Guanxiong Luo1, Xiaoqing Wang1, Volkert Roeloffs1, Zhengguo Tan1, and Martin Uecker1,2
1Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Germany, Göttingen, Germany, 2Campus Institute Data Science (CIDAS), University of Göttingen, Germany, Göttingen, Germany
1Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Germany, Göttingen, Germany, 2Campus Institute Data Science (CIDAS), University of Göttingen, Germany, Göttingen, Germany
The nonlinear inversion reconstruction is a calibrationless parallel imaging technique, which jointly estimate coil sensitivities and image content. We demonstrate how to combine such a calibrationless technique with an advanced neural network based image prior for efficient MR imaging.
Figure 1. For the case of moderate undersampling, the two reconstructions regularized by log-likelihood and $$$\ell_1$$$ are very close, and the structural similarity index between them is 0.95. The $$$\ell_1$$$ reconstruction has blocky artifacts in Region 1 introduced by the wavelet transform, especially for higher undersampling. Overall, the learned log-likelihood outperforms $$$\ell_1$$$ in noise suppression, especially in Region 2. The reconstructions regularized by the learned log-likelihood also better preserve the boundaries between tissues and have less noise.
Figure 2. Reconstructed coil sensitivities (grayscale magnitude and color-coded phase) after channel compression.
