RESUMO
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.
