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Abstract #4046

1D Convolutional Neural Network as Regularizer for Learning DCE-MRI Reconstruction

Zhengnan Huang1,2, Jonghyun Bae1,2, Eddy Solomon3, Linda Moy1,2, Sungheon Gene Kim3, Patricia M. Johnson1, and Florian Knoll1,4
1Center for Biomedical Imaging, Radiology, New York University School of Medicine, New York, NY, United States, 2Vilcek Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY, United States, 3Department of Radiology, Weill Cornell Medical College, New York, NY, United States, 4Friedrich-Alexander-University of Erlangen-N├╝rnberg, Erlangen, Germany

Synopsis

We proposed to use a variational network (VN) reconstruction algorithm with a 1-dimensional convolutional neural network (CNN) as a temporal regularizer for DCE-MRI reconstruction in this study. We used our newly developed breast perfusion simulation pipeline, to generate simulate data and train the reconstruction model. The machine learning (ML) reconstruction shows non-inferior structural similarity and improved visual image quality when compared with the iGRASP reconstruction. The ML reconstruction also takes much less time than the iGRASP reconstruction.

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