Xin Chen1, Muhammad Usman1, Christian Baumgartner2, Claudia Prieto1, and Andrew King1
1Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom, 2Biomedical Image Analysis Group, Imperial College, London, United Kingdom
We present a novel method based on manifold alignment, which
enables reconstruction of motion-free abdominal images throughout the
respiratory cycle to better capture respiratory intra- and inter-cycle
variations. The proposed method was evaluated on both simulated and in-vivo 2D
acquisitions. Based on virtual navigator measurement, the reconstructed dynamic
sequence achieved Pearson correlation coefficient of 0.9504 with the ground
truth of the simulated dataset. The proposed method enables much richer profile
data to be used for self-gating, resulting in less blurring when compared to
conventional central k-space self-gating method for the in-vivo acquisition.
