MRI Image Reconstruction Using Multi-Algorithm Correction
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Solution Overview
Problem
MRI images often suffer from intensity inhomogeneity, leading to potential misdiagnosis, as existing image reconstruction methods fail to effectively correct this issue.
Innovation Solution
A method and system for image reconstruction in MRI that involves obtaining multiple coil images, reconstructing images using different algorithms (such as sum of squares and geometric average algorithms), generating correction information based on these images, and correcting intensity inhomogeneity by dividing and normalizing the images to produce a high-quality reconstructed image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single reconstruction algorithm is used, then the reconstruction process is simple and fast, but intensity inhomogeneity cannot be effectively corrected
Solution Approach 1:
The patent combines multiple reconstruction algorithms (first reconstruction algorithm and second reconstruction algorithm) to generate multiple reconstructed images. By merging the results from different algorithms, the system achieves effective intensity inhomogeneity correction while maintaining a systematic approach to image reconstruction.
Solution Approach 2:
The patent introduces correction information as an intermediary element that mediates between multiple reconstructed images and the final corrected image. This correction information, derived from comparing multiple reconstruction results, acts as a bridge to eliminate intensity inhomogeneity while preserving the benefits of multiple algorithms.
2Reliability
If multiple reconstruction algorithms are used, then intensity inhomogeneity can be corrected, but the reconstruction time increases
Solution Approach 1:
The patent performs preliminary reconstruction using multiple algorithms to generate initial reconstructed images and correction information. By pre-computing these intermediate results, the system establishes a foundation for rapid final image generation, reducing the time penalty of using multiple algorithms.
Solution Approach 2:
The system uses the reconstructed images from multiple algorithms to automatically generate correction information that serves the final reconstruction process. The correction information is derived self-consistently from the multiple reconstruction results, eliminating the need for external correction data or manual intervention.
3Measurement precision
If correction information is generated from multiple reconstructed images, then image quality improves, but processing complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where correction information is generated by comparing multiple reconstructed images and then applied back to improve the final image quality. This feedback loop systematically enhances image precision by using the differences between multiple reconstructions to identify and correct intensity inhomogeneity.
Solution Approach 2:
The patent changes the parameters of the reconstruction process by using different reconstruction algorithms with varying characteristics. By adjusting which algorithms are used and how their results are combined, the system optimizes the balance between image quality improvement and processing complexity.
Data Source
AI summary
A method and system for image reconstruction are provided. Multiple coil images may be obtained. A first reconstructed image based on the multiple coil images may be reconstructed based on a first reconstruction algorithm. A second reconstructed image based on the multiple coil images may be reconstructed based on a second reconstruction algorithm. Correction information about the first reconstructed image may be generated based on the first reconstructed image and the second reconstructed image. A third reconstructed image may be generated based on the first reconstructed image and the correction information about the first reconstructed image.