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Denoising Unpaired Low Dose CT Images with Self-Ensembled CycleGAN
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Denoising Unpaired Low Dose CT Images with Self-Ensembled CycleGAN
Obtaining paired image data is a major obstacle for applying deep learning in CT denoising, where imaging the same target at two different radiation doses would expose patients to unnecessary radiation. In this session, we propose a method to overcome this limitation and train a model that can denoise low dose CT images with only unpaired high dose data using the CycleGAN model. Additionally, we propose the use of Strided Patch Cropping Self-Ensemble (SPACE), a method for stable reconstruction of arbitrarily sized images that does not lose fine details important for accurate clinical analysis.
Obtaining paired image data is a major obstacle for applying deep learning in CT denoising, where imaging the same target at two different radiation doses would expose patients to unnecessary radiation. In this session, we propose a method to overcome this limitation and train a model that can denoise low dose CT images with only unpaired high dose data using the CycleGAN model. Additionally, we propose the use of Strided Patch Cropping Self-Ensemble (SPACE), a method for stable reconstruction of arbitrarily sized images that does not lose fine details important for accurate clinical analysis.
