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.
We propose a deep learning framework for undersampled 3D MRI data reconstruction and apply it to MULTIPLEX MRI.
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.
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.
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 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.
In this paper, we propose Motion Pyramid Networks, a novel deep learning-based approach for accurate and efficient 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.
To this end, these two components are tackled in an end-to-end manner via reinforcement learning in this work.
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.
Recent progress in computer vision has been dominated by deep neural networks trained over large amounts of labeled data.