Abstract #4046

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

Zhengnan Huang^1,2, Jonghyun Bae^1,2, Eddy Solomon³, Linda Moy^1,2, Sungheon Gene Kim³, Patricia M. Johnson¹, and Florian Knoll^1,4

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-Nürnberg, 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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