2 citations · 2 across the 1 of their papers we have counts for
4 papers
Noisy MRI Reconstruction via MAP Estimation with an Implicit Deep-Denoiser Prior
Nikola Janjušević, Amirhossein Khalilian-Gourtani, Yao Wang +1
Accelerating magnetic resonance imaging (MRI) remains challenging, particularly under realistic acquisition noise. While diffusion models have recently shown promise for reconstruc…
Hybrid Learning: A Novel Combination of Self-Supervised and Supervised Learning for Joint MRI Reconstruction and Denoising in Low-Field MRI
Haoyang Pei, Nikola Janjuvsevic, Renqing Luo +6
Deep learning has demonstrated strong potential for MRI reconstruction. However, conventional supervised learning requires high-quality, high-SNR references for network training, w…
Self-Supervised Noise Adaptive MRI Denoising via Repetition to Repetition (Rep2Rep) Learning
Nikola Janjušević, Jingjia Chen, Luke Ginocchio +5
Purpose: This work proposes a novel self-supervised noise-adaptive image denoising framework, called Repetition to Repetition (Rep2Rep) learning, for low-field (<1T) MRI applicatio…
Learned Primal Dual Splitting for Self-Supervised Noise-Adaptive MRI Reconstruction
Nikola Janjusevic, Amirhoussein Khalilian-Gourtani, Yao Wang +1
Magnetic resonance imaging (MRI) reconstruction has largely been dominated by deep neural networks (DNN); however, many state-of-the-art architectures use black-box structures, whi…