Abstract #4914

Manifold-based Respiratory Phase Estimation Enables Motion and Distortion Correction of Free-Breathing Cardiac Diffusion Tensor MRI

Jaume Coll-Font^1,2,3, Shi Chen¹, Robert Eder¹, Yilin Fan⁴, Qiao Joyce Han¹, Maaike van den Boomen^1,2,3, David Sosnovik^1,2,3, Choukri Mekkaoui^2,3, and Christopher T. Nguyen^1,2,3

¹Cardiology, Massachusetts General Hospital, Charlestown, MA, United States, ²Harvard Medical School, Boston, MA, United States, ³Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ⁴Massachusetts Insitute of Technology, Cambridge, MA, United States

Synopsis

For in-vivo cardiac diffusion-tensor MRI (DT-MRI), respiratory motion and B₀ field inhomogeneities produce misalignment and geometric distortion in diffusion weighted images acquired with conventional single shot echo planar imaging. We propose using Laplacian Eigenmaps (LE), a dimensionality reduction method, to retrospectively estimate the respiratory phase of DWI and facilitate both distortion correction (DisCo) and motion compensation (MoCo). LE-based DisCo and MoCo reduces geometric distortion by 13.2% while producing computationally efficient image alignment. Furthermore, preliminary results indicate the LE-based DisCo and MoCo can be applied with only acquiring a single set of b-value averages.

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