MRI Joint Image Reconstruction and Motion Estimation
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Solution Overview
Problem
Patient motion during MRI scans causes image artifacts, leading to degraded diagnostic utility and increased costs or risks when attempting to correct for motion, as existing methods are either ineffective or impractical.
Innovation Solution
A joint optimization method for image reconstruction and motion estimation that operates on motion-corrupted k-space data, allowing for the estimation of motion parameters and reconstruction of a substantially motion-free image using a parallel imaging formulation, without requiring prior motion information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If motion correction methods such as video tracking systems or MR navigators are used, then motion parameters can be detected, but the methods suffer from low sensitivity, disrupt optimal timing of MR pulse sequence, or require complex marker attachment
Solution Approach 1:
The patent extracts and removes the need for external motion tracking devices and markers by implementing motion estimation directly from the MRI signal data itself. The method uses the acquired k-space data to jointly estimate motion parameters and reconstruct images, eliminating complex marker attachment procedures and external tracking systems while maintaining motion detection capability.
Solution Approach 2:
The patent makes the MRI acquisition system multi-functional by enabling it to simultaneously perform image acquisition and motion estimation using the same k-space data. The joint optimization framework allows the MRI system to extract both diagnostic image information and motion parameters from the same signal source, eliminating the need for separate motion tracking hardware.
2Reliability
If conventional motion correction methods are used, then some motion parameters can be estimated, but reconstruction time increases and image quality deteriorates due to artifacts
Solution Approach 1:
The patent merges the motion estimation process and image reconstruction process into a single joint optimization framework. Instead of performing motion correction and reconstruction as separate sequential steps, the method simultaneously estimates motion parameters and reconstructs images from the acquired k-space data, improving both reliability and efficiency.
Solution Approach 2:
The patent performs preliminary motion estimation during the image reconstruction process itself, using the acquired k-space data to jointly estimate motion parameters before final image generation. This preliminary action allows motion artifacts to be corrected during reconstruction rather than requiring separate post-processing steps.
3Object-affected harmful factors
If patient motion is corrected using general anesthesia, then motion artifacts are eliminated, but the cost of the scan more than doubles and risks are magnified
Solution Approach 1:
The patent enables the MRI system to self-correct motion artifacts by using the acquired signal data to estimate motion parameters and reconstruct motion-free images. The system serves its own motion correction needs without requiring external intervention such as general anesthesia, thereby eliminating the associated costs and risks while effectively removing motion artifacts.
4Measurement precision
If MR navigator scans are used for motion correction, then motion parameters can be obtained, but the sensitivity is low and optimal timing of MR pulse sequence is disrupted
Solution Approach 1:
The patent combines motion estimation and image reconstruction into a single integrated process that uses the same k-space data for both purposes. This merging eliminates the need for separate navigator scans, maintaining scan efficiency while improving motion parameter accuracy through joint optimization.
Solution Approach 2:
The patent makes the MRI acquisition multi-functional by using the primary imaging k-space data to simultaneously provide both diagnostic image information and accurate motion parameters. This eliminates the need for dedicated navigator scans that disrupt pulse sequence timing, as the motion estimation is performed on the same data used for image reconstruction.
Data Source
AI summary
Described here are systems and methods for retrospectively estimating and correcting for rigid-body motion by using a joint optimization technique to jointly solve for motion parameters and the underlying image. This method is implemented for magnetic resonance imaging (“MRI”), but can also be adapted for other imaging modalities.