We investigated three different Deep Learning techniques for performing retrospective fat suppression in T2 weighted imaging of lung cancer. The methods considered were two U-nets, using an L1 cost function or a conditional GAN, and a CycleGAN. The networks were trained on 900 images and then 16 test images were scored by 3 oncologists and a research radiographer. The L1 U-net and CycleGAN were scored at 73% and 72% respectively, relative to a gold standard of 80% for prospectively fat saturated images, and the scorers indicated they would be happy to use the generated images for radiotherapy target delineation.
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