TarGAN: CT to MRI Translation Using Private Unpaired Data Domain

Abstract

The detection and treatment of cancer and other disorders depend on the use of magnetic resonance imaging (MRI) and computed tomography (CT) scans. Compared to CT scan, MRI scans provide sharper pictures. An MRI is preferable to an X-ray or CT scan when the doctor needs to observe the soft tissues. Besides, MRI scans of organs and soft tissues, such as damaged ligaments and herniated discs, can be more accurate than CT imaging. However, capturing MRI typically takes longer than CT. Furthermore, MRI is substantially more expensive than CT because it requires more sophisticated current equipment. As a result, it is challenging to gather MRI scans to help with the medical image segmentation training issue. To address the aforementioned issue, we suggest using a deep learning network (TarGAN) to reconstruct MRI from CT scans. These created MRI images can then be used to enrich training data for MRI images segmentation issues.