@conference {663,
	title = {Cardio-respiratory motion estimation for compressed sensing reconstruction of free-breathing 2D cine MRI},
	booktitle = {Modulated/Incomplete Data 2016, SFB Workshop.},
	year = {2016},
	month = {2016},
	publisher = {Mathematical Optimization and Applications in Biomedical Sciences (MOBIS), SFB Research Center},
	organization = {Mathematical Optimization and Applications in Biomedical Sciences (MOBIS), SFB Research Center},
	address = {Graz, Austria},
	abstract = {<p>Respiratory motion is still an issue in MRI of the heart despite the introduction of Compressed Sensing (CS) techniques, which significantly accelerate acquisition [1]. Recently [2], a double-binning scheme was introduced in which k-space data is split according both to the cardiac and respiratory phases (Fig. 1); at reconstruction, sparsity along both dimensions is exploited. Other methods introduce motion estimation and compensation in CS (MC-CS) either to correct the respiratory motion [3] or to promote sparsity for reconstruction improvement [4]. In this work, we propose a technique to jointly estimate the respiratory and cardiac motions within a double-binning scheme, enabling the MC-CS reconstruction of respiratory resolved free-breathing 2D CINE MRI. Preliminary results on synthetic, highly undersampled (x16) Cartesian setup are shown.</p>

<p>[1] Lustig et al. MRM 2007, [2] Feng et al. MRM 2015, [3] Usman et al. MRM 2013. [4]\&nbsp;Royuela-del-Val et al. MRM 2015.</p>
},
	author = {J Royuela-del-Val and Marcos Mart{\'\i}n-Fern{\'a}ndez and Federico Simmross-Wattenberg and Carlos Alberola-Lopez}
}
