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Abstract #2288

Variable density Poisson disc acquisition with iterative deep learning reconstruction for highly accelerated 3D T1-weighted abdominal imaging

Ty A Cashen1, Sangtae Ahn2, Uri Wollner3, Graeme McKinnon4, Isabelle Heukensfeldt Jansen2, Rafi Brada3, Dan Rettmann5, Xucheng Zhu6, and Ersin Bayram7
1GE Healthcare, Madison, WI, United States, 2GE Research, Niskayuna, NY, United States, 3GE Research, Herzliya, Israel, 4GE Healthcare, Waukesha, WI, United States, 5GE Healthcare, Rochester, MN, United States, 6GE Healthcare, Menlo Park, CA, United States, 7GE Healthcare, Houston, TX, United States


3D T1-weighted gradient echo imaging is a key component of the MRI assessment of the abdomen, particularly for the identification and characterization of liver tumors, however, significant acceleration is necessary to consistently mitigate respiratory motion artifact. A variable density Poisson disc undersampled acquisition with a densely connected iterative deep convolutional neural network reconstruction was developed to provide next-generation acceleration up to a factor of 10. On retrospectively undersampled data, the technique outperformed compressed sensing reconstruction in terms of normalized mean-squared error and structural similarity; with a prospectively undersampled scan, the technique maintained image quality in terms of artifact and contrast.

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