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Zero-shot Learning for Unsupervised Reconstruction of Accelerated MRI Acquisitions
Yilmaz Korkmaz1,2, Salman Ul Hassan Dar1,2, Mahmut Yurt1,2, and Tolga Çukur1,2,3
1Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey, 2National Magnetic Resonance Research Center, Bilkent University, Ankara, Turkey, 3Neuroscience Program, Aysel Sabuncu Brain Research Center, Bilkent University, Ankara, Turkey
We propose a zero-shot learning approach for unsupervised reconstruction of accelerated MRI  without any prior information about reconstruction task. Our approach efficiently recovers undersampled acquisitions, irrespective of the contrast, acceleration rate or undersampling pattern.
Figure 1: (a) Pretraining of the style-generative model. A fully connected mapper to generate intermediate latent vectors w, a synthesizer to generate images, a discriminator for adversarial training and noise n. w and n are defined for each synthesizer block separately, where block cover resolution from 4x4 to 256x256 pixels. (b) Testing phase of ZSL-Net. w* and n* correspond to optimized latent vector and noise components for the synthesizer. Optimization is performed to minimize partial k-space loss between masked Fourier coefficients of reconstructed and undersampled images.
Figure 2: Demonstrations of the proposed and competing methods on IXI for T1-contrast image reconstruction when acceleration rate R is 8. Reconstructed images are shown along with the error maps which are absolute differences between reconstructed and reference images. Error map corresponds to ZSL-Net, appears to be darker compared to the competing methods and most of the error concentrated on skull.