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Consistency Models for Fast MRI Reconstruction Using Regularization by Denoising
Merve Gülle, Junno Yun, Yaşar Utku Alçalar +1
Diffusion models (DMs) have emerged as powerful generative priors for MRI reconstruction with promising results. Yet DM-based methods require extensive iterative refinement, limiti…
Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors
Mahdi Saberi, Yaşar Utku Alçalar, Merve Gülle +2
MRI reconstruction methods for undersampled k-space data naturally utilize complex-valued measurements. Parallel developments in sparse phase retrieval have shown that magnitude-on…
Time-Embedded Algorithm Unrolling for Computational MRI
Junno Yun, Yaşar Utku Alçalar, Mehmet Akçakaya
Algorithm unrolling methods have proven powerful for solving the regularized least squares problem in computational magnetic resonance imaging (MRI). These approaches unfold an ite…
Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining
Yaşar Utku Alçalar, Junno Yun, Mehmet Akçakaya
Diffusion/score-based models have recently emerged as powerful generative priors for solving inverse problems, including accelerated MRI reconstruction. While their flexibility all…
PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problems
Merve Gülle, Junno Yun, Yaşar Utku Alçalar +1
Diffusion models have found extensive use in solving inverse problems, by sampling from an approximate posterior distribution of data given the measurements. Recently, consistency…
Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study
Yaşar Utku Alçalar, Yu Cao, Mehmet Akçakaya
Physics-driven artificial intelligence (PD-AI) reconstruction methods have emerged as the state-of-the-art for accelerating MRI scans, enabling higher spatial and temporal resoluti…
Sparsity-Driven Parallel Imaging Consistency for Improved Self-Supervised MRI Reconstruction
Yaşar Utku Alçalar, Mehmet Akçakaya
Physics-driven deep learning (PD-DL) models have proven to be a powerful approach for improved reconstruction of rapid MRI scans. In order to train these models in scenarios where…
Zero-Shot Adaptation for Approximate Posterior Sampling of Diffusion Models in Inverse Problems
Yaşar Utku Alçalar, Mehmet Akçakaya
Diffusion models have emerged as powerful generative techniques for solving inverse problems. Despite their success in a variety of inverse problems in imaging, these models requir…