Jana Hutter1,2, Robert Grimm3, Christoph Forman3,4, Joachim Hornegger3,4, Peter Schmitt5. 1. Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nuremberg, Martensstr 3, 91058, Erlangen, Germany. jana.hutter@cs.fau.de. 2. School of Advanced Optical Technologies, Erlangen, Germany. jana.hutter@cs.fau.de. 3. Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nuremberg, Martensstr 3, 91058, Erlangen, Germany. 4. School of Advanced Optical Technologies, Erlangen, Germany. 5. MR Applications and Workflow Development, Healthcare Sector, Siemens AG, Erlangen, Germany.
Abstract
OBJECT: The aim of this study was to investigate the acceleration of peripheral Time-of-Flight magnetic resonance angiography using Compressed Sensing and parallel magnetic resonance imaging (MRI) while preserving image quality and vascular contrast. MATERIALS AND METHODS: An analytical sampling pattern is proposed that combines aspects of parallel MRI and Compressed Sensing. It is used in combination with a dedicated Split Bregman algorithm. This approach is compared with current state-of-the-art patterns and reconstruction algorithms. RESULTS: The acquisition time was reduced from 30 to 2.5 min in a study using ten volunteer data sets, while showing improved sharpness, better contrast and higher accuracy compared to state-of-the-art techniques. CONCLUSION: This study showed the benefits of the proposed dedicated analytical sampling pattern and Split Bregman algorithm for optimizing the Compressed Sensing reconstruction of highly accelerated peripheral Time-of-Flight data.
OBJECT: The aim of this study was to investigate the acceleration of peripheral Time-of-Flight magnetic resonance angiography using Compressed Sensing and parallel magnetic resonance imaging (MRI) while preserving image quality and vascular contrast. MATERIALS AND METHODS: An analytical sampling pattern is proposed that combines aspects of parallel MRI and Compressed Sensing. It is used in combination with a dedicated Split Bregman algorithm. This approach is compared with current state-of-the-art patterns and reconstruction algorithms. RESULTS: The acquisition time was reduced from 30 to 2.5 min in a study using ten volunteer data sets, while showing improved sharpness, better contrast and higher accuracy compared to state-of-the-art techniques. CONCLUSION: This study showed the benefits of the proposed dedicated analytical sampling pattern and Split Bregman algorithm for optimizing the Compressed Sensing reconstruction of highly accelerated peripheral Time-of-Flight data.
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