Excitation and geometrically matched local encoding of curved slices
Autor: | Daniel Gallichan, Chris A. Cocosco, Gerrit Schultz, Wilfried Reichardt, Maxim Zaitsev, Jürgen Hennig, Hans Weber, Anna Welz, Sebastian Littin, Walter R Witschey |
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Rok vydání: | 2012 |
Předmět: |
Field (physics)
Brain Reproducibility of Results Geometry Iterative reconstruction Image Enhancement computer.software_genre Magnetic Resonance Imaging Sensitivity and Specificity Imaging phantom Pattern Recognition Automated Nonlinear system Superposition principle Imaging Three-Dimensional Voxel Encoding (memory) Image Interpretation Computer-Assisted Humans Radiology Nuclear Medicine and imaging Algorithm computer Algorithms Excitation Mathematics |
Zdroj: | Magnetic Resonance in Medicine. 69:1317-1325 |
ISSN: | 0740-3194 |
Popis: | In this work, the concept of excitation and geometrically matched local in-plane encoding of curved slices (ExLoc) is introduced. ExLoc is based on a set of locally near-orthogonal spatial encoding magnetic fields, thus maintaining a local rectangular shape of the individual voxels and avoiding potential problems arising due to highly irregular voxel shapes. Unlike existing methods for exciting curved slices based on multidimensional radiofrequency-pulses, excitation and geometrically matched local encoding of curved slices does not require long duration or computationally expensive radiofrequency-pulses. As each encoding field consists of a superposition of potentially arbitrary (spatially linear or nonlinear) magnetic field components, the resulting field shape can be adapted with high flexibility to the specific region of interest. For extended nonplanar structures, this results in improved relevant volume coverage for fewer excited slices and thus increased efficiency. In addition to the mathematical description for the generation of dedicated encoding fields and data reconstruction, a verification of the ExLoc concept in phantom experiments and examples for in vivo curved single and multislice imaging are presented. |
Databáze: | OpenAIRE |
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