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Found 21 papers, 1 papers with code
Enhancing Cardiac MRI Segmentation via Classifier-Guided Two-Stage Network and All-Slice Information Fusion Transformer
Cardiac Magnetic Resonance imaging (CMR) is the gold standard for assessing cardiac function.
An Unsupervised Framework for Joint MRI Super Resolution and Gibbs Artifact Removal
Previous studies tackled these two problems separately, where super resolution methods tend to enhance Gibbs artifacts, whereas Gibbs ringing removal methods tend to blur the images.
Computationally Efficient 3D MRI Reconstruction with Adaptive MLP
Recon3DMLP improves HR 3D reconstruction and outperforms several existing CNN-based models under similar GPU memory consumption, which demonstrates that Recon3DMLP is a practical solution for HR 3D MRI reconstruction.
Holistic Multi-Slice Framework for Dynamic Simultaneous Multi-Slice MRI Reconstruction
Deep learning (DL)-based methods have shown promising results for single-slice MR reconstruction, but the addition of SMS acceleration raises unique challenges due to the composite k-space signals and the resulting images with strong inter-slice artifacts.
JoJoNet: Joint-contrast and Joint-sampling-and-reconstruction Network for Multi-contrast MRI
The proposed framework consists of a sampling mask generator for each image contrast and a reconstructor exploiting the inter-contrast correlations with a recurrent structure which enables the information sharing in a holistic way.
Deep Statistic Shape Model for Myocardium Segmentation
Additionally, the predicted point cloud guarantees boundary correspondence for sequential images, which contributes to the downstream tasks, such as the motion estimation of myocardium.
Flow-based Visual Quality Enhancer for Super-resolution Magnetic Resonance Spectroscopic Imaging
Specifically, we propose a flow-based enhancer network to improve the visual quality of super-resolution MRSI.
Robust Landmark-based Stent Tracking in X-ray Fluoroscopy
Even though angioplasty devices are designed to have radiopaque markers for the ease of tracking, current methods struggle to deliver satisfactory results due to the small marker size and complex scenes in angioplasty.
Invertible Sharpening Network for MRI Reconstruction Enhancement
During inference, the learned blurring transform can be inverted to a sharpening transform leveraging the network's invertibility.
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.
Accelerating 3D MULTIPLEX MRI Reconstruction with Deep Learning
We propose a deep learning framework for undersampled 3D MRI data reconstruction and apply it to MULTIPLEX MRI.
Cardiac Functional Analysis with Cine MRI via Deep Learning Reconstruction
To the best of our knowledge, this is the first work to evaluate the cine MRI with deep learning reconstruction for cardiac function analysis and compare it with other conventional methods.
Measure Anatomical Thickness from Cardiac MRI with Deep Neural Networks
We also analyze thickness patterns on different cardiac pathologies with a standard clinical model and the results demonstrate the potential clinical value of our method for thickness based cardiac disease diagnosis.
Anatomy-Aware Cardiac Motion Estimation
A baseline dense motion tracker is trained to approximate the motion fields and then refined to estimate anatomy-aware motion fields under the weak supervision from the VAE.
Real-Time Cardiac Cine MRI with Residual Convolutional Recurrent Neural Network
Real-time cardiac cine MRI does not require ECG gating in the data acquisition and is more useful for patients who can not hold their breaths or have abnormal heart rhythms.
Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation
In this paper, we propose Motion Pyramid Networks, a novel deep learning-based approach for accurate and efficient cardiac motion estimation.
MRI Image Reconstruction via Learning Optimization Using Neural ODEs
We investigate several models based on three ODE solvers and compare models with fixed solvers and learned solvers.
FOAL: Fast Online Adaptive Learning for Cardiac Motion Estimation
In clinical deployment, however, they suffer dramatic performance drops due to mismatched distributions between training and testing datasets, commonly encountered in the clinical environment.
Robust Multimodal Image Registration Using Deep Recurrent Reinforcement Learning
To this end, these two components are tackled in an end-to-end manner via reinforcement learning in this work.
Pyramid Convolutional RNN for MRI Image Reconstruction
We evaluate our model on the fastMRI knee and brain datasets and the results show that the proposed model outperforms other methods and can recover more details.
DepthSynth: Real-Time Realistic Synthetic Data Generation from CAD Models for 2.5D Recognition
Recent progress in computer vision has been dominated by deep neural networks trained over large amounts of labeled data.
