Abstract #5123

Data-driven clustering for ECG-free cine MRI with robustness against irregular cardiac motion

Zhengyang Ming^1,2, Arutyun Pogosyan³, Anthony G. Christodoulou^4,5, J. Paul Finn^1,2, Dan Ruan^1,4,6, and Kim-Lien Nguyen^1,2,3,4

¹Physics and Biology in Medicine Graduate Program, University of California,Los Angeles, Los Angeles, CA, United States, ²Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States, ³Division of Cardiology, David Geffen School of Medicine at UCLA and VA Greater Los Angeles Healthcare System, Los Angeles, CA, United States, ⁴Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, ⁵Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ⁶Department of Radiation Oncology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States

Synopsis

Keywords: Arrhythmia, Machine Learning/Artificial Intelligence

Both ECG-gating and self-gated approaches are used for cardiac motion binning in segmented cardiac cine MRI. Typical self-gated methods reduce ECG-dependency by assuming periodic cardiac motion, but may be less reliable in the presence of irregular cardiac motion. We propose a novel clustering algorithm that incorporates regularization in both temporal and cluster dimension to provide robustness against irregular cardiac motion in complex arrhythmias such as atrial fibrillation and premature ventricular contraction. Compared with images from k-means clustering, initial validation using the modified algorithm shows higher image quality scores with comparable single-to-noise ratio (SNR) and image sharpness.

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