计算机科学
一致性(知识库)
相似性(几何)
人工智能
匹配(统计)
噪音(视频)
模式识别(心理学)
模态(人机交互)
图像(数学)
算法
数学
统计
作者
Shaoyan Pan,Chih-Wei Chang,Junbo Peng,Jiahan Zhang,Richard L. J. Qiu,Tonghe Wang,Justin Roper,Tian Liu,Hui Mao,Xiao‐Jun Yang
出处
期刊:Cornell University - arXiv
日期:2023-04-28
标识
DOI:10.48550/arxiv.2305.00042
摘要
This study aims to develop a novel Cycle-guided Denoising Diffusion Probability Model (CG-DDPM) for cross-modality MRI synthesis. The CG-DDPM deploys two DDPMs that condition each other to generate synthetic images from two different MRI pulse sequences. The two DDPMs exchange random latent noise in the reverse processes, which helps to regularize both DDPMs and generate matching images in two modalities. This improves image-to-image translation ac-curacy. We evaluated the CG-DDPM quantitatively using mean absolute error (MAE), multi-scale structural similarity index measure (MSSIM), and peak sig-nal-to-noise ratio (PSNR), as well as the network synthesis consistency, on the BraTS2020 dataset. Our proposed method showed high accuracy and reliable consistency for MRI synthesis. In addition, we compared the CG-DDPM with several other state-of-the-art networks and demonstrated statistically significant improvements in the image quality of synthetic MRIs. The proposed method enhances the capability of current multimodal MRI synthesis approaches, which could contribute to more accurate diagnosis and better treatment planning for patients by synthesizing additional MRI modalities.
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