Magnetic resonance image (MRI) synthesis from brain computed tomography (CT) images based on deep learning methods for magnetic resonance (MR)-guided radiotherapy

• Published in: Quantitative imaging in medicine and surgery Vol. 10; no. 6; pp. 1223 - 1236
• Main Authors: Li, Wen, Li, Yafen, Qin, Wenjian, Liang, Xiaokun, Xu, Jianyang, Xiong, Jing, Xie, Yaoqin
• Format: Journal Article
• Language: English
• Published: China AME Publishing Company 01.06.2020
• Subjects: Original; radiotherapy; Computed tomography (CT); patient setup; image synthesis; magnetic resonance image (MRI)
• DOI: 10.21037/qims-19-885
• ISSN: 2223-4292

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. 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). In our experiments, U-Net with L1+L2 loss achieved the best results with the lowest overall average MAE of 74.19 and MSE of 1.035*10 , respectively, and produced the highest SSIM of 0.9440 and PSNR of 32.44. 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.