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Array compression for MRI with large coil arrays
Author(s) -
Buehrer Martin,
Pruessmann Klaas P.,
Boesiger Peter,
Kozerke Sebastian
Publication year - 2007
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.21237
Subject(s) - electromagnetic coil , computer science , phased array , compression (physics) , region of interest , set (abstract data type) , noise (video) , compressed sensing , signal compression , compression ratio , algorithm , artificial intelligence , image (mathematics) , image processing , physics , telecommunications , quantum mechanics , internal combustion engine , antenna (radio) , thermodynamics , programming language
Arrays with large numbers of independent coil elements are becoming increasingly available as they provide increased signal‐to‐noise ratios (SNRs) and improved parallel imaging performance. Processing of data from a large set of independent receive channels is, however, associated with an increased memory and computational load in reconstruction. This work addresses this problem by introducing coil array compression. The method allows one to reduce the number of datasets from independent channels by combining all or partial sets in the time domain prior to image reconstruction. It is demonstrated that array compression can be very effective depending on the size of the region of interest (ROI). Based on 2D in vivo data obtained with a 32‐element phased‐array coil in the heart, it is shown that the number of channels can be compressed to as few as four with only 0.3% SNR loss in an ROI encompassing the heart. With twofold parallel imaging, only a 2% loss in SNR occurred using the same compression factor. Magn Reson Med 57:1131–1139, 2007. © 2007 Wiley‐Liss, Inc.