Parameter and Structure Indentification in Optical Tomography

光学层析成像是一种非侵入性成像技术，用于成像生物组织的光学特性，特别是外周肌肉、乳房和大脑。光学层析成像利用放置在组织表面的一组光极来传递输入信号。另一组位于不同位置的光管探测通过生物组织传播的出射光子。这些边界测量中的光子分布被用来重建内部光吸收和散射系数分布的图像，所得到的图像支持广泛的临床应用。这些包括(I)非侵入性检测乳房肿瘤；(Ii)肌肉和大脑活动的功能成像；(Iii)估计大脑氧合和血流动力学；(Iv)测量细胞色素氧化酶和线粒体能量；(V)研究肌肉的氧化代谢；(Vi)测量器官移植中的组织存活；(Vii)检测关节炎患者的关节异常。硬件紧凑，允许在其他成像方式不实用的临床环境中使用。然而，尽管有这些优点，光学层析成像还没有得到广泛的应用。广泛接受的主要障碍之一是图像重建方法速度慢且不准确。我们考虑了三个基本问题：1.组织中光传播的精确建模方法太慢，无法重复使用来解决成像问题。光学测量的噪声和数量有限，这使得成像问题本质上是不准确的。在重建图像中识别具有临床意义的对象涉及到噪声图像的处理，即使寻找的对象的数量或类型很少。我们将通过三种策略来解决这些问题：1.使用模型约简技术，允许使用相对不准确(但快速)的模型，前提是正确地处理所产生的错误。使用统计技术严格使用先验知识3。直接从数据中重建临床对象，省去了制作图像的潜在不稳定步骤。我们将对这些方法进行严格的开发和评估，包括对实验数据的验证。开发的软件将在互联网上发布。

Optical tomography is a non-invasive imaging technique for imaging the optical properties of biological tissue, particularly the peripheral muscle, breast and the brain. Optical tomography utilizes a set of optodes placed on the surface of the tissue to deliver an input signal. A second set of optodes at different locations detect exiting photons which have propagated through the biological tissue. The distribution of photons in these boundary measurements is used to reconstruct images of internal optical absorption and scattering coefficient distributions.The resulting images support a wide range of clinical applications. These include (i) non-invasive detection of breast tumours; (ii) functional imaging of muscle and brain activities; (iii) estimation of cerebral oxygenation and haemodynamics; (iv) measurements of cytochrome oxidase and mitochondrial energetics; (v) investigation of oxidative metabolism in muscle; (vi) measurements of tissue viability in transplantationof organs; and (vii) detection of abnormalities in joints of arthritic patients.Optical tomography is faster and cheaper than alternative imaging methods. The hardware is compact, allowing use in clinical settings where other imaging modalities are impractical. However, despite these advantages, optical tomography is not yet widely used. One of the major barriers to widespread acceptance is that the image reconstruction methods are slow and inaccurate. We consider three basic problems that are at the root of this block to progress :1. Accurate modelling methods for light propagation in tissue are too slow to be used repeatedly in solving the imaging problem.2. Optical measurements are noisy and limited in number which makes the imaging problem intrinsically inaccurate.3. Identification of clinically significant objects in the reconstructed images involves processing of noisy images even though the number or tyoe of object being sought is small.We will tackle these problems with three strategies :1. The use of model reduction techniques that allow the use of relatively inaccurate (but fast )models provided that the resultant errors are correctly handled2. The use of prior knowledge in a rigorous way using statistical techniques3. The direct reconstruction of clinical objects from the data, missing out the potentially unstable step of making the images.We will undertake a rigorous development and evaluation of these methods, including validation on experimental data. Developed software will be released on the internet.