Abstract #3893

Joint Estimation of Coil Sensitivity and Image by Using Untrained Neural Network without External Training Data

Gulfam Ahmed Saju¹, Zhiqiang Li², Reza Abiri³, Tianming Liu⁴, and Yuchou Chang¹

¹Computer and Information Science, University of Massachusetts Dartmouth, North Dartmouth, MA, United States, ²Neuroradiology, Barrow Neurological Institute, Phoenix, AZ, United States, ³Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI, United States, ⁴Computer Science, University of Georgia, Athens, GA, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Parallel ImagingTraining data for MRI reconstruction are difficult to be acquired in clinical practice. In addition, machine learning or deep learning-based MRI reconstruction suffers the distribution shift problem between training data and testing data. Generalization error always exists, so reconstructed images are unstable. We proposed a joint estimation of coil sensitivity and image using the prior of an untrained neural network (UNN). Coil sensitivity map improvement gradually enhances the UNN prior and the image to be reconstructed in an iterative optimization process. The method outperforms other MRI reconstruction methods by suppressing noise and aliasing artifacts.

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