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Authors: Giuseppe Placidi 1 ; Luigi Cinque 2 ; Andrea Petracca 1 ; Matteo Polsinelli 1 and Matteo Spezialetti 1

Affiliations: 1 University of L'Aquila, Italy ; 2 Sapienza University, Italy

ISBN: 978-989-758-222-6

ISSN: 2184-4313

Keyword(s): Adaptive Acquisition Method, Sparse Sampling, Compressed Sensing, Undersampling, Sparsity, Reconstruction, Radial Directions, Projections, Non-linear Reconstruction.

Related Ontology Subjects/Areas/Topics: Applications ; Cardiovascular Imaging and Cardiography ; Cardiovascular Technologies ; Computer Vision, Visualization and Computer Graphics ; Health Engineering and Technology Applications ; Image Understanding ; Medical Imaging ; Pattern Recognition ; Signal Processing ; Software Engineering

Abstract: Magnetic Resonance Imaging (MRI) represents a major imaging modality for its low invasiveness and for its property to be used in real-time and functional applications. The acquisition of radial directions is often used but a complete examination always requires long acquisition times. The only way to reduce acquisition time is undersampling. We present an iterative adaptive acquisition method (AAM) for radial sampling/reconstruction MRI that uses the information collected during the sequential acquisition process on the inherent structure of the underlying image for calculating the following most informative directions. A full description of AAM is furnished and some experimental results are reported; a comparison between AAM and weighted compressed sensing (CS) strategy is performed on numerical data. The results demonstrate that AAM converges faster than CS and that it has a good termination criterion for the acquisition process.

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Paper citation in several formats:
Placidi, G.; Cinque, L.; Petracca, A.; Polsinelli, M. and Spezialetti, M. (2017). Iterative Adaptive Sparse Sampling Method for Magnetic Resonance Imaging.In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, ISSN 2184-4313, pages 510-518. DOI: 10.5220/0006199105100518

@conference{icpram17,
author={Giuseppe Placidi. and Luigi Cinque. and Andrea Petracca. and Matteo Polsinelli. and Matteo Spezialetti.},
title={Iterative Adaptive Sparse Sampling Method for Magnetic Resonance Imaging},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={510-518},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006199105100518},
isbn={978-989-758-222-6},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Iterative Adaptive Sparse Sampling Method for Magnetic Resonance Imaging
SN - 978-989-758-222-6
AU - Placidi, G.
AU - Cinque, L.
AU - Petracca, A.
AU - Polsinelli, M.
AU - Spezialetti, M.
PY - 2017
SP - 510
EP - 518
DO - 10.5220/0006199105100518

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