使用深度学习的3D电声断层扫描图像增强与SAM-Med3D编码器

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Yankun Lang¹, Jadon Buller², Yifei Xu²

¹Department of Radiation Oncology Physics, University of Maryland, Baltimore, Baltimore, MD 21201, United States of America.

Physics in medicine and biology

|September 16, 2025

概括

本研究引入了使用SAM-Med3D的深度学习框架,以从有限角度数据中改进3D电声断层扫描 (EAT) 成像,使电穿孔治疗的可视化更快,更准确.

科学领域:

医疗成像医学成像
生物医学工程生物医学工程
人工智能的人工智能

背景情况:

电声断层扫描 (EAT) 在临床环境中面临限制,原因是有限角度数据采集的工件.
精确的电场可视化对于基于电穿孔的疗法至关重要.

研究的目的:

开发一个深度学习框架,以增强从单视图投影的3D EAT图像重建.
克服EAT成像中的人工物和扭曲,以改善临床应用.

主要方法:

开发了一个利用SAM-Med3D大型基础模型 (LFM) 的新型深度学习框架.
该框架具有用于本地-全球特征融合的修改编码器和用于生成高分辨率图像的轻量级解码器.
该模型在50次EAT扫描 (6000次查看) 的数据集上进行了训练和验证.

主要成果:

拟议的模型显著优于基线3D U-Nets,具有优越的RMSE,PSNR和SSIM指标.
在2秒内从单个视图实现了全视图3D EAT图像的重建.
在电穿孔疗法中显示了近实时监测和适应剂量验证的潜力.

结论:

这项工作介绍了SAM-Med3D的首次应用,用于增强3D EAT成像.
该框架有效地解决了EAT中有限角度数据的挑战.
这种方法具有显著的潜力,可以提高基于电穿孔的疗法的精度和安全性,增加临床可行性.

关键词:

电声断层扫描 (电声断层扫描) 是一种电声断层扫描.图像增强图像增强图像增强大型基础模型有监督的深度学习.

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