Parallel Imaging Compressed Sensing MR Reconstruction
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Solution Overview
Problem
Combining parallel imaging and compressed sensing techniques in MR imaging reduces computational efficiency, negating benefits of individual techniques, and results in suboptimal image reconstruction with significant artifacts and prolonged scan times.
Innovation Solution
An MR imaging system and method that acquires undersampled k-space data, synthesizes unacquired data using parallel imaging, and applies compressed sensing reconstruction to generate high-quality images, separating the techniques to optimize each phase and improve computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If parallel imaging and compressed sensing are combined by including parallel imaging as a data consistency constraint in compressed sensing reconstruction, then image reconstruction quality is improved, but computational efficiency is greatly reduced
Solution Approach 1:
The patent divides the reconstruction process into two separate stages: first applying parallel imaging to synthesize missing k-space data, then applying compressed sensing to the synthesized data. This segmentation allows each technique to be optimized independently, avoiding the computational burden of combining them in a single iterative reconstruction process while maintaining image quality.
Solution Approach 2:
The patent performs parallel imaging synthesis as a preliminary step before applying compressed sensing reconstruction. By pre-synthesizing the missing k-space data using parallel imaging, the subsequent compressed sensing process works with more complete data, improving reconstruction quality without requiring the computationally intensive combined optimization of both techniques simultaneously.
2Loss of time
If undersampled k-space data is acquired to reduce scan time, then scan time is reduced, but image quality deteriorates due to artifacts
Solution Approach 1:
The patent uses parallel imaging synthesis as an intermediary process that takes the undersampled k-space data and generates synthesized data to fill in the missing samples. This intermediary step creates a more complete data set that can then be processed by compressed sensing to produce high-quality images without requiring full k-space sampling, thus maintaining image quality while reducing scan time.
Solution Approach 2:
The patent combines the results of parallel imaging synthesis with compressed sensing reconstruction in a composite approach. The parallel imaging component provides synthesized data to supplement the undersampled measurements, while the compressed sensing component reconstructs the final image by exploiting signal sparsity. This composite methodology achieves both accelerated scanning and high image quality.
Data Source
AI summary
A system and method for combining parallel imaging and compressed sensing techniques to reconstruct an MR image includes a computer programmed to acquire undersampled MR data for a plurality of k-space locations that is less than an entirety of a k-space grid. The computer is further programmed to synthesize unacquired MR data by way of a parallel imaging technique for a portion of k-space location at which MR data was not acquired and apply a compressed sensing reconstruction technique to generate a reconstructed image from the acquired undersampled MR data and the synthesized unacquired data.