Premium
Self‐calibrated correlation imaging with k‐space variant correlation functions
Author(s) -
Li Yu,
Edalati Masoud,
Du Xingfu,
Wang Hui,
Cao Jie J.
Publication year - 2018
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.26818
Subject(s) - correlation , k space , magnetic resonance imaging , sensitivity (control systems) , iterative reconstruction , computer science , physics , artificial intelligence , medical imaging , nuclear magnetic resonance , fourier transform , computer vision , pattern recognition (psychology) , mathematics , mathematical analysis , geometry , medicine , electronic engineering , engineering , radiology
Purpose Correlation imaging is a previously developed high‐speed MRI framework that converts parallel imaging reconstruction into the estimate of correlation functions. The presented work aims to demonstrate this framework can provide a speed gain over parallel imaging by estimating k‐space variant correlation functions. Methods Because of Fourier encoding with gradients, outer k‐space data contain higher spatial‐frequency image components arising primarily from tissue boundaries. As a result of tissue‐boundary sparsity in the human anatomy, neighboring k‐space data correlation varies from the central to the outer k‐space. By estimating k‐space variant correlation functions with an iterative self‐calibration method, correlation imaging can benefit from neighboring k‐space data correlation associated with both coil sensitivity encoding and tissue‐boundary sparsity, thereby providing a speed gain over parallel imaging that relies only on coil sensitivity encoding. This new approach is investigated in brain imaging and free‐breathing neonatal cardiac imaging. Results Correlation imaging performs better than existing parallel imaging techniques in simulated brain imaging acceleration experiments. The higher speed enables real‐time data acquisition for neonatal cardiac imaging in which physiological motion is fast and non‐periodic. Conclusion With k‐space variant correlation functions, correlation imaging gives a higher speed than parallel imaging and offers the potential to image physiological motion in real‐time. Magn Reson Med 79:1483–1494, 2018. © 2017 International Society for Magnetic Resonance in Medicine.