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There is a significant need for the generation of virtual histologicalinformation from coronary optical coherence tomography (OCT) images to betterguide the treatment of coronary artery disease. However, existing methodseither require a large pixel-wisely paired training dataset or have limitedcapability to map pathological regions. To address these issues, we proposed astructural constrained, pathology aware, transformer generative adversarialnetwork, namely SCPAT-GAN, to generate virtual stained H&E histology from OCTimages. The proposed SCPAT-GAN advances existing methods via a novel design toimpose pathological guidance on structural layers using transformer-basednetwork.

SCPAT-GAN: Structural Constrained and Pathology Aware Convolutional Transformer-GAN for Virtual Histology Staining of Human Coronary OCT images