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A framework for temperature imaging using the change in backscattered ultrasonic signals.

机译：利用反向散射超声信号的变化进行温度成像的框架。

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摘要

Hyperthermia is a cancer treatment that elevates tissue temperature to 40 to 43oC. It would benefit from a non-invasive, safe, inexpensive and convenient thermometry to monitor heating patterns. Ultrasound is a modality that meets these requirements. In our initial work, using both prediction and experimental data, we showed that the change in the backscattered energy (CBE) is a potential parameter for TI. CBE, however, was computed in a straightforward yet ad hoc manner. In this work, we developed and exploited a mathematical representation for our approach to TI to optimize temperature accuracy. Non-thermal effects of noise and motion confound the use of CBE. Assuming additive white Gaussian noise, we applied signal averaging and thresholding to reduce noise effects. Our motion compensation algorithms were also applied to images with known motion to evaluate factors affecting the compensation performance. In the framework development, temperature imaging was modeled as a problem of estimating temperature from the random processes resulting from thermal changes in signals. CBE computation was formalized as a ratio between two random variables. Mutual information (MI) was studied as an example of possible parameters for temperature imaging based on the joint distributions. Furthermore, a maximum likelihood estimator (MLE) was developed. Both simulations and experimental results showed that noise effects were reduced by signal averaging. The motion compensation algorithms proved to be able to compensate for motion in images and were improved by choosing appropriate interpolation methods and sample rates. For images of uniformly distributed scatterers, CBE and MI can be computed independent of SNR to improve the temperature accuracy. The application of the MLE also showed improvements in temperature accuracy compared to the energy ratio from the signal mean in simulations. The application of the framework to experimental data requires more work to implement noise reduction approaches in 3D heating experiments. The framework identified ways in which we were able to reduce the effects of both noise and motion. The framework formalized our approaches to temperature imaging, improved temperature accuracy in simulations, and can be applied to experimental data if the noise reduction approaches can be implemented for 3D experiments.
机译：热疗是将组织温度升高至40至43oC的癌症治疗方法。这将受益于非侵入性，安全，便宜且方便的测温法来监测加热模式。超声是一种可以满足这些要求的方法。在我们的初步工作中，同时使用预测和实验数据，我们证明了背向散射能量（CBE）的变化是TI的潜在参数。但是，CBE是通过直接但临时的方式进行计算的。在这项工作中，我们为TI的方法开发并利用了数学表示法来优化温度精度。噪声和运动的非热效应混淆了CBE的使用。假设加性高斯白噪声，我们应用了信号平均和阈值处理以减少噪声影响。我们的运动补偿算法也应用于已知运动的图像，以评估影响补偿性能的因素。在框架开发中，温度成像被建模为根据信号热变化产生的随机过程估算温度的问题。 CBE计算形式化为两个随机变量之间的比率。研究了互信息（MI），作为基于关节分布的温度成像可能参数的示例。此外，开发了最大似然估计器（MLE）。仿真和实验结果均表明，通过信号平均可以降低噪声影响。运动补偿算法被证明能够补偿图像中的运动，并且通过选择适当的插值方法和采样率得到了改进。对于均匀分布的散射体图像，可以独立于SNR计算CBE和MI，以提高温度精度。与模拟中信号平均值的能量比相比，MLE的应用还显示出温度精度的提高。将该框架应用于实验数据需要更多的工作来实现3D加热实验中的降噪方法。该框架确定了我们能够减少噪声和运动影响的方法。该框架使我们的温度成像方法正式化，在模拟中提高了温度精度，如果可以将降噪方法用于3D实验，则可以将其应用于实验数据。

著录项

  • 作者

    Guo, Yuzheng.;

  • 作者单位

    Washington University in St. Louis.;

  • 授予单位 Washington University in St. Louis.;
  • 学科 Engineering Biomedical.;Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 190 p.
  • 总页数 190
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物医学工程;无线电电子学、电信技术;
  • 关键词

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