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Abstract #4765

Non-iterative model for synthetic image-based registration of MOLLI cardiac T1 mapping images

Laura Claire Saunders1, Neil Stewart1, David Kiely2, Martin Graves3, Andy Swift1, and Jim Wild1

1Academic Radiology, University of Sheffield, Sheffield, United Kingdom, 2Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Trust, 3University of Cambridge

Cardiac T1 maps rely upon the acquisition of spatially aligned images. When patients fail to maintain breath hold, respiratory motion can cause T1 map inaccuracies due to poor image alignment. In the method demonstrated here, images are registered by co-registration to synthetic images, which are created via a non-iterative, automatic, model-based method. This method is compared to an iterative registration method using an energy minimisation process for quantitative registration accuracy and speed. Both methods were found to significantly improve image registration. The resultant registrations from both methods did not significantly differ, however the non-iterative model-based method reduced processing time by 1/5th.

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