通过皮肤局部化网络和自适应权重进行增强的临床光声血管成像
Chuqin Huang1, Emily Zheng1, Wenhan Zheng1
1Department of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14228, United States.
Photoacoustics
|February 7, 2025
概括
这项研究引入了一个新的框架,以改善光声断层扫描 (PAT) 成像中的深血管可视化. 该方法有效地减少了皮肤信号干扰和组织衰减,以获得更清晰的深血管成像.
科学领域:
- 生物医学成像技术 生物医学成像技术
- 光学成像技术的成像
- 医疗技术 医疗技术 医学技术
背景情况:
- 光声断层扫描 (PAT) 为临床前和临床研究提供高分辨率成像.
- 在PAT中深层血管的可视化可能会受到表面信号和信号减弱的阻碍.
- 现有的方法难以准确地描绘不同患者群体的深血管结构.
研究的目的:
- 开发和评估一种新的框架,用于在光声断层扫描中增强深层血管检索.
- 为应对皮肤信号和组织衰减在PAT成像中的挑战.
- 提高PAT对深血管结构的定量分析能力.
主要方法:
- 使用深度学习网络来准确细分皮肤层.
- 开发了一种自适应权重算法,以补偿组织中信号减弱.
- 该框架将深度学习细分与信号补偿集成为船舶增强.
主要成果:
- 拟议的框架显著改善了深水船只的可视化.
- 船舶占用量指标和信号与噪声比 (SNR) 显示出了显著的改善.
- 这种方法在各种体位和各种皮肤色调中被证明是有效的.
结论:
- 开发的框架增强了光声断层扫描中的深血管恢复.
- 这种方法有可能改善光声学研究中的定量分析.
- 该方法在需要深血管成像的临床前和临床应用中都显示出前途.
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