9 citations · 36 across the 11 of their papers we have counts for
11 papers
SMRD: SURE-based Robust MRI Reconstruction with Diffusion Models
Batu Ozturkler, Chao Liu, Benjamin Eckart +3
Diffusion models have recently gained popularity for accelerated MRI reconstruction due to their high sample quality. They can effectively serve as rich data priors while incorpora…
Coil Sketching for computationally-efficient MR iterative reconstruction
Julio A. Oscanoa, Frank Ong, Siddharth S. Iyer +6
Purpose: Parallel imaging and compressed sensing reconstructions of large MRI datasets often have a prohibitive computational cost that bottlenecks clinical deployment, especially…
Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks
Dave Van Veen, Rogier van der Sluijs, Batu Ozturkler +9
We propose using a coordinate network decoder for the task of super-resolution in MRI. The continuous signal representation of coordinate networks enables this approach to be scale…
ThinkSum: Probabilistic reasoning over sets using large language models
Batu Ozturkler, Nikolay Malkin, Zhen Wang +1
Large language models (LLMs) have a substantial capacity for high-level analogical reasoning: reproducing patterns in linear text that occur in their training data (zero-shot evalu…
GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction
Batu Ozturkler, Arda Sahiner, Tolga Ergen +6
Unrolled neural networks have recently achieved state-of-the-art accelerated MRI reconstruction. These networks unroll iterative optimization algorithms by alternating between phys…
Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers
Arda Sahiner, Tolga Ergen, Batu Ozturkler +3
Vision transformers using self-attention or its proposed alternatives have demonstrated promising results in many image related tasks. However, the underpinning inductive bias of a…
VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstruction
Arjun D Desai, Beliz Gunel, Batu M Ozturkler +6
Deep neural networks have enabled improved image quality and fast inference times for various inverse problems, including accelerated magnetic resonance imaging (MRI) reconstructio…
Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learning
Arjun D Desai, Batu M Ozturkler, Christopher M Sandino +7
Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction. However, supervised DL methods depend on extensive amounts of fully-sampled (labeled)…
Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions
Arda Sahiner, Tolga Ergen, Batu Ozturkler +4
Generative Adversarial Networks (GANs) are commonly used for modeling complex distributions of data. Both the generators and discriminators of GANs are often modeled by neural netw…
Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularization
Tolga Ergen, Arda Sahiner, Batu Ozturkler +3
Batch Normalization (BN) is a commonly used technique to accelerate and stabilize training of deep neural networks. Despite its empirical success, a full theoretical understanding…
Convex Regularization Behind Neural Reconstruction
Arda Sahiner, Morteza Mardani, Batu Ozturkler +2
Neural networks have shown tremendous potential for reconstructing high-resolution images in inverse problems. The non-convex and opaque nature of neural networks, however, hinders…