MRI Reconstruction via Multi-Modal Compressed Sensing
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Solution Overview
Problem
Magnetic resonance imaging (MRI) is limited by slow acquisition times and limited temporal scanning rates, which restricts the degree of temporal and spatial subsampling, leading to artifacts and reduced spatial resolution when attempting to accelerate image capture.
Innovation Solution
The method extends compressed sensing by incorporating data from a second imaging modality, such as X-ray imaging, to modify the boundary conditions and target function, allowing for increased temporal and spatial subsampling in MRI, using a combined imaging device to register and integrate data from both modalities, and applying sparsifying operators to minimize the l1 norm of the candidate data set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If the temporal scanning rate of MRI is increased to achieve faster image acquisition, then the acquisition time is reduced, but the spatial resolution deteriorates and artifacts increase
Solution Approach 1:
The patent incorporates data from a second imaging modality (e.g., X-ray or CT) into the MRI reconstruction process, effectively adding another dimensional source of information. This multi-modal approach allows the system to compensate for the loss of spatial resolution in accelerated MRI by leveraging the complementary strengths of the second modality, thereby resolving the contradiction between fast acquisition and high resolution
Solution Approach 2:
The patent modifies the boundary conditions and target function of the compressed sensing reconstruction algorithm to incorporate data from the second imaging modality. By changing the reconstruction parameters and optimization criteria, the system can achieve both faster acquisition times and maintained spatial resolution through the integrated multi-modal reconstruction process
2Productivity
If compressed sensing is used to accelerate MRI by subsampling measurement data, then the acquisition time is reduced, but the degree of subsampling is limited due to artifact formation
Solution Approach 1:
The patent merges data from two different imaging modalities (MRI and X-ray/CT) into a unified reconstruction process. By combining the information from both modalities within the compressed sensing framework, the system can achieve higher subsampling factors than would be possible with MRI alone, while maintaining image quality and reducing artifacts through the complementary information provided by the second modality
3Loss of time
If the degree of spatial subsampling is increased to further accelerate MRI, then the acquisition time is reduced, but the spatial resolution and image quality deteriorate significantly
Solution Approach 1:
The second imaging modality serves as an intermediary that bridges the gap between accelerated acquisition and maintained resolution. By incorporating data from this intermediate source into the reconstruction process, the system can achieve higher subsampling factors while the intermediary modality's data compensates for the resolution losses, enabling faster capture times without significant quality deterioration
Data Source
AI summary
A method for reconstructing an image data set from magnetic resonance data is provided. First measurement data is captured using an image capturing device. The first measurement data is captured using temporal and/or spatial subsampling and is used for reconstructing the image data set with a compressed sensing algorithm in which a boundary condition that provided agreement with the measurement data and a target function that is used in an iterative optimization. The compressed sensing algorithm evaluates candidate data sets for the image data set are used. In the reconstruction using the compressed sensing algorithm, in addition to the first measurement data, second measurement data that is captured by a second imaging modality that is different from the first imaging modality of the first measurement data but by the same image capturing device. The second measurement data is registered to the first measurement data, by a modification of the boundary condition and/or target function.