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.

翻译：基于相同组织样本的磁共振成像与显微成像的跨模态增强具有广阔前景，因为它能在无需进行侵入性活检的情况下实现组织病理学分析。本研究采用条件生成对抗网络架构，测试了一种从胼胝体MRI扫描生成显微组织学图像的方法。据我们所知，这是首次实现同一脑组织样本的MRI到组织学体积表征的多模态转换。通过训练配对图像翻译模型，利用MRI扫描与显微成像的图像集对该技术进行了评估。在此应用中使用cGAN面临挑战，因为显微图像尺寸较大且样本可用性通常较低。本研究表明，该框架能可靠地从胼胝体MRI扫描中合成组织学图像，突显了网络在较高分辨率组织学图像与相对较低分辨率MRI扫描配对数据上的训练能力。该工具以最终避免活检为目标，可用于教育目的。