Abstract #3716

Relative noise variation with Unrolled Neural Networks for Accelerated Cardiac Cine Reconstruction

Suryanarayanan Sivaram Kaushik¹, Xucheng Zhu², Robert Marc Lebel³, Kavitha Manickam¹, Ke Li¹, Kailash Saravanan¹, Florian Wiesinger⁴, and Martin Janich⁴

¹GE Healthcare, Waukesha, WI, United States, ²GE Healthcare, Menlo Park, CA, United States, ³GE Healthcare, Calgary, AB, Canada, ⁴GE Healthcare, Munich, Germany

Synopsis

Keywords: Machine Learning/Artificial Intelligence, CardiovascularDeep Learning (DL) based reconstructions help alleviate the longer scan times seen in bSSFP Cine acquisitions by offering higher acceleration factors. This work analyzes the spatial and temporal variations in the noise as a function of acceleration factors in DL reconstructions of highly accelerated bSSFP Cine data.

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