C3W2 and C3W3：Which is more suitable for MRI sequence translation, pix2pix GAN or Cycle-GAN?

Course Q&A Generative Adversarial Networks (GANS) Apply Generative Adversarial Networks
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Hello everyone,

I have finished this GAN specialization. It is cool. So I am going to synthesize MRI images and have access to a dataset of paired MRI scans, where each subject has been scanned with T1-weighted (T1WI), T2-weighted (T2WI), FLAIR, and DWI sequences. I aim to synthesize either FLAIR or DWI sequences from either T2WI or T1WI sequences.

Could you advise on which method would be more appropriate for this task, pix2pix GAN or Cycle-GAN, given the availability of paired data?

Additionally, when synthesizing the FLAIR or DWI sequences, would it be more advantageous to use a combined input of T1WI and T2WI, or should I opt for using just one of these sequences as the input? What are the pros and cons of each approach in your experience?

Could anyone provide me with some advice? Thank you!

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Hi @Lucifer2012

Both pix2pix GAN and Cycle-GAN are suitable for image-to-image translation tasks. However, the choice between them depends on the characteristics of your dataset and output.

Since you mentioned having access to paired data, you can stick with pix2pix GAN. Pix2pix GAN is more appropriate when you have paired data, where each input image has a corresponding target output image.

On the other hand, Cycle-GAN is useful when you have unpaired data, meaning you don’t have a direct correspondence between input and output images.

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