Abstract #4754

Semi-Automatic Ejection Fraction Calculation from Cardiac Low-Rank Tensor Images Based on Unsupervised Machine Learning

Zihao He^1,2, Anthony G. Christodoulou^1,3, Hua Guo², and Debiao Li^1,4

¹Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ²Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People's Republic of China, ³Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ⁴Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States

Calculation of the ejection fraction from cardiac cine MR images requires segmenting multiple images of the left ventricle. This process, which is often performed manually, is time-consuming and observer-dependent. In this work, an unsupervised machine learning algorithm, combining hidden Markov random field and optical flow, has been proposed to perform semi-automatic tissue segmentation on T1/T2-weighted low-rank tensor images that have a built-in feature space due to low-rank factorization performed during image reconstruction. The segmentation results then allow automatic EF calculation. Demonstrated results have higher efficiency and similar accuracy compared with manual segmentation, and were stable with respect to different initializations.

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