调查旅行时间和反射断层扫描在超声计算机断层扫描中的基于深度学习的声速估计的使用
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
|September 12, 2024
概括
这项研究表明,结合旅行时断层扫描和反射断层扫描数据,可以提高超声波计算机断层扫描 (USCT) 声速 (SOS) 重建使用深度学习的准确性. 这种双输入方法为传统方法提供了一个计算效率高的替代方案.
科学领域:
- 医疗成像医学成像
- 计算成像技术的成像
- 生物医学工程 生物医学工程
背景情况:
- 超声波计算机断层扫描 (USCT) 对于量化声学组织属性,如声速 (SOS) 等,至关重要.
- 全波形反转 (FWI) 提供了准确的SOS重建,但在计算上是密集的.
- 基于深度学习的图像对图像学习重建 (IILR) 提供了一个计算高效的替代方案.
研究的目的:
- 调查输入方式对IILR对USCT高分辨率SOS重建的影响.
- 为了比较双通道 (旅行时间断层扫描和反射断层扫描) 输入与单通道输入的性能.
- 用数字幻影和临床数据评估IILR在SOS重建中的有效性.
主要方法:
- 一个虚拟的USCT成像系统与数值乳房幻影被用于系统分析.
- 一个受监督的卷积神经网络 (CNN) 被训练到将双通道 (TT和RT) 输入映射到高分辨率的SOS地图上.
- 单输入CNN (仅TT或RT) 被训练进行比较,通过NRMSE,SSIM和PSNR评估准确性.
主要成果:
- 与单通道方法相比,双通道IILR方法显示出更高的性能.
- 定量指标 (NRMSE,SSIM,PSNR) 验证了双输入方法的提高准确性和图像质量.
- 在临床数据上,双通道IILR的整体平均NRMSE为0.2355,SSIM为0.8845,PSNR为28.33dB.
结论:
- 将旅行时间断层扫描和反射断层扫描作为双输入相结合,可以通过IILR在USCT中显著增强高分辨率SOS重建.
- 这种深度学习方法为USCT成像提供了计算效率高,准确的FWI替代方案.
- 这些发现表明,在乳腺成像应用中,有可能提高诊断能力.
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