VarNetRecon

KO | EN

gitlite — search

#hacktoberfest #react #typescript #go #golang #android #rust #deep-learning #java #cli #kubernetes #vue #awesome-list #ios

(★ 14)

Implementation of Variational Networks for image reconstruction in tensorflow

VarNetRecon Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Variational Networks for image reconstruction in tensorflow
This is a generic implementation of Variational Network (loop unrolling) for MR image reconstruction, proposed by [Hammerick et al.](https://arxiv.org/abs/1704.00447). 
MRI data used to train the network is introduced and described by [Johannes Schmidt and Sebastian Kozerke](http://dev.ismrm.org/2017/3991.html).

## Running the code 
* Download 2D short axis cardiac MRI dataset https://polybox.ethz.ch/index.php/s/pYQc9w3M5MgRd4X
* Inspect configuration of the network in `recon_model_300_066.py`. We used:
  * 10 layers
  * 7x7 filters
  * 30 filters per layer
  * 35 knots to parametrize activations
  * cubic interpolation for activations
* Run `example.sh` for training, or use saved parameters `ckpts/model.ckpt` and run `recon_model_300_066.py`
* Run matlab script `report_imgs.m` for illustrations of reconstruction and learned parameters

## Demo
For training we used fully sampled 128x128 short axis MR images of the heart with artificially generated smooth phase offset.
The k-space was retrospectively undersampled to achieve acceleration factor of ~3. 
Variational Network reconstruction was compared to total variation (TV) regularized reconstruction (with the optimal regularization weight).
![recons example](https://raw.githubusercontent.com/visva89/VarNetRecon/master/imgs/img_ex.png)

Reconstruction for each layer of the network:
![recons layers](https://raw.githubusercontent.com/visva89/VarNetRecon/master/imgs/recon_by_layer.png)

Learned filters (real part):
![recons layers](https://raw.githubusercontent.com/visva89/VarNetRecon/master/imgs/filters.png)

Learned activation functions (integral of activation, i.e. element-wise potentials):
![recons layers](https://raw.githubusercontent.com/visva89/VarNetRecon/master/imgs/pot_int.png)

## Important Notes
* We are reconstructing smaller images compared to original work. Hence different filter sizes are used.
* Multi coil acquisition as well as coil sensitivity maps are not modelled.
* Batch size is fixed at configuration time.
* Cubic interpolation is used by default instead of RBF, since it is more memory efficient. You can choose `linear`, `cubic`, or `RBF` in the network configuration.
* Maximal estimated filter response is fixed for all layers.

## 
The code is developed by Valery Vishnevskiy, [Cardiac Magnetic Resonance group](http://www.cmr.ethz.ch/), Institute for Biomedical Engineering, ETH Zurich.

VarNetRecon

VarNetRecon Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

VarNetRecon

VarNetRecon Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README