2 citations · 2 across the 4 of their papers we have counts for
4 papers
Training-Free Adversarial Robustness in Computational MRI
Mahdi Saberi, Chi Zhang, Mehmet Akçakaya
Deep learning (DL) methods have become the state-of-the-art for reconstructing sub-sampled magnetic resonance imaging (MRI) data. However, studies have shown that these methods are…
On the Robustness of deep learning-based MRI Reconstruction to image transformations
Jinghan Jia, Mingyi Hong, Yimeng Zhang +2
Although deep learning (DL) has received much attention in accelerated magnetic resonance imaging (MRI), recent studies show that tiny input perturbations may lead to instabilities…
20-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstruction
Omer Burak Demirel, Burhaneddin Yaman, Logan Dowdle +7
High spatial and temporal resolution across the whole brain is essential to accurately resolve neural activities in fMRI. Therefore, accelerated imaging techniques target improved…
Improved Simultaneous Multi-Slice Functional MRI Using Self-supervised Deep Learning
Omer Burak Demirel, Burhaneddin Yaman, Logan Dowdle +7
Functional MRI (fMRI) is commonly used for interpreting neural activities across the brain. Numerous accelerated fMRI techniques aim to provide improved spatiotemporal resolutions.…