MRI Distortion Correction via Reference Image Registration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques for correcting distortion in magnetic resonance images (MRI) due to magnetic field variations are inefficient, requiring longer acquisition times and being susceptible to noise and resolution limitations, especially at high fields.
Innovation Solution
A method and system that receive a distorted target MRI image and an undistorted reference image, using image registration with optimized parameters to perform image transformation and generate a corrected image, employing techniques like non-rigid transformations and similarity metrics for accurate distortion correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If field mapping techniques are used to correct distortion, then distortion correction accuracy is improved, but acquisition time increases and noise susceptibility worsens
Solution Approach 1:
The patent applies preliminary action by acquiring a reference image before the actual imaging sequence. This reference image is used to pre-calculate distortion correction parameters, allowing the main imaging process to proceed faster without requiring time-consuming field mapping during the actual scan. The reference image acquisition is performed once and reused for multiple corrections.
Solution Approach 2:
The patent uses a reference image as a copy of the undistorted image structure. By comparing the distorted target image with this reference copy, the system can calculate distortion vectors and correct geometric distortions without re-acquiring the entire image sequence. This copying approach eliminates the need for repeated acquisitions in opposite directions.
2Reliability
If conventional MR imaging is used, then image quality is improved, but scan time increases
Solution Approach 1:
The patent performs preliminary distortion correction using a reference image acquired before the main imaging sequence. This allows the actual imaging to proceed at high speed using EPI techniques, while the distortion correction is handled separately through pre-calculated transformation parameters, thus maintaining both speed and quality.
Solution Approach 2:
The patent segments the imaging process into two independent parts: fast acquisition of the target image using EPI, and separate distortion correction using reference image comparison. This segmentation allows each part to be optimized independently - the acquisition for speed and the correction for accuracy - without compromising either aspect.
3Productivity
If EPI is used for fast imaging, then scan time is reduced, but image distortion increases
Solution Approach 1:
The patent implements feedback by comparing the distorted target image with the reference image and using this comparison to calculate distortion correction parameters. The distortion vectors derived from this feedback loop are then applied to correct the geometric distortions, allowing EPI to maintain its speed advantage while achieving accurate geometric representation.
Solution Approach 2:
The patent changes parameters by calculating and applying distortion correction vectors that map distorted pixel locations to their correct positions. These parameter transformations (translation, rotation, scaling) are derived from the reference image comparison and applied to rectify the geometric distortions introduced during fast EPI acquisition.
Data Source
AI summary
A method implemented using at least one processor includes receiving a target image and a reference image. The target image is a distorted magnetic resonance image and the reference image is an undistorted magnetic resonance image. The method further includes selecting an image registration method for registering the target image to the reference image, wherein the image registration method uses an image transformation. The method further includes performing image registration of the target image with the reference image, wherein the image registration provides a plurality of optimized parameters of the image transformation. The method also includes generating a corrected image based on the target image and the plurality of optimized parameters of the image transformation.