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DLPVI:深度学习框架集成投影,视图反向投影和图像域,用于高和超稀疏视图CBCT重建

Xuzhi Zhao¹, Yi Du², Yahui Peng¹

¹School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

|February 8, 2025

概括

本研究介绍了DLPVI,这是一种深度学习框架,可以通过稀疏视图投影提高形光束计算机断层扫描 (CBCT) 图像质量. 在重建高和超稀疏视图CBCT图像方面,DLPVI显著优于现有方法.

科学领域:

医疗成像医学成像
深度学习 (Deep Learning) 是一种深度学习.
计算成像技术的成像

背景情况:

稀疏视图圆束计算断层扫描 (CBCT) 对于降低辐射剂量至关重要,但受到图像工件和噪声的影响.
从有限的投影数据中重建高质量的CBCT图像仍然是医学成像中的重大挑战.

研究的目的:

开发和验证一个新的深度学习框架,DLPVI,以提高高散度视图和超散度视图CBCT图像的质量.
通过整合多个域来提高图像保真度来解决现有重建方法的局限性.

主要方法:

DLPVI框架集成了投影,视图对视图反向投影 (VVBP) 和图像领域.
它采用一个投影域子框架用于全视图投影恢复,一个VVBP域子框架用于噪音和文物抑制,以及一个基于变压器的图像域模型用于最终改进.
该模型在真实患者的CBCT数据上进行了训练和验证,并使用RMSE,PSNR,SSIM和FSIM指标进行了定量评估.

主要成果:

与15种最先进的方法相比,DLPVI在1/8高散度视图和1/16超散度视图重建任务上表现出卓越的性能.
统计分析证实了图像质量指标的统计显著改善 (p < 0.05).
该框架有效地减少了文物和噪音,从而导致更高保真度的CBCT图像.

结论:

拟议的DLPVI框架显著提高了CBCT图像的质量,这些图像是从高和超稀疏视图投影数据中重建的.
DLPVI为稀疏视图CBCT重建提供了强大而有效的解决方案,为医学成像中剂量减少铺平了道路.

关键词:

在CBCT中,CBCT是CBCT.深度学习是一种深度学习.图像域名图像域名投影域是一个投影域.斯帕斯视图重建的重建视图对视图反向投影 (VVBP) 域名

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