Transfer generative adversarial network for multimodal CT image super-resolution (Conference Presentation)
Transfer generative adversarial network for multimodal CT image super-resolution (Conference Presentation)
复制标题
用于多模态 CT 图像超分辨率的转移生成对抗网络（会议演示）
DOI：
10.1117/12.2549533
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Fang, Ruogu
中科院分区：
文献类型：
--
作者：
Xiao, Yao;Fang, Ruogu
多模式CT扫描包括非增强CT(NCCT)、CT灌注扫描(CTP)和CT血管成像(CTA)，广泛应用于急性卒中的诊断和治疗计划。虽然每种成像方式用于不同的可视化目的，例如解剖结构和功能信息，但获得的图像质量是不同的。在这项工作中，我们的目标是通过使用深度学习技术来提高所有模式的图像质量。通过实验证明，利用迁移学习和生成对抗网络，NCCT图像有利于CTP图像的重建，CTP图像有助于CTA图像质量的提高。
Multimodal computed tomography (CT) scans, including non-contrast CT (NCCT), CT Perfusion (CTP), and CT Angiography (CTA) are widely used in acute stroke diagnosis and treatment planning. While each imaging modality is for different visualization purposes such as anatomical structures and functional information, image quality is obtained variously. In this work, we aim at enhancing the image quality for all modalities by using deep learning technology. Through our experiments, we demonstrate that by using transfer learning and generative adversarial network, NCCT images are beneficial for CTP image reconstruction, and CTP images are helpful for CTA image quality enhancement.