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Found 8 papers, 3 papers with code
IVIM-Morph: Motion-compensated quantitative Intra-voxel Incoherent Motion (IVIM) analysis for functional fetal lung maturity assessment from diffusion-weighted MRI data
IVIM-morph combines two sub-networks, a registration sub-network, and an IVIM model fitting sub-network, enabling simultaneous estimation of IVIM model parameters and motion.
qDWI-Morph: Motion-compensated quantitative Diffusion-Weighted MRI analysis for fetal lung maturity assessment
Our approach couples a registration sub-network with a quantitative DWI model fitting sub-network.
SUPER-IVIM-DC: Intra-voxel incoherent motion based Fetal lung maturity assessment from limited DWI data using supervised learning coupled with data-consistency
SUPER-IVIM-DC has the potential to reduce the long acquisition times associated with IVIM analysis of DWI data and to provide clinically feasible bio-markers for non-invasive fetal lung maturity assessment.
CORPS: Cost-free Rigorous Pseudo-labeling based on Similarity-ranking for Brain MRI Segmentation
With this motivation, we propose CORPS, a semi-supervised segmentation framework built upon a novel atlas-based pseudo-labeling method and a 3D deep convolutional neural network (DCNN) for 3D brain MRI segmentation.
Learning the Regularization in DCE-MR Image Reconstruction for Functional Imaging of Kidneys
At the same time, the proposed approach reduces the artifacts in the reconstructed images.
ODE-based Deep Network for MRI Reconstruction
Our results with undersampled data demonstrate that our method can deliver higher quality images in comparison to the reconstruction methods based on the standard UNet network and Residual network.
Deep Plug-and-Play Prior for Parallel MRI Reconstruction
Our proposed reconstruction enables an increase in acceleration factor, and a reduction in acquisition time while maintaining high image quality.
Non-Learning based Deep Parallel MRI Reconstruction (NLDpMRI)
For different MRI scanner configurations using these approaches, the network must be trained from scratch every time with new training dataset, acquired under new configurations, to be able to provide good reconstruction performance.
