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以先前信息为导向的重建网络用于正电子发射断层扫描图像

Zhiyuan Liu¹, Bo Wang¹, Huihui Ye^1,2,3

¹State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China.

Quantitative imaging in medicine and surgery

|December 18, 2023

概括

一个新的深度学习网络,先前信息引导重建网络 (PIGRN),提高了 pozitron发射断层扫描 (PET) 图像质量. 该方法使用先前的知识来指导重建,提高医学成像的可靠性和准确性.

科学领域:

医疗成像医学成像
人工智能的人工智能
辐射科学辐射科学

背景情况:

深度学习在医学图像重建方面显示出前景.
对PET图像的直接深度学习重建可以创建文物.
需要可靠的PET图像重建方法至关重要.

研究的目的:

开发和测试用于PET图像重建的灵活深度学习网络.
使用先前知识指导重建,以确保高质量和可靠性.
解决PET成像中不受约束的深度学习的局限性.

主要方法:

开发了一个新的先前信息引导重建网络 (PIGRN).
使用基于条件生成对抗网络 (cGAN) 的双通道生成器和2级歧视器.
将先前信息 (PI) 纳入重建过程中,例如过后投影 (FBP) 图像.

主要成果:

与模拟数据上的U-Net和Deep-PET相比,PIGRN显著提高了PET图像重建质量.
达到了高峰信号噪声比 (PSNR) 31.8498和结构相似度指数 (SSIM) 0.9754.
在真实的Sprague Dawley大鼠数据上表现出强大的概括能力.

结论:

灵活的PIGRN有效地结合了原始PET数据和先前信息.
在PET重建中,PIGRN提高了图像质量和可靠性.
该网络显示了针对各种PET成像场景的强大的概括能力.

关键词:

阳位子辐射断层扫描图像 (PET图像)深度学习是一种深度学习.预先提供信息 (PI)重建的重建的重建.这是一个sinogram,sinogram.

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