K-Space Based Image Reconstruction of MRI Data Encoded with Ambiguous Gradient Fields

Gerrit Schultz¹, Daniel Gallichan¹, Hans Weber¹, Walter Witschey¹, Matthias Honal¹, Jrgen Hennig¹, Maxim Zaitsev¹

In parallel imaging, acquisition is usually accelerated by omitting k-space lines resulting in aliased images. A similar effect occurs when ambiguous encoding fields are applied instead of the standard linear gradient fields. Highly aliased images are produced when ambiguous field encoding is combined with k-space acceleration. In this case, calibration lines can only be acquired to partially unfold the image. Whereas in SENSE the aliasing artifacts from field ambiguities and from undersampling cannot be treated separately, we show that this is fundamentally different with k-space based methods like GRAPPA. This interesting property of GRAPPA is essential for k-space based image reconstructions from acquisitions based on ambiguous field encoding.