Biomedical Image Reconstruction Using Correction Matrices
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for reconstructing biomedical images using compressed sensing are limited in utilizing redundant information for artifact correction and do not effectively incorporate prior knowledge of artifacts, especially when multiple sets of image data are acquired under different undersampling schemes or modes, leading to incomplete artifact suppression.
Innovation Solution
The method involves acquiring two or more sets of image data using different undersampling schemes or acquisition modes, multiplying each data set by a correction matrix calculated from a mathematical model of expected artifacts, and using a nonlinear iterative algorithm to combine the data sets, ensuring fidelity to corrected acquired data and suppressing incoherent artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sets of image data are acquired using different undersampling schemes to suppress artifacts, then image quality is improved, but the complexity of the reconstruction algorithm increases
Solution Approach 1:
The reconstruction algorithm is segmented into distinct functional modules: a combination module that merges multiple undersampled data sets, a artifact suppression module that identifies and removes incoherent artifacts, and a reconstruction module that generates the final image. This modular segmentation makes the complex algorithm more manageable and implementable while achieving superior image quality through systematic artifact suppression.
2Measurement precision
If multiple sets of image data are acquired with different undersampling schemes, then artifact suppression is improved, but acquisition time increases
Solution Approach 1:
Multiple data sets are acquired by changing the undersampling parameters (different sampling patterns, different acceleration factors) rather than acquiring completely separate data sets. This allows the system to exploit parameter variability to make artifacts incoherent across data sets, improving artifact suppression while minimizing the increase in acquisition time by reusing the same basic acquisition protocol with modified parameters.
3Measurement precision
If a correction matrix is applied to each data set based on mathematical models of expected artifacts, then fidelity to corrected data is improved, but computational complexity increases
Solution Approach 1:
Correction matrices are pre-calculated based on mathematical models of expected artifacts before the actual reconstruction process. These pre-computed correction matrices account for common artifact patterns, allowing the system to quickly apply corrections during reconstruction without performing complex real-time calculations. This preliminary action maintains high fidelity to corrected data while reducing the computational burden during the actual reconstruction phase.
Data Source
Figure 1
Figure 2a~2b
Figure 3a~3c
AI summary
A method of reconstructing an MRI or ultrasound biomedical image, based on compressed sensing and comprising the steps of acquiring several sets of image data from said generated signals, each data set being acquired in a different undersampling scheme and/or a different acquisition mode such as to make expected and unavoidable artifacts incoherent. Each of the acquired image data set is multiplied by a correction matrix Δ, which is calculated from a mathematical model of expected artifacts according to prior knowledge, for adjusting fidelity of the reconstructed image to the corrected acquired image data. For each iteration of said nonlinear iterative algorithm the data sets are processed to generate a combination image which will therefore be faithful to the acquired data but not to the incoherent artifacts.