MR Artifact Reduction via Coil Sensitivity Map Exclusion
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Solution Overview
Problem
Radial sampling in magnetic resonance imaging leads to increased streaking artifacts due to undersampling and field inhomogeneities, which affect image quality and diagnostic relevance, with existing methods like iterative reconstruction and oversampling being inefficient or impractical.
Innovation Solution
A method that identifies and excludes magnetic resonance data from specific coil elements responsible for artifacts by comparing sensitivity maps with acquired data, using exclusion information to remove data outside the sensitivity region, thereby reducing streaking artifacts in image datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If radial sampling is used to reduce scan time, then productivity is improved, but manufacturing precision deteriorates due to increased streaking artifacts
Solution Approach 1:
The patent segments the k-space data by separating it into different radial spokes or angular segments. By processing and evaluating each segment individually against sensitivity maps, the method can identify and exclude only the specific segments contributing to artifacts, rather than discarding all data. This selective segmentation maintains image quality while preserving the efficiency of radial sampling.
Solution Approach 2:
The patent applies local quality assessment by comparing sensitivity maps with actual acquired data in specific regions of k-space. The method evaluates data quality locally at different angular positions and radial distances, excluding only those specific regions where artifacts are detected. This localized approach preserves useful data while removing artifact-prone portions, resolving the contradiction between scan time and image quality.
2Productivity
If undersampling is employed to reduce measurement time, then productivity is improved, but manufacturing precision deteriorates due to violation of Nyquist criterion
Solution Approach 1:
The patent performs preliminary action by acquiring sensitivity maps before the actual imaging data collection. These sensitivity maps serve as reference data that enable subsequent identification and exclusion of artifact-prone undersampled data. By preparing this reference information in advance, the method can efficiently handle undersampled data without compromising image quality, thus resolving the contradiction between measurement time and data completeness.
3Manufacturing precision
If iterative reconstruction approaches are used to reduce artifacts, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts and removes the source of artifacts by identifying data points that violate sensitivity map constraints and excluding them before reconstruction. This extraction approach eliminates artifacts at the data selection stage rather than requiring complex iterative reconstruction algorithms to suppress them afterward. The method achieves artifact reduction through simple data exclusion based on sensitivity map comparison, significantly reducing computational complexity while maintaining manufacturing precision.
4Manufacturing precision
If oversampling is applied to reduce artifacts, then manufacturing precision is improved, but productivity deteriorates due to longer measurement time
Solution Approach 1:
The patent applies partial action by using sensitivity maps to selectively exclude only the specific portions of data that contribute to artifacts, rather than requiring complete oversampling of the entire k-space. This partial exclusion approach achieves adequate artifact reduction with the original sampling density, avoiding the time penalty of oversampling while maintaining manufacturing precision.
Data Source
AI summary
In a method and magnetic resonance (MR) apparatus for reducing artifacts in an image dataset reconstructed from MR raw data that were acquired by radial sampling using different coil elements, for each of at least some of the coil elements, exclusion information is determined that identify MR data from that coil element that are responsible for at least one artifact, by a comparison of a sensitivity map, which defines a spatial reception capability of that coil element, with at least one comparison dataset obtained from at least a portion of the MR data from that coil element. At least the MR data identified from the exclusion information are excluded from the reconstruction of the image dataset.