently lost during the accelerated acquisition process, as the filtering operation recovers missing data points from the combined information in all datasets.
3Device complexity
If conventional filtering is applied to individual k-space datasets, then device complexity is reduced, but manufacturing precision deteriorates
Solution Approach 1:
The patent extends the filtering operation from a single-dataset approach to a multi-dataset dimension. Instead of applying filters independently to each dataset, the convolutional filter operates across multiple datasets simultaneously, adding a new dimension of parallel processing that enhances image quality without proportionally increasing complexity.
Solution Approach 2:
The convolutional filter serves multiple functions: it processes multiple under-sampled datasets, performs information recovery, and reconstructs the full k-space data. This multi-functional approach achieves high image quality without requiring separate complex processing systems for each function, thereby managing complexity effectively.
Data Source
AI summary
A method for magnetic resonance imaging (MRI) may include obtaining a plurality of k-space datasets related to a scanning of a subject performed by an MRI scanner. The method may also include obtaining at least one filter for processing the plurality of k-space datasets, and applying the at least one filter to the plurality of k-space datasets convolutionally to obtain a plurality of processed k-space datasets. By applying the at least one filter, an application region extending through the plurality of k-space datasets may be determined within the plurality of k-space datasets, and a data point within the application region may be modified based at least on the other data points within the application region. The method may further include generating an image based on at least one of the processed k-space datasets.