MRI Motion Correction via Slice-Specific Volumetric Registration
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Solution Overview
Problem
Current MRI systems lack effective methods to reliably identify and correct for motion-related signal artifacts, which can result in corrupted data due to head and brain movement during image acquisition, especially in functional MRI where subtle changes in blood oxygenation levels are critical.
Innovation Solution
A method and system for detecting motion in MRI systems by capturing a time series of volumetric images as two-dimensional slices, calculating representative values, generating simulated volumetric time series, and performing volumetric registration to estimate motion parameters, which are then used to correct for motion-related signal changes through regression analysis and normalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If motion correction is performed using traditional volumetric registration methods, then computational efficiency is improved, but motion detection precision deteriorates due to inability to capture slice-specific motion variations
Solution Approach 1:
The patent segments the volumetric image into individual slices and performs motion correction on each slice independently. This allows capture of slice-specific motion variations while maintaining computational efficiency by processing smaller 2D slice data rather than entire 3D volumes for each motion parameter estimation.
Solution Approach 2:
The patent transitions from traditional 3D volumetric registration to a hybrid approach where 2D slice motion parameters are estimated and then applied to correct the 3D volumetric data. This dimensional transformation enables precise motion detection at the slice level while maintaining overall volumetric correction efficiency.
2Measurement precision
If BOLD contrast sensitivity is increased to detect subtle brain activity changes, then functional imaging capability is improved, but susceptibility to motion artifacts increases
Solution Approach 1:
The patent performs motion parameter estimation and motion correction in advance of the actual BOLD signal analysis. By pre-characterizing motion artifacts through the slice motion detection and regression modeling, the system can remove motion effects before functional analysis, thereby preserving BOLD contrast sensitivity without contamination from motion artifacts.
Solution Approach 2:
The patent converts the harmful effect of motion artifacts into a beneficial process by using the detected motion parameters as regressors in the BOLD signal analysis. The motion information, which initially corrupts the data, is transformed into correction terms that enable more accurate functional imaging by separating motion effects from true BOLD signal changes.
3Speed
If slice acquisition time is reduced to improve temporal resolution, then imaging speed is improved, but motion correction accuracy deteriorates due to fewer time points for analysis
Solution Approach 1:
The patent creates simulated volumetric time series by copying and interpolating slice data to represent the complete 3D volume motion characteristics. This allows motion correction analysis to be performed on the simulated data which contains sufficient temporal information, enabling accurate motion correction even when actual acquisition time is reduced.
Solution Approach 2:
The patent changes the temporal parameter by using the representative value time series as a reference for motion estimation. By normalizing and aligning the timing of representative value acquisitions with the slice acquisition timeline, the system can accurately estimate motion parameters even with reduced temporal sampling, maintaining correction accuracy while improving imaging speed.
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 accurate characterization and correction of motion artifacts, improving the sensitivity and specificity of image data analysis by accounting for six degrees of freedom in slice motion, thereby reducing BOLD sensitivity reduction and enhancing image quality.
Implementation Method 1
MRI makes use of the property of nuclear magnetic resonance to image nuclei of atoms inside the body. An MRI machine uses a powerful magnetic field to align the magnetization of some atoms in the body, and radio frequency fields to systematically alter the alignment of this magnetization. This causes the nuclei to produce a rotating magnetic field detectable by the scanner
Implementation Method 2
A magnetic resonance imaging scanner is configured to capture a time series of volumetric images of a region of interest at the imaging system
Data Source
AI summary
Systems and methods are provided for detecting motion in an imaging system. A time series of volumetric images of a region of interest are captured at the imaging system. Each volumetric image of the time series of volumetric images is captured as a series of two-dimensional slices of the region of interest. A representative value is calculated for each voxel to create a representative volumetric dataset representing the region of interest. For each slice of the series of two-dimensional slices, a simulated volumetric time series is generated, including time series data for the slice and the calculated representative value at all times for the other slices of the series of two-dimensional slices. A volumetric registration is performed on each of the simulated volumetric time series to provide a set of estimated motion parameters for the slice associated with the simulated volumetric time series.