Abstract #4926
ECG-free 2D cardiac cine MRI with data-driven clustering
Zhengyang Ming1,2, Caroline M. Colbert1,2,3, Ruan Dan1,4, Holden H. Wu1,2, Anthony G. Christodoulou5, J. Paul Finn1,2, Peng Hu1,2, and Kim-Lien Nguyen1,2,3
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 Radiation Oncology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States, 5Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
Synopsis
Conventional 2D cardiac cine imaging relies on ECG-gating whereas self-gated approaches mitigate ECG-dependency by assuming that cardiac motion is periodic with well-defined frequencies. This assumption breaks down when patients have irregular cardiac rhythm. We propose a segmented Cartesian golden step balanced steady-state free precession sequence (bSSFP) with motion navigators and a clustering algorithm to alleviate the dependency on regular motion assumptions and to emphasize the intrinsic similarity of mechanical motion. Compared to standard ECG-gated 2D bSSFP cine performed in normal sinus rhythm, initial validation using our approach achieved similar image quality and quantitative metrics for cardiac function.
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