https://github.com/mrphys/hyperslice

https://github.com/mrphys/hyperslice

Last synced: about 2 months ago
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• Host: GitHub
• URL: https://github.com/mrphys/hyperslice
• Owner: mrphys
• Created: 2023-06-01T10:34:41.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2023-11-21T16:22:36.000Z (about 1 year ago)
• Last Synced: 2024-03-25T20:23:16.556Z (10 months ago)
• Language: Jupyter Notebook
• Size: 57.9 MB
• Stars: 0
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
HyperSLICE: HyperBand optimised Spiral for Low-latency Interactive Cardiac Examination

======================================================================================

Dr. Olivier Jaubert, Dr. Javier Montalt-Tordera, Dr. Daniel Knight, Pr.

Simon Arridge, Dr. Jennifer Steeden, Pr. Vivek Muthurangu

Synopsis: 

---------

A modified FastDVDnet \[1\] network is trained for interactive and low latency cardiac MRI imaging.

Provided code provides optimized trajectory, model training and offline reconstruction as implemented for the paper.

The ethics does not allow sharing medical image data. 

The code uses natural images of roses with additional simulation of the coils and simple motion.   

Example with abrupt image change: Input - Truth - Reconstructed

-------

https://github.com/mrphys/HyperSLICE/assets/68073827/373de9ca-e23e-4adb-ae22-36ef758b539a

------------------------------------------------------

Installation

============

All required packages can be installed using conda in a virtual environment:

``` {.console}

$ conda env create --name hyperslice --file environment.yml

```

Note that only Linux is supported.

Training and testing

====================

Once the environment created, activate using:

``` {.console}

$ conda activate hyperslice

```

You can then run:

-   the python file example.py file from project directory.

``` {.console}

$ python example.py

```

-   or the ipython notebook example.ipynb (to see intermediate results)

Results are saved in ./Training\_folder (as in the already trained

exemple model ./Training\_folder/Exemple\_Trained\_FastDVDnet)

Logs can be visualised using tensorboard:

``` {.console}

$ tensorboard --logdir path_to_directory

```

Acknowledgments

===============

Network architecture inspired from original FastDVDnet \[1\].

Code relies heavily on TensorFlow \[2\] and TensorFlow\_MRI \[3\].

\[1\] ​​Tassano, M., Delon, J., & Veit, T. (2020). FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1351–1360.

\[2\] Abadi M, Barham P, Chen J, et al. TensorFlow: A System for Large-Scale Machine Learning TensorFlow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation 2016:265--283.

\[3\] Montalt Tordera J. TensorFlow MRI. 2022 doi:10.5281/ZENODO.7120930.

https://pypi.org/project/tensorflow-mri/