5 papers
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…
No Alignment Needed for Generation: Learning Linearly Separable Representations in Diffusion Models
Junno Yun, Yaşar Utku Alçalar, Mehmet Akçakaya
Efficient training strategies for large-scale diffusion models have recently emphasized the importance of improving discriminative feature representations in these models. A centra…
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…
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…