MRI Movement Correction Using Benchmark Slice
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Solution Overview
Problem
Current MRI techniques using non-Cartesian sampling methods face challenges in accurately correcting body movement artifacts, particularly in multi-slice imaging, leading to incorrect slice detection and recalculating correction parameters, which complicates processing and reduces accuracy.
Innovation Solution
The technique involves detecting body movement in specific regions using a benchmark slice and applying correction across all slices, utilizing mathematical analysis and parallel data collection and processing to improve accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If body movement correction is performed using overlap portion data from each slice, then body movement can be detected, but the spatial resolution is insufficient and characteristic points cannot be detected accurately
Solution Approach 1:
The patent divides the correction process into two segments: (1) use overlap portion data to detect body movement parameters, and (2) apply these parameters to correct the entire diagnostic image data. This segmentation allows using low-resolution overlap data for movement detection while preserving high-resolution diagnostic data for final imaging.
Solution Approach 2:
The overlap portion of k-space data serves dual purposes: it provides the information needed for body movement correction while also being part of the complete diagnostic image data. The system uses its own redundant data (the overlap region) to correct itself, eliminating the need for separate correction data acquisition.
2Reliability
If operator manually determines and removes incorrect slices, then incorrect correction data can be eliminated, but the processing becomes complicated and time-consuming
Solution Approach 1:
The patent implements an automated feedback mechanism where the system evaluates the quality of correction parameters derived from each slice's overlap portion, automatically identifies and excludes slices with poor correction quality, and recalculates parameters using only high-quality slices. This closed-loop process eliminates manual intervention while maintaining high correction accuracy.
Solution Approach 2:
The system introduces an automated quality evaluation intermediary that assesses correction parameter reliability and mediates between conflicting slice data. This intermediary automatically determines which slices to include or exclude from correction parameter calculation, replacing manual operator judgment with an objective automated system.
3Measurement precision
If multiple slices are processed sequentially for body movement correction, then each slice can be corrected individually, but the processing time increases
Solution Approach 1:
The patent performs preliminary body movement detection using only the overlap portion data from all slices before proceeding to correct the complete diagnostic image data. By separating the detection phase (using low-resolution overlap data) from the correction phase (applying to high-resolution diagnostic data), the system efficiently processes multiple slices without sacrificing correction precision.
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 enables stable and high-speed body movement correction in MRI, enhancing image accuracy by accurately detecting and correcting movement across all slices, thereby simplifying the processing and reducing processing time.
Implementation Method 1
The MRI apparatus is an apparatus that measures an NMR signal generated by the object, especially, the spins of nuclei which form human tissue
Implementation Method 2
In the imaging, different phase encoding and different frequency encoding are given to NMR signals according to the gradient magnetic field
Data Source
AI summary
In multi-slice imaging of a magnetic resonance imaging apparatus based on a non-Cartesian sampling method in which an overlap portion is generated in k space, stable body movement correction is realized at high speed. In order to do so, the rotation and translation of an object is detected for each specific region (in the case of a hybrid radial method, each blade) using a most characteristic slice in the imaging region, and the detected body movement is used for body movement correction of the specific region in all slices. The slice used for correction may be determined using a mathematical analysis result, such as correlation. In addition, data collection and correction processing may be performed in parallel.