Abstract #0835

ECcentric Circle ENcoding TRajectorIes for Compressed-sensing (ECCENTRIC): A fully random non-Cartesian sparse k-space sampled MRSI at 7 Tesla

Antoine Klauser^1,2,3, Bernhard Strasser^2,4, Wolfgang Bogner⁴, Lukas Hingerl⁴, Claudiu Schirda⁵, Bijaya Thapa², Daniel Cahill⁶, Tracy Batchelor⁷, François Lazeyras^1,3, and Ovidiu Andronesi²

¹Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland, ²Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States, ³CIBM Center for Biomedical Imaging, Geneva, Switzerland, ⁴High‐Field MR Center, Department of Biomedical Imaging and Image‐guided Therapy, Medical University of Vienna, Vienna, Austria, ⁵Department of Radiology, University of Pittsburgh, Pittsburgh, PA, United States, ⁶Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States, ⁷Department of Neurology, Brigham and Women, Harvard Medical School, Boston, MA, United States

A new encoding trajectory for magnetic resonance spectroscopic imaging was developed and implemented on a 7T human scanner. ECcentric Circle ENcoding TRajectorIes for Compressed-sensing (ECCENTRIC) is a spatial-spectral encoding strategy optimized for random non-Cartesian sparse Fourier domain sampling. Acceleration by undersampling ECCENTRIC prevents coherent aliasing artefacts in the spatial response function. ECCENTRIC allows smaller circles to avoid temporal interleaving for large matrix size, which is beneficial for spectral quality. Circle trajectories need limited gradient slewrate without rewinding deadtime, and are robust to timing imperfection and eddy-current delays.

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