Dynamic High Resolution Photoacoustic Tomography System

生物医学成像包括测量几乎所有类型的波和粒子的方法，包括声、电、光和核。通常，在那些提供高分辨率结构图像但在对比度方面不能很好地区分不同生理状态的系统和那些具有良好生理对比度但分辨率较差的系统之间往往存在权衡。光声成像是“耦合物理”系统的一个例子，因为它测量光谱的光学部分的对比度，这对不同的组织具有高光谱灵敏度，但使用声音来提供高分辨率。它的工作原理与雷击产生的雷声相同，但规模要小得多：一道闪光照射到标本上，当光线将组织加热到几分之一度时，就会发出非常小的声波。我们用非常高分辨率的传感器阵列在空间和时间上测量声音，并使用计算机程序将这些测量结果重新组合成3D图像。然而，目前这些数据需要几分钟的时间才能收集到，所以成像仅限于时间上静态的标本。在这项提议中，我们的目标是将这一过程加快数百倍，使用受图像压缩启发的新数学传感理论-该技术允许在不降低图像质量的情况下显著减小数码相机磁盘上的图像文件大小。当光声波通过时，传感器阵列上的声场是一个时变的2D函数。在单个时刻，该函数可以被认为是基本模式的总和(更像是时间序列可以被分解成多个频率分量的方式)。事实证明，通常可以选择这些基本模式，以便相对较少的模式对功能做出贡献。然而，由于我们事先不知道哪些是它们，我们不能直接衡量它们的贡献。在这种情况下，数学理论告诉我们，我们所能做的最好的事情就是使用与基本模式尽可能不相关的询问模式来感知函数。如果精确描述声场所需的基本模式的数量很少，那么我们只需要相对较少的询问模式来捕获声波中的信息。这就是所谓的压缩传感，挑战是找到这样一组基本和询问模式，即准确描述场所需的测量次数尽可能少。基于这个想法，在这个项目中，我们将建立一个光声系统，使用这种询问模式测量发出的声波，并测试它准确地捕获数据中所有所需的信息。与此同时，我们将发展数学，以确定哪些基本模式和审讯模式是最好的。我们将把该系统应用于测试移动和流动物体的情况，在那里我们确切地知道变化是什么，然后应用到真正的临床前问题，观察小动物(如老鼠)的毛细血管中的血液流动。这一新系统将使我们能够观察动物大脑耗氧量的变化，这将准确地告诉我们大脑的哪些部分与不同的功能有关。这些信息可以用来建立一个模型，说明药物是如何在人体组织中被摄取的，以及它们是如何随着时间的推移而代谢或洗出的。该项目的成功将是生物医学成像的重大突破，使高分辨率的空间和时间直接成为重要的组织状态测量手段。它将把先进的光学和声学测量系统与新的数学和计算机编程结合在一起。它将开辟光声学的新应用范围，并为医学和生物科学家研究活标本的生理学提供一个独特的工具。

Biomedical imaging encompasses methods that measure almost every type of wave and particle including acoustic, electrical, optical and nuclear. Often there is a tradeoff between those systems that give high resolution structural images, but do not discriminate different physiological states well in terms of contrast, and those with good physiological contrast, but poor resolution. Photoacoustic imaging is an example of a "coupled Physics" system because it measures contrast in the optical part of the spectrum, which has high spectral sensitivity for different tissues, but uses sound to give high resolution. It works the same way as thunder is generated from a lightning strike, but on a very much smaller scale: a flash of light is shone onto a specimen and very small waves of sound are emitted when the light heats tissue a few fractions of a degree. We measure the sound with a very high resolution sensor array over space and time and use computer programs to recombine these measurements into 3D images. However, at present, this data takes several minutes to collect, so the imaging is limited to specimens that are static in time. In this proposal we aim to make this process hundreds of times faster, using a new mathematical sensing theory inspired by image compression - the technique that allows significant reduction in the size of an image file on the disk of a digital camera without visually diminishing the image quality.The acoustic field on the sensor array as the photoacoustic wave passes through is a time-varying 2D function. This function, at a single moment in time, can be considered as the sum of basic patterns (rather like the way a time series can be decomposed into a number of frequency components). It turns out that frequently these basic patterns can be chosen so that there are relatively few of them which contribute to the function. However, as we do not know apriori which ones those are, we cannot measure their contribution directly. In this case the mathematical theory tells us that the best we can do is to sense the function using interrogation patterns which are as uncorrelated with the basic patterns as possible. If the number of basic patterns needed to accurately describe the field is small, then we only need relatively few of the interrogation patterns to capture the information in the acoustic wave. This is known as compressed sensing, and the challenge is to find such sets of the basic and interrogating patterns, that the number of measurements required to describe the field accurately is as small as possible.Based on this idea, in this project we are going to build a photoacoustic system that measures the emitted sound waves using such interrogation patterns, and test that it accurately captures all the required information in the data. At the same time we are going to develop the mathematics that determines which basic and interrogation patterns are best. We will apply the system to test cases of moving and flowing objects where we know exactly what the changes are, and then to real preclinical problems looking at the flow of blood in the capillaries of small animals such as mice. This new system will enable us to look at the change in the oxygen consumption of the brain of animals which will tell us exactly which parts of the brain relate to different functions. This information can be used to develop a model of how drugs are taken up in tissues of the body, and how they are metabolised or washed out over time. Success in this project will be a major breakthrough in biomedical imaging, allowing high resolution in space and time of directly important measures of tissue state. It will bring together advanced optical and acoustic measurement systems with novel mathematics and computer programming. It will open up a new range of applications of photoacoustics and provide a unique tool to medical and biological scientists investigating the physiology of living specimens.