MRI Image Reconstruction via K-Space Data Segmentation
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Solution Overview
Problem
Magnetic Resonance Imaging (MRI) apparatuses face challenges in reducing artifacts caused by subject movement during imaging, as existing methods like PROPELLER increase imaging time and may not effectively handle movements detected by external devices.
Innovation Solution
The MRI apparatus employs a sequence controller and image generator that acquire k-space data and selectively reconstruct images using data sets arranged in a time series, detecting movement using inter-frame errors and norms like L1 norm to identify and exclude affected data, thereby reducing artifacts and shortening imaging time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the PROPELLER method is used to reduce movement artifacts, then artifact reduction is improved, but imaging time increases
Solution Approach 1:
The patent segments the k-space data into multiple time-series data sets based on the sampling timing. Each data set corresponds to a specific time period during the imaging process. By segmenting the data in this manner, the system can identify and exclude data sets affected by movement without requiring complete re-imaging, thus reducing artifacts while minimizing time loss.
Solution Approach 2:
The patent extracts and removes data sets that are affected by subject movement from the k-space data. By identifying movement-affected data through inter-frame error analysis and excluding these problematic data sets, the system reconstructs images without the harmful artifacts, achieving artifact reduction without the need for extended imaging time.
2Measurement precision
If full k-space data is sampled at high resolution, then image quality is improved, but imaging time increases
Solution Approach 1:
The patent applies partial sampling by selecting only the necessary data sets from the time-series k-space data. Instead of requiring complete full sampling, the system identifies movement-affected portions and excludes them, then reconstructs images from the remaining valid data. This partial action approach maintains image quality while reducing the effective imaging time required.
3Reliability
If movement correction is performed by integrating multiple blades, then artifact reduction is improved, but imaging time increases
Solution Approach 1:
The patent extracts and removes movement-affected data sets from the k-space data rather than attempting to correct movements through integration of multiple blades. By identifying which data sets are contaminated by movement and excluding them, the system achieves movement correction more efficiently, reducing the time required compared to the PROPELLER method's blade integration approach.
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 allows for the generation of high-quality images by selecting data sets unaffected by movement, reducing artifacts and minimizing the need for repeated imaging processes.
Implementation Method 1
Magnetic resonance imaging apparatuses (hereinafter, 'MRI apparatuses', as necessary) are apparatuses used for imaging information on the inside of a subject, by using a nuclear magnetic resonance phenomenon. An MRI apparatus acquires data called k-space data, by sampling nuclear magnetic resonance signals from specific atoms (e.g., hydrogen atoms) that are present on the inside of an object, by using coils.
Implementation Method 2
the MRI apparatus reconstructs a Magnetic Resonance image (hereinafter, an 'MR image', as necessary) by applying a Fourier transform to the k-space data
Implementation Method 3
the image generator extracts a plurality of data sets arranged in a time series from the k-space data, selects a data set from among the extracted plurality of data sets on the basis of a movement of a subject
Data Source
AI summary
A magnetic resonance imaging apparatus according to an embodiment includes a sequence controller and an image generator. The sequence controller acquires k-space data corresponding to a predetermined quantity of encoding processes. The image generator extracts a plurality of data sets arranged in a time series from the k-space data, selects a data set from among the extracted plurality of data sets on the basis of a movement of a subject, and generates an image by using the selected data set.