Iterative MRI Motion Correction with CNN Image Enhancement
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Solution Overview
Problem
Existing retrospective motion compensation techniques in magnetic resonance imaging (MRI) suffer from image quality degradation and reproducibility issues when highly undersampled data acquisition is used, particularly due to motion during image data acquisition, which affects radiologist interpretation and automated post-processing algorithms.
Innovation Solution
A computer-implemented method involving multiple iterations of motion correction and image quality improvement using a trained machine learning model, specifically a convolutional neural network (CNN) and a non-uniform Fourier-transform, to enhance the quality, robustness, and reproducibility of MRI images by compensating for motion and sparse k-space sampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If highly undersampled data acquisition is used to accelerate imaging, then scan time is reduced, but image quality and reproducibility degrade
Solution Approach 1:
The patent applies preliminary action by acquiring motion guidance lines and performing initial motion estimation before the main imaging sequence. This preliminary motion correction enables subsequent aggressive undersampling without compromising image quality, as the motion artifacts are already addressed in the preprocessing stage.
Solution Approach 2:
The patent implements feedback through iterative optimization processes where motion correction and image reconstruction are performed multiple times with progressively refined results. Each iteration uses the output of the previous iteration as input, allowing the system to adapt and improve image quality while maintaining acceleration benefits.
2Reliability
If retrospective motion correction is applied to compensate for motion, then image quality improves, but processing time and computational cost increase
Solution Approach 1:
The patent performs motion estimation and correction in the preprocessing stage before the main imaging sequence, rather than performing computationally intensive retrospective correction after image acquisition. This preliminary approach reduces the processing time required during the imaging process itself.
Solution Approach 2:
The patent segments the motion correction process into distinct stages: motion guidance line acquisition, motion estimation, and iterative motion correction. This segmentation allows each stage to be optimized independently, reducing overall computational burden while maintaining correction effectiveness.
3Measurement precision
If motion guidance lines and scout scans are acquired to guide motion correction, then motion estimation accuracy improves, but additional scan time is required
Solution Approach 1:
The patent makes the motion guidance lines and scout scans serve multiple functions: they provide motion information for correction, enable quality assessment, and can be used for registration purposes. This multi-functionality reduces the need for separate dedicated acquisitions, minimizing additional scan time.
Data Source
AI summary
A computer-implemented method for providing a final motion corrected image dataset includes: receiving magnetic resonance data; determining a first motion corrected image dataset by solving a first optimization problem, wherein the first optimization problem depends on the magnetic resonance data and on motion data, and wherein the motion data concerns a movement of an object during the acquisition of the magnetic resonance data; processing the first motion corrected image dataset by an algorithm for image quality improvement to provide a processed image dataset; determining a second motion corrected image dataset by solving a second optimization problem that depends on the magnetic resonance data, on the motion data and on the processed image dataset, and either providing the second motion corrected image dataset as the final motion corrected image dataset or determining the provided final motion corrected image dataset based on the second motion corrected image dataset.