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BACKGROUND: Precise patient setup is critical in radiation therapy. Medical imaging plays an essential role in patient setup. As compared to computed tomography (CT) images, magnetic resonance image (MRI) has high contrast for soft tissues, which becomes a promising imaging modality during treatment. In this paper, we proposed a method to synthesize brain MRI images from corresponding planning CT (pCT) images. The synthetic MRI (sMRI) images can be used to align with positioning MRI (pMRI) equipped by an MRI-guided accelerator to account for the disadvantages of multi-modality image registration. METHODS: Several deep learning network models were applied to implement this brain MRI synthesis task, including CycleGAN, Pix2Pix model, and U-Net. We evaluated these methods using several metrics, including mean absolute error (MAE), mean squared error (MSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). RESULTS: , respectively, and produced the highest SSIM of 0.9440 and PSNR of 32.44. CONCLUSIONS: Quantitative comparisons suggest that the performance of U-Net, a supervised deep learning method, is better than the performance of CycleGAN, a typical unsupervised method, in our brain MRI synthesis procedure. The proposed method can convert pCT/pMRI multi-modality registration into mono-modality registration, which can be used to reduce registration error and achieve a more accurate patient setup.
Li et al. (2020) studied this question.