Contrast-enhanced image synthesis using latent diffusion model for precise online tumor delineation in MRI-guided adaptive radiotherapy for brain metastases

Abstract Objective:&#xD;Magnetic resonance imaging-guided adaptive radiotherapy (MRIgART) is a promising technique for long-course RT of large-volume brain metastasis (BM), due to the capacity to track tumor changes throughout treatment course. Contrast-enhanced T1-weighted (T1CE) MRI is essential for BM delineation, yet is often unavailable during online treatment concerning the requirement of contrast agent injection. This study aims to develop a synthetic T1CE (sT1CE) generation method to facilitate accurate online adaptive BM delineation.&#xD;Approach:&#xD;We developed a novel ControlNet-coupled latent diffusion model (CTN-LDM) combined with a personalized transfer learning strategy and a denoising diffusion implicit model (DDIM) inversion method to generate high quality sT1CE images from online T2-weighted (T2) or fluid attenuated inversion recovery (FLAIR) images. Visual quality of sT1CE images generated by the CTN-LDM was compared with classical deep learning models. BM delineation results using the combination of our sT1CE images and online T2/FLAIR images were compared with the results solely using online T2/FLAIR images, which is the current clinical method.&#xD;Main results:&#xD;Visual quality of sT1CE images from our CTN-LDM was superior to classical models both quantitatively and qualitatively. Leveraging sT1CE images, radiation oncologists achieved significant higher precision of adaptive BM delineation, with average Dice similarity coefficient of 0.93 ± 0.02 vs. 0.86 ± 0.04 (p < 0.01), compared with only using online T2/FLAIR images. &#xD;Significance:&#xD;The proposed method could generate high quality sT1CE images and significantly improve accuracy of online adaptive tumor delineation for long-course MRIgART of large-volume BM, potentially enhancing treatment outcomes and minimizing toxicity.