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Abstract #3373

Parallel Imaging and Convolutional Neural Network Combined Fast Image Reconstruction for Low Latency Accelerated 2D Real-Time Imaging

Ziwu Zhou1, Fei Han1, Vahid Ghodrati1, Yu Gao1, Yingli Yang2, and Peng Hu1

1Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States, 2Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States

Real-time imaging is a powerful technique to exam multiple physiological motions are the same time. Previous literature has described methods to accelerate the real-time imaging acquisition down to 20ms with the help of compressed sensing. However, reconstruction time remains relatively long, preventing its wide clinical use. Recent developments in deep learning have shown great potential in reconstructing high-quality MR images with low-latency reconstruction. In this work, we proposed a framework that combines the parallel imaging, which is a unique feature in MR imaging, with convolution neural network to reconstruct 2D real-time images with low-latency and high-quality.

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