Adaptive Compressed Sensing MRI Motion Artifact Correction
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Solution Overview
Problem
Magnetic resonance imaging (MRI) is adversely affected by patient movement, leading to motion artifacts such as blurring and ghosting, which degrade image quality and can result in misdiagnosis, especially in pediatric and geriatric patients who find it difficult to remain still during prolonged scans.
Innovation Solution
The use of Compressed Sensing (CS) to identify and exclude motion-corrupted data encodes/measurements from the MRI data, allowing for the reconstruction of images without these corrupted data points, thereby reducing motion artifacts. This involves generating subsets of data by omitting individual shots and comparing reconstructed images to determine the least affected subset for further iterations, ultimately producing a high-quality image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the MRI scan time is prolonged to collect sufficient data for high-quality images, then the image resolution and signal-to-noise ratio are improved, but the patient motion artifacts increase
Solution Approach 1:
The patent segments the MRI acquisition data into multiple shots or segments, allowing individual segments to be evaluated and selected based on motion corruption levels. This enables the system to identify and exclude only the corrupted segments while retaining usable data from other segments, thereby maintaining image quality without requiring the entire prolonged scan.
Solution Approach 2:
The patent changes the parameter of data selection by using compressed sensing to identify and remove corrupted encodes based on their consistency with the reconstructed image. This parameter change allows the system to selectively use only the cleanest data segments, achieving high image quality without the need for prolonged scanning that would increase motion artifacts.
2Loss of time
If hardware performance is improved to enable faster imaging, then the scan time is reduced and motion artifacts are decreased, but the sensitivity to motion and phase accumulation increase
Solution Approach 1:
The patent implements a feedback mechanism where the reconstructed image is used to evaluate the quality of individual data segments. Segments that show high consistency with the reconstructed image are identified as clean, while those with low consistency are marked as corrupted. This feedback loop allows the system to adaptively select the best data segments, compensating for the increased motion sensitivity brought by faster hardware.
Solution Approach 2:
The patent replaces the mechanical approach of simply acquiring all data and hoping for the best with an algorithmic approach using compressed sensing and iterative reconstruction. This substitution allows for intelligent selection and weighting of data segments based on their quality, effectively managing the trade-off between fast scanning and motion sensitivity.
3Productivity
If all acquired MRI data is used for image reconstruction, then the data utilization is maximized, but motion-corrupted data degrades the final image quality
Solution Approach 1:
The patent applies partial action by selectively using only the cleanest data segments for final image reconstruction. Rather than using all acquired data equally, the system identifies and excludes corrupted segments, achieving better image quality by using a subset of the most reliable data while still maintaining high data utilization from the usable portions.
Data Source
AI summary
An apparatus and method are provided to correct motion artifacts in magnetic resonance imaging (MRI) data by finding and removing motion-corrupted encodes. The MRI data non-uniformly sample k-space using a series of shots, each including one or more encodes. The motion-corrupted encodes/shots are identified by omitting respective encodes/shots from the MRI data when reconstructing respective images using a compressed-sensing (CS) method. The image quality is improved for those reconstructed images in which the motion-corrupted encodes are omitted, whereas all other images include the motion-corrupted encodes and exhibit the motion artifact. Assuming a minority of encodes/shots are corrupted by motion, the images improved by omitting the motion-corrupted encodes can be identified as outliers. Once, the motion-corrupted encodes are identified and excluded from the final MRI dataset, a final, high-resolution image is reconstructed using the final MRI dataset.