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Abstract #4481

Motion Correction with a Model Target (MoCoMo): A universal approach for quantitative MRI?

Fotios Tagkalakis1, Kanishka Sharma1, Susmita Basak1, Christopher Kelly2, David Shelley3, Irvin Teh2, Jehill Parikh4, Peter Thelwall4, Neil Sheerin5, and Steven Sourbron1

1Leeds Imaging Biomarkers Group, Biomedical Imaging Science Department, University of Leeds, Leeds, United Kingdom, 2Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom, 3Advanced Imaging Centre, University of Leeds, Leeds, United Kingdom, 4Newcastle Magnetic Resonance Centre, Newcastle University, Newcastle, United Kingdom, 5Institute of Cellular Medicine, Newcastle University, Newcastle, United Kingdom

Motion correction with a model-target (MoCoMo) has been used in DCE-MRI to overcome the problem of changes in image contrast, but the method applies in principle to any other quantitative MRI method. The aim of this study is to demonstrate this hypothesis by applying the algorithm to renal DCE, DTI, T1 and T2-mapping in human subjects. The results show that MoCoMo is effective in removing even major motion effects in all 4 modalities and does not affect data where no motion is present. We conclude that MoCoMo is a suitable candidate for universal motion correction across all functional MRI modalities.

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