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Found 11 papers, 4 papers with code
The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023
To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on MICCAI.
$k$-$t$ CLAIR: Self-Consistency Guided Multi-Prior Learning for Dynamic Parallel MR Image Reconstruction
Cardiac magnetic resonance imaging (CMR) has been widely used in clinical practice for the medical diagnosis of cardiac diseases.
CartiMorph: a framework for automated knee articular cartilage morphometrics
We compared our FCL measurements with those from a previous study and found that our measurements deviated less from the ground truths.
An Uncertainty Aided Framework for Learning based Liver $T_1ρ$ Mapping and Analysis
Approach: To address this need, we propose a parametric map refinement approach for learning-based $T_1\rho$ mapping and train the model in a probabilistic way to model the uncertainty.
CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI Reconstruction
To maximize the benefits of image domain and k-domain prior knowledge, the reconstructions are aggregated in a frequency fusion module, exploiting their complementary properties to optimize the trade-off between artifact removal and fine detail preservation.
Unsupervised Domain Adaptation for Automated Knee Osteoarthritis Phenotype Classification
Conclusion: The proposed UDA approach improves the performance of automated knee OA phenotype classification for small target datasets by utilising a large, high-quality source dataset for training.
Uncertainty-Aware Self-supervised Neural Network for Liver $T_{1ρ}$ Mapping with Relaxation Constraint
Epistemic uncertainty and aleatoric uncertainty are modelled for the $T_{1\rho}$ quantification network to provide a Bayesian confidence estimation of the $T_{1\rho}$ mapping.
Denoising of Three-Dimensional Fast Spin Echo Magnetic Resonance Images of Knee Joints using Spatial-Variant Noise-Relevant Residual Learning of Convolution Neural Network
In this study, inherent true noise information from 2-NEX acquisition was used to develop a deep-learning model based on residual learning of convolutional neural network (CNN), and this model was used to suppress the noise in 3D FSE MR images of knee joints.
Source-free unsupervised domain adaptation for cross-modality abdominal multi-organ segmentation
Domain adaptation is crucial for transferring the knowledge from the source labeled CT dataset to the target unlabeled MR dataset in abdominal multi-organ segmentation.
Multipath CNN with alpha matte inference for knee tissue segmentation from MRI
A novel multipath CNN-based method is proposed, which consists of an encoder decoder-based segmentation network in combination with a low rank tensor-reconstructed segmentation network.
Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning
In this work, we report a novel unsupervised domain adaptation framework for cross-modality liver segmentation via joint adversarial learning and self-learning.
