Compressed sensing MRI using random B1 field

Therefore, the technology has the potential to revolutionize MRI speed, making real-time, high-resolution imaging possible. It has important applications in dynamic and functional imaging, such as cardiac imaging, functional imaging, and dynamic contrast-enhanced (DCE) imaging, especially in three-dimensional imaging. Magnetic resonance imaging (MRI) has revolutionized radiology for the last three decades because of its unique capabilities for structural, physiological, and functional imaging. However, its applications are still rather limited due to its relatively low imaging speeds. This invention applies compressed sensing, a new theoretical framework for signal recovery with very few samples, to MRI using random encoding. Since the MRI acquisition time is directly related to the number of samples, the application of compressed sensing to MRI is able to reduce acquisition time. Because the conventional Fourier encoding in MRI has partially satisfied the compressed sensing conditions, there is some existing work on compressed sensing MRI using Fourier encoding. However, the requirement of random sampling in compressed sensing is impractical in MRI. Any practical sampling trajectory must satisfy hardware and physiological constraints and follow relatively smooth lines and curves. In this invention, a completely new encoding scheme was proposed using random B1 field in replace of the Fourier encoding. It is the first time that non-Fourier encoding is considered for compressed sensing in MRI. The work was presented at the annual meeting of the International Society for Magnetic Resonance in Medicine (ISMRM) 2008 and won the first place in the poster awards. Our new scheme was motivated by the fact that the random encoding better satisfies the compressed sensing requirements than the conventional Fourier encoding used in all existing work. The reconstruction results show the proposed random encoding method reconstructs more details than the randomly sampled Fourier encoding when the same reduction factor is used. Jessica Silvaggi

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