Authors:
Anamika Jha
1
and
Hitoshi Iima
2
Affiliations:
1
Department of Information Science, Kyoto Institute of Technology, Matsugasaki, Kyoto, Japan
;
2
Department of Information and Human Sciences, Kyoto Institute of Technology, Matsugasaki, Kyoto, Japan
Keyword(s):
MRI, CT, Deep Learning, CycleGAN, Unpaired Dataset, Image Translation.
Abstract:
Medical imaging plays a crucial role in healthcare, with Magnetic Resonance Imaging (MRI) and Computed tomography (CT) as key modalities, each having unique strengths and weaknesses. MRI offers exceptional soft tissue contrast, but it is slow and costly, while CT is faster but involves ionizing radiation. To address this paradox, we leverage deep learning, employing CycleGAN to translate CT scans into MRI-like images. This approach eliminates the need for additional radiation exposure or costs. Our results, which show the effectiveness of our image translation method with an MAE of 0.5309, MSE of 0.37901, and PSNR of 52.344, demonstrate the promise of this invention in lowering healthcare costs, expanding diagnostic capabilities, and improving patient outcomes. The model was trained for 500 epochs with a batch size of 500 on an Nvidia GPU, RTX A6OOO.