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This is the code repository for the paper titiled "Enhancing Multi-Contrast MRI Synthesis: A Novel 3D Dual-CycleGAN Approach"

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HosseinShahverdi/3D-Dual-CycleGAN

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MedNifti_DuCycleGan

Enhancing 3D Multi-Contrast MRI Synthesis with the 3D Dual-CycleGAN Model

Description

GANs offer the ability to represent sharp and complex probability densities through a nonparametric approach . They have been widely adopted in medical image analysis, particularly for tasks like data augmentation and multi-modality image translations, due to their capability to handle domain shift . To address the issue of domain-specific deformations being encoded as domain-specific features and reproduced in the synthesized output, researchers have integrated CycleGAN into the training process. Previous studies have demonstrated that CycleGAN can be trained using unpaired brain data . However, these studies were more limited to training the network on a single slice and were two-dimensional in nature. Moreover, image synthesis was primarily performed within a single modality, such as synthesizing T1W from T2W or synthesizing T2W from FLAIR and vice versa. This study aims to synthesize 3D Multi-Contrast MRI using 3D Dual-CycleGAN.

Getting Started

Dependencies

  • prerequisites, libraries, etc., needed before installing program.
  • tensorflow==1.14.0
  • imageio==2.22.4
  • matplotlib==3.5.3
  • nibabel==4.0.2
  • numpy==1.21.6
  • opencv_python==4.1.0.25
  • Pillow==9.3.0
  • scipy==1.7.3
  • SimpleITK==2.2.1

Installing

  • first of all you should install requirments with code below:
pip install -r requiremnets.txt

Executing program

for running the code you should use main.py file and run it.

python main.py

Authors

Contributors names and contact info

Ali Mahboubisarighieh mahboubi.ali1991@gmail.com , Shabnam Jafarpoor shabnamjafarpoor1371@gmail.com , Hossein Shahverdi h.shahverdi1997@gmail.com

Version History

  • 0.1
    • Initial Release

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This is the code repository for the paper titiled "Enhancing Multi-Contrast MRI Synthesis: A Novel 3D Dual-CycleGAN Approach"

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