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Found 11 papers, 6 papers with code
Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series
The placenta plays a crucial role in fetal development.
Zero-Shot Self-Supervised Joint Temporal Image and Sensitivity Map Reconstruction via Linear Latent Space
We perform experiments on simulated and retrospective in-vivo data to evaluate the performance of the proposed zero-shot learning method for temporal FSE reconstruction.
Latent Signal Models: Learning Compact Representations of Signal Evolution for Improved Time-Resolved, Multi-contrast MRI
Purpose: Training auto-encoders on simulated signal evolution and inserting the decoder into the forward model improves reconstructions through more compact, Bloch-equation-based representations of signal in comparison to linear subspaces.
SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI
Experiments with real-world MRI data are also performed to demonstrate the ability of the proposed model to improve the quality of 3D reconstruction under severe fetal motion.
STRESS: Super-Resolution for Dynamic Fetal MRI using Self-Supervised Learning
Fetal motion is unpredictable and rapid on the scale of conventional MR scan times.
Deformed2Self: Self-Supervised Denoising for Dynamic Medical Imaging
Further, it only requires a single noisy image with a few auxiliary observations at different time frames for training and inference.
Multi-scale Neural ODEs for 3D Medical Image Registration
The inference consists of iterative gradient updates similar to a conventional gradient descent optimizer but in a much faster way, because the neural ODE learns from the training data to adapt the gradient efficiently at each iteration.
Enhanced detection of fetal pose in 3D MRI by Deep Reinforcement Learning with physical structure priors on anatomy
The proposed DRL for fetal pose landmark search demonstrates a potential clinical utility for online detection of fetal motion that guides real-time mitigation of motion artifacts as well as health diagnosis during MRI of the pregnant mother.
Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency
The proposed method is also implemented and evaluated on an MR scanner, which demonstrates the feasibility of online image quality assessment and image reacquisition during fetal MR scans.
Fetal Pose Estimation in Volumetric MRI using a 3D Convolution Neural Network
The performance and diagnostic utility of magnetic resonance imaging (MRI) in pregnancy is fundamentally constrained by fetal motion.
200x Low-dose PET Reconstruction using Deep Learning
Experiments shows the proposed method can reconstruct low-dose PET image to a standard-dose quality with only two-hundredth dose.
