Methods for Free-Breathing Dynamic Contrast-Enhanced MRI
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Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is a valuable diagnostic tool due to the combination of anatomical and physiological information it provides. However, the sequential sampling of MRI presents an inherent tradeoff between spatial and temporal resolution.
We present several model-based image reconstruction methods to improve the spatial and temporal resolution of MR images and/or the computational time for multi-coil MRI reconstruction. This work includes efficient variable splitting methods for support-constrained MRI reconstruction, image reconstruction and denoising with non-circulant boundary conditions, and improved temporal regularization for breast DCE-MRI.
We also present a method for indirect motion compensation in 5D liver DCE-MRI. This work applies a pre-computed motion model to perform motion compensated regularization across the respiratory dimension to improve the conditioning of this highly sparse 5D reconstruction problem. We demonstrate a proof of concept using a digital phantom with contrast and respiratory changes, and we show preliminary results for the proposed model on real patient data.