MRI Reconstruction with Gradient Nonlinearity Correction
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Solution Overview
Problem
Conventional MRI image reconstruction methods fail to accurately correct gradient nonlinearity, leading to geometric distortion and noise amplification, especially in large field-of-view imaging and compact systems with asymmetric gradient designs, which degrades spatial resolution and introduces artifacts.
Innovation Solution
A model-based method for reconstructing MRI images that prospectively accounts for gradient nonlinearity during the reconstruction process using a computer system, incorporating gradient distortion field data and spatial support constraints to optimize image reconstruction and correct errors associated with gradient nonlinearity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image-domain interpolation is used to correct gradient nonlinearity after reconstruction, then geometric distortion is corrected, but spatial resolution is degraded and noise amplification occurs
Solution Approach 1:
The patent applies preliminary action by incorporating gradient nonlinearity correction into the image reconstruction process itself, rather than applying correction after reconstruction. The distorted gradient fields are accounted for during the reconstruction algorithm, preventing distortion from occurring in the first place and avoiding the need for post-reconstruction interpolation that degrades resolution.
2Stability of the object's composition
If intensity correction using Jacobian-determinant is applied to compensate for GNL-induced uniformity changes, then image uniformity is improved, but noise amplification increases in regions with strong GNL distortion
Solution Approach 1:
The patent prevents noise amplification by incorporating gradient nonlinearity correction during the reconstruction process itself. By accounting for distorted gradient fields upfront in the reconstruction algorithm, the method avoids the need for post-reconstruction intensity correction using Jacobian-determinant, which causes noise amplification in regions with strong gradient nonlinearity distortion.
3Measurement precision
If retrospective gradient distortion correction is applied after image reconstruction, then geometric distortion is corrected, but the effects of finite sampling and undersampling are not accounted for
Solution Approach 1:
The patent applies preliminary action by integrating gradient nonlinearity correction into the image reconstruction process itself. The reconstruction algorithm directly accounts for distorted gradient fields during the reconstruction step, simultaneously addressing both geometric distortion and the effects of finite sampling and undersampling, rather than applying retrospective correction that ignores sampling effects.
Data Source
AI summary
A system and method for simultaneously reconstructing magnetic resonance images and correcting those imaged for gradient nonlinearity effects are provided. As opposed to conventional methods for gradient nonlinearity correction where distortion is corrected after image reconstruction is performed, the model-based method described here prospectively accounts for the effects of gradient nonlinearity during reconstruction and implements a spatial support constraint to reduce noise amplification effects.