Cross-modal augmentation of Magnetic Resonance Imaging (MRI) and microscopic imaging based on the same tissue samples is promising because it can allow histopathological analysis in the absence of an underlying invasive biopsy procedure. Here, we tested a method for generating microscopic histological images from MRI scans of the corpus callosum using conditional generative adversarial network (cGAN) architecture. To our knowledge, this is the first multimodal translation of the brain MRI to histological volumetric representation of the same sample. The technique was assessed by training paired image translation models taking sets of images from MRI scans and microscopy. The use of cGAN for this purpose is challenging because microscopy images are large in size and typically have low sample availability. The current work demonstrates that the framework reliably synthesizes histology images from MRI scans of corpus callosum, emphasizing the network's ability to train on high resolution histologies paired with relatively lower-resolution MRI scans. With the ultimate goal of avoiding biopsies, the proposed tool can be used for educational purposes.

MRI扫描的磁共振成像和显微成像的跨模态增强具有潜力，可以在没有侵入性活检程序的情况下进行组织病理学分析。本文测试了一种使用条件生成对抗网络（cGAN）架构从MRI扫描中生成微观组织学图像的方法。该方法通过训练配对的图像转换模型，将MRI扫描和显微镜图像集合起来。通过可靠地合成胼胝体的组织学图像，这项技术突显了网络在训练高分辨率组织学图像和相对较低分辨率MRI扫描方面的能力。该工具的最终目标是避免活检，可用于教育目的。

通过生成对抗网络在小样本大小设置中进行显微镜和磁共振成像之间的风格转移