10 citations · 15 across the 3 of their papers we have counts for
5 papers
A Few-Shot Learning Approach for Accelerated MRI via Fusion of Data-Driven and Subject-Driven Priors
Salman Ul Hassan Dar, Mahmut Yurt, Tolga Çukur
Deep neural networks (DNNs) have recently found emerging use in accelerated MRI reconstruction. DNNs typically learn data-driven priors from large datasets constituting pairs of un…
Three Dimensional MR Image Synthesis with Progressive Generative Adversarial Networks
Muzaffer Özbey, Mahmut Yurt, Salman Ul Hassan Dar +1
Mainstream deep models for three-dimensional MRI synthesis are either cross-sectional or volumetric depending on the input. Cross-sectional models can decrease the model complexity…
mustGAN: Multi-Stream Generative Adversarial Networks for MR Image Synthesis
Mahmut Yurt, Salman Ul Hassan Dar, Aykut Erdem +2
Multi-contrast MRI protocols increase the level of morphological information available for diagnosis. Yet, the number and quality of contrasts is limited in practice by various fac…
Synergistic Reconstruction and Synthesis via Generative Adversarial Networks for Accelerated Multi-Contrast MRI
Salman Ul Hassan Dar, Mahmut Yurt, Mohammad Shahdloo +2
Multi-contrast MRI acquisitions of an anatomy enrich the magnitude of information available for diagnosis. Yet, excessive scan times associated with additional contrasts may be a l…
Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks
Salman Ul Hassan Dar, Mahmut Yurt, Levent Karacan +3
Acquiring images of the same anatomy with multiple different contrasts increases the diversity of diagnostic information available in an MR exam. Yet, scan time limitations may pro…