Synthetic CT Generation from MRI Using Generative Adversarial Networks for MR-Only Brain Radiotherapy Planning

IntroductionRapid magnetic resonance imaging (MRI) plays an increasingly important role in radiotherapy. It improves the accuracy of delineation of target volumes and organs at risk (OARs), but generating accurate synthetic CT(sCT) from MRI remains challenging, and the lack of electron density information limits its further clinical application. Therefore, the purpose of this study was to develop and evaluate a CBAMPix2Pix model for MRI to CT synthesis.MethodsWe adapted the CBAMPix2Pix architecture, incorporating convolutional block attention module(CBAM), to synthesize CT images from 17260 MRI slices of 86 patients with metastatic brain cancer. The model analyzes local features to enhance image authenticity and is designed to map T1-weighted Contrast-enhanced (T1wc) to sCT. To address the data imbalance between normal tissue and bone, we introduce structural similarity loss(SSIM) to enhance local features of learning images, thereby better reducing differences in Hounsfield Unit(HU).ResultsWe evaluate the performance of the model through quantitative and qualitative evaluations. Our proposed model achieves higher peak signal-to-noise ratio (PSNR) of 27.5 ± 3.3 dB, normalized mean absolute error (NMAE) of 0.019 ± 0.023, and structural similarity index (SSIM) of 0.857 ± 0.059 for sCT images in MR simulation sequences, and the average mean absolute error(MAE) was 74.48 ± 22.88 HU in body and 185.89 ± 21.59 HU in bone. The P-values of the Wilcoxon signed-rank test for the CBAMPix2Pix model compared with the other two models in PSNR, SSIM, MAE, and NMAE were all less than 0.05 in the test cohorts.ConclusionWe have developed a novel CBAMPix2Pix model that can effectively generate realistic sCT images comparable to real images, potentially improving the accuracy of MRI-based treatment planning.