MRI Image Reconstruction with Spatially-Varying Coefficients
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current parallel or partially parallel MRI methods suffer from strong noise amplification and unresolved aliasing artifacts when using higher reduction factors, and require time-consuming calibration scans that can be affected by target motion.
Innovation Solution
A method for reconstructing MRI images from sensitivity-encoded data using spatially-varying reconstruction coefficients and neighborhoods in the frequency domain, combining undersampled data from multiple receiver coils to estimate unacquired data points, thereby producing a fully sampled k-space dataset with reduced artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If parallel MRI methods use higher reduction factors to decrease data acquisition time, then productivity is improved, but manufacturing precision deteriorates due to strong noise amplification and unresolved aliasing artifacts
Solution Approach 1:
The patent performs preliminary calibration to determine spatially-varying reconstruction coefficients before actual image reconstruction. This preliminary action stores the coil sensitivity information and reconstruction parameters, enabling fast reconstruction without calibration during the actual imaging process, thus resolving the contradiction between speed and quality.
Solution Approach 2:
The patent uses spatially-varying reconstruction coefficients that are specific to different locations in the image space. Each coefficient is tailored to the local coil sensitivity characteristics, providing optimal reconstruction quality at each spatial location while maintaining overall image fidelity even at high reduction factors.
2Productivity
If parallel MRI methods use higher reduction factors to decrease data acquisition time, then productivity is improved, but reliability deteriorates due to noise amplification and artifacts
Solution Approach 1:
The patent changes the parameters used for reconstruction by employing spatially-varying reconstruction coefficients instead of uniform coefficients. This parameter change adapts the reconstruction process to local coil sensitivity variations, maintaining image accuracy and reliability even when using high reduction factors for faster acquisition.
Solution Approach 2:
The patent incorporates feedback from the calibration process where reconstruction coefficients are determined based on measured coil sensitivity data. This feedback mechanism ensures that the reconstruction algorithm compensates for the effects of undersampling, maintaining reliability despite high reduction factors.
3Manufacturing precision
If calibration scans are performed to improve image reconstruction accuracy, then manufacturing precision is improved, but loss of time increases due to time-consuming calibration scans
Solution Approach 1:
The patent performs calibration scans as a preliminary action before actual imaging, storing the reconstruction coefficients in memory. This one-time preliminary calibration enables subsequent fast reconstructions without requiring repeated calibration scans, thus resolving the time-accuracy tradeoff.
Solution Approach 2:
The patent creates a copy of the coil sensitivity information and reconstruction parameters during calibration, storing them for reuse. This copying allows the system to avoid repeating the time-consuming calibration process for each imaging scan, maintaining accuracy while reducing time loss.
Data Source
AI summary
Methods and systems in a parallel magnetic resonance imaging (MRI) system utilize sensitivity-encoded MRI data acquired from multiple receiver coils together with spatially dependent receiver coil sensitivities to generate MRI images. The acquired MRI data forms a reduced MRI data set that is undersampled in at least a phase-encoding direction in a frequency domain. The acquired MRI data and auto-calibration signal data are used to determine reconstruction coefficients for each receiver coil using a weighted or a robust least squares method. The reconstruction coefficients vary spatially with respect to at least the spatial coordinate that is orthogonal to the undersampled, phase-encoding direction(s) (e.g., a frequency encoding direction). Values for unacquired MRI data are determined by linearly combining the reconstruction coefficients with the acquired MRI data within neighborhoods in the frequency domain that depend on imaging geometry, coil sensitivity characteristics, and the undersampling factor of the acquired MRI data. An MRI image is determined from the reconstructed unacquired data and the acquired MRI data.