Mayo Foundation for Medical Education and Research

Uten Yarach

Matthew A Bernstein

Joshua D Trzasko

Yi Sui

Myung-Ho In

Images are reconstructed from k-space data using a model-based image reconstruction that prospectively and simultaneously accounts for multiple non-idealities in accelerated single-shot-EPI acquisitions. In some implementations, nonlinear regularization (e.g., sparsity regularization) is also incorporated to mitigate noise amplification. The reconstructed images have reduced distortions and noise amplification effects relative to those images that are processed using conventional post-reconstruction techniques to correct for non-idealities.

Model-based iterative reconstruction for magnetic resonance imaging with echo planar readout

Nyquist ghost artifacts in echo planar imaging ('EPI') are mitigated, reduced, or otherwise eliminated by implementing robust Nyquist ghost correction (“NGC”) directly from two reversed readout EPI acquisitions. As one advantage, these techniques do not require explicit reference scanning. A model-based process is used for directly estimating statistically optimal NGC coefficients from multi-channel k-space data.

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Rochester, MN, United States of America

Uten Yarach