602 citations · 816 across the 24 of their papers we have counts for
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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…
Progressively Volumetrized Deep Generative Models for Data-Efficient Contextual Learning of MR Image Recovery
Mahmut Yurt, Muzaffer Özbey, Salman Ul Hassan Dar +3
Magnetic resonance imaging (MRI) offers the flexibility to image a given anatomic volume under a multitude of tissue contrasts. Yet, scan time considerations put stringent limits o…
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…
A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networks
Salman Ul Hassan Dar, Muzaffer Özbey, Ahmet Burak Çatlı +1
Purpose: Neural networks have received recent interest for reconstruction of undersampled MR acquisitions. Ideally network performance should be optimized by drawing the training a…