Search Results for author:
Found 10 papers, 4 papers with code
Improving cross-domain brain tissue segmentation in fetal MRI with synthetic data
Segmentation of fetal brain tissue from magnetic resonance imaging (MRI) plays a crucial role in the study of in utero neurodevelopment.
Learning to sample in Cartesian MRI
Altogether, our findings suggest that stochastic LBCS and similar methods represent promising alternatives to deep RL.
FetMRQC: an open-source machine learning framework for multi-centric fetal brain MRI quality control
We present FetMRQC, an open-source machine-learning framework for automated image quality assessment and quality control that is robust to domain shifts induced by the heterogeneity of clinical data.
FetMRQC: Automated Quality Control for fetal brain MRI
Quality control (QC) has long been considered essential to guarantee the reliability of neuroimaging studies.
Simulation-based parameter optimization for fetal brain MRI super-resolution reconstruction
Tuning the regularization hyperparameter $\alpha$ in inverse problems has been a longstanding problem.
On the benefits of deep RL in accelerated MRI sampling
Deep learning approaches have shown great promise in accelerating magnetic resonance imaging (MRI), by reconstructing high quality images from highly undersampled data.
Solving Inverse Problems with Hybrid Deep Image Priors: the challenge of preventing overfitting
When the noise level is small, it does not considerably reduce the overfitting problem.
Uncertainty-Driven Adaptive Sampling via GANs
We propose an adaptive sampling method for the linear model, driven by the uncertainty estimation with a generative adversarial network (GAN) model.
Closed loop deep Bayesian inversion: Uncertainty driven acquisition for fast MRI
This work proposes a closed-loop, uncertainty-driven adaptive sampling frame- work (CLUDAS) for accelerating magnetic resonance imaging (MRI) via deep Bayesian inversion.
Scalable Learning-Based Sampling Optimization for Compressive Dynamic MRI
Compressed sensing applied to magnetic resonance imaging (MRI) allows to reduce the scanning time by enabling images to be reconstructed from highly undersampled data.
