Wave-CAIPI MR Reconstruction with Bin-Dependent PSF Calibration
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Solution Overview
Problem
Existing Wave-CAIPI methods for magnetic resonance imaging (MRI) struggle to account for patient movements, leading to image artifacts due to the dependency of PSF subfunctions on absolute gradient amplitudes and orientations, especially when combined with prospective motion correction methods.
Innovation Solution
A method that monitors patient movement during an MRI scan and assigns k-space lines to bins based on movement values, performing PSF calibration for each bin to adapt PSF subfunctions for image reconstruction, allowing for improved image quality even with patient movements by using bin-dependent PSF subfunctions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If identical calibrated PSF subfunctions are used for all k-space lines in Wave-CAIPI reconstruction, then the reconstruction process is simple and fast, but image artifacts occur when patient movement exceeds threshold values
Solution Approach 1:
The patent segments the k-space lines into multiple bins based on patient movement values. Each bin receives PSF subfunctions calibrated for its specific movement range, rather than applying a single identical PSF to all k-space lines. This segmentation resolves the contradiction by enabling movement-specific reconstruction while maintaining computational efficiency through bin-based organization.
Solution Approach 2:
The patent introduces dynamic adaptation of PSF subfunctions based on monitored patient movement. The system transitions from static identical PSFs to dynamic movement-dependent PSFs, where the reconstruction parameters adapt in real-time to patient motion states. This dynamic approach maintains image quality across varying movement conditions while preserving reconstruction efficiency through predefined bins.
2Reliability
If PSF calibration is performed for each bin based on patient movement, then image quality is maintained during patient movement, but the computational overhead increases
Solution Approach 1:
The patent performs PSF calibration only for representative bins rather than all possible bins. By selecting key bins that cover the range of patient movements and interpolating or reusing PSFs for intermediate bins, the system achieves adequate image quality without the full computational burden of calibrating every possible movement state. This partial calibration approach resolves the contradiction between quality and complexity.
Solution Approach 2:
The patent changes the calibration approach from fixed identical parameters to movement-dependent parameters organized in bins. By pre-calculating PSF subfunctions for different movement bins and selecting appropriate bins based on monitored movement values, the system adapts parameters dynamically without performing full calibration computations for every reconstruction, thus managing computational overhead while maintaining quality.
3Reliability
If Wave-CAIPI is combined with prospective motion correction, then patient movements can be compensated, but image artifacts still occur due to dependency of PSF subfunctions on absolute gradient amplitudes and orientations
Solution Approach 1:
The patent applies different PSF subfunctions to different bins based on local patient movement characteristics. Instead of using a single global PSF that becomes inaccurate with movement, the system selects locally optimized PSFs matched to each bin's movement range. This local quality approach resolves the contradiction by ensuring PSF accuracy is maintained locally within each bin even when global patient position changes.
Data Source
AI summary
A method serves for MR-based reconstruction of images of a patient. Whether a value of a movement of the patient in at least one motion direction during an MR scan exceeds a respective threshold value is monitored. If this is not the case, an image reconstruction is performed by a Wave-CAIPI method on the basis of identical calibrated PSF subfunctions for all k-space lines. When this is the case, a number of bins are provided that correspond to sequential value ranges of the patient movement in at least one motion direction, the k-space lines are assigned to the bins based on a movement value determined during their respective acquisition, a calibration of PSF subfunctions is performed for at least two bins on the basis of the k-space lines assigned to said bins, and an image reconstruction is performed by a Wave-CAIPI method in such a way that the PSF subfunctions associated with the assigned bins are used for the respective k-space lines.