Liver synthetic CT generation based on a dense-CycleGAN for MRI-only treatment planning

The application of MRI significantly improves the accuracy and reliability of target delineation for many disease sites in radiotherapy due to its superior soft tissue contrast as compared to CT. However, MRI data do not contain the electron density information that is necessary for accurate dose calculation. There has been limited work in abdominal synthetic CT (sCT) generation. In this work, we propose to integrate dense blocks and a novel compound loss function into a 3D cycleGAN-based framework to generate sCT from MR images. Since MRI and CT are two different image modalities, dense blocks are employed to combine low- and high-frequency information that can effectively represent different image patches. A novel compound loss function with lp-norm (p = 1.5) distance and gradient difference is used to differentiate the structure boundaries and to retain the sharpness of the sCT image. This proposed algorithm was evaluated using 21 hepatocellular cancer patients’ registered MR and CT images as the training dataset. Leave-one-out cross-validation was performed. The average mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross correlation (NCC) were 72.87±18.16 HU, 22.65±3.63 dB, and 0.92±0.04 respectively. The proposed method has encouraging outcomes in generating sCT for the potential use in MR-only photon or proton radiotherapy treatment planning.