241 citations · 295 across the 5 of their papers we have counts for
5 papers
A Tutorial on MRI Reconstruction: From Modern Methods to Clinical Implications
Tolga Çukur, Salman U. H. Dar, Valiyeh Ansarian Nezhad +4
MRI is an indispensable clinical tool, offering a rich variety of tissue contrasts to support broad diagnostic and research applications. Clinical exams routinely acquire multiple…
One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis
Onat Dalmaz, Usama Mirza, Gökberk Elmas +5
Multi-institutional collaborations are key for learning generalizable MRI synthesis models that translate source- onto target-contrast images. To facilitate collaboration, federate…
Unsupervised Medical Image Translation with Adversarial Diffusion Models
Muzaffer Özbey, Onat Dalmaz, Salman UH Dar +4
Imputation of missing images via source-to-target modality translation can improve diversity in medical imaging protocols. A pervasive approach for synthesizing target images invol…
Adaptive Diffusion Priors for Accelerated MRI Reconstruction
Alper Güngör, Salman UH Dar, Şaban Öztürk +4
Deep MRI reconstruction is commonly performed with conditional models that de-alias undersampled acquisitions to recover images consistent with fully-sampled data. Since conditiona…
Federated Learning of Generative Image Priors for MRI Reconstruction
Gokberk Elmas, Salman UH Dar, Yilmaz Korkmaz +5
Multi-institutional efforts can facilitate training of deep MRI reconstruction models, albeit privacy risks arise during cross-site sharing of imaging data. Federated learning (FL)…