Dynamic High Resolution Photoacoustic Tomography System
动态高分辨率光声断层扫描系统
基本信息
- 批准号:EP/K009745/1
- 负责人:
- 金额:$ 83.81万
- 依托单位:
- 依托单位国家:英国
- 项目类别:Research Grant
- 财政年份:2013
- 资助国家:英国
- 起止时间:2013 至 无数据
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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.
生物医学成像包括测量几乎所有类型的波和粒子的方法,包括声学,电学,光学和核子。通常,在给出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那些系统与具有良好生理对比度但分辨率差的那些系统之间存在折衷。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:闪光照射到标本上,当光加热组织时,发出非常小的声波。我们用一个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,收集这些数据需要几分钟,因此成像仅限于时间上静止的标本。在这个提议中,我们的目标是使这个过程快几百倍,使用一种新的数学传感理论的灵感来自于图像压缩-该技术允许显着减少的大小的图像文件在磁盘上的数码相机,而不会在视觉上降低图像质量。传感器阵列上的声场作为光声波通过是一个随时间变化的二维函数。这个函数,在一个单一的时刻,可以被认为是基本模式的总和(就像一个时间序列可以被分解成许多频率分量的方式)。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有助于功能。然而,由于我们不知道哪些是先验的,我们不能直接衡量他们的贡献。在这种情况下,数学理论告诉我们,我们能做的最好的事情就是使用尽可能与基本模式无关的询问模式来感知函数。如果精确描述场所需的基本模式的数量很小,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,挑战在于找到这样的基本和询问模式集,精确描述场所需的测量数量尽可能少。基于这个想法,在这个项目中,我们将构建一个光声系统,使用这种询问模式测量发射的声波,并测试它是否准确地捕获了数据中的所有所需信息。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。我们将应用该系统来测试移动和流动物体的情况,我们确切地知道这些变化是什么,然后将其应用于真实的临床前问题,即观察小动物(如小鼠)毛细血管中的血液流动。这个新系统将使我们能够观察动物大脑耗氧量的变化,这将告诉我们大脑的哪些部分与不同的功能有关。这些信息可用于开发药物如何在身体组织中被吸收的模型,以及它们如何随着时间的推移被代谢或冲洗出来。该项目的成功将是生物医学成像的重大突破,可以在空间和时间上实现直接重要的组织状态测量的高分辨率。它将把先进的光学和声学测量系统与新颖的数学和计算机编程结合在一起。它将开辟光声的新应用范围,并为医学和生物科学家研究活体标本的生理学提供独特的工具。
项目成果
期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
A hierarchical Bayesian perspective on majorization-minimization for non-convex sparse regression: application to M/EEG source imaging
- DOI:10.1088/1361-6420/aac9b3
- 发表时间:2018-08-01
- 期刊:
- 影响因子:2.1
- 作者:Bekhti, Yousra;Lucka, Felix;Gramfort, Alexandre
- 通讯作者:Gramfort, Alexandre
Iterated preconditioned LSQR method for inverse problems on unstructured grids
- DOI:10.1088/0266-5611/30/7/075009
- 发表时间:2014-06
- 期刊:
- 影响因子:2.1
- 作者:S. Arridge;M. Betcke;Lauri Harhanen
- 通讯作者:S. Arridge;M. Betcke;Lauri Harhanen
Joint Reconstruction and Low-Rank Decomposition for Dynamic Inverse Problems
- DOI:10.3934/ipi.2021059
- 发表时间:2020-05
- 期刊:
- 影响因子:0
- 作者:S. Arridge;Pascal Fernsel;A. Hauptmann
- 通讯作者:S. Arridge;Pascal Fernsel;A. Hauptmann
On the adjoint operator in photoacoustic tomography
- DOI:10.1088/0266-5611/32/11/115012
- 发表时间:2016-11-01
- 期刊:
- 影响因子:2.1
- 作者:Arridge, Simon R.;Betcke, Marta M.;Treeby, Brad E.
- 通讯作者:Treeby, Brad E.
Task adapted reconstruction for inverse problems
- DOI:10.1088/1361-6420/ac28ec
- 发表时间:2022-07-01
- 期刊:
- 影响因子:2.1
- 作者:Adler, Jonas;Lunz, Sebastian;Oktem, Ozan
- 通讯作者:Oktem, Ozan
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Simon Arridge其他文献
Investigating Intensity Normalisation for PET Reconstruction with Supervised Deep Learning
利用监督深度学习研究 PET 重建的强度归一化
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
I. Singh;Alexander Denker;Bangti Jin;Kris Thielemans;Simon Arridge - 通讯作者:
Simon Arridge
Deep Image Prior PET Reconstruction using a SIRF-Based Objective
使用基于 SIRF 的物镜进行深度图像先验 PET 重建
- DOI:
10.1109/nss/mic44845.2022.10399292 - 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
I. Singh;Riccardo Barbano;R. Twyman;Ž. Kereta;Bangti Jin;Simon Arridge;Kris Thielemans - 通讯作者:
Kris Thielemans
Rapid workflow of mMR PET list-mode data processing using CUDA
- DOI:
10.1186/2197-7364-2-s1-a42 - 发表时间:
2015-05-18 - 期刊:
- 影响因子:3.200
- 作者:
Pawel Markiewicz;Kris Thielemans;David Atkinson;Simon Arridge;Brian Hutton;Sebastien Ourselin - 通讯作者:
Sebastien Ourselin
Improved parameter-estimation with combined PET-MRI kinetic modelling
- DOI:
10.1186/2197-7364-2-s1-a25 - 发表时间:
2015-05-18 - 期刊:
- 影响因子:3.200
- 作者:
Kjell Erlandsson;Maria Liljeroth;David Atkinson;Simon Arridge;Sebastien Ourselin;Brian Hutton - 通讯作者:
Brian Hutton
Data-driven approaches for electrical impedance tomography image segmentation from partial boundary data
根据部分边界数据进行电阻抗断层扫描图像分割的数据驱动方法
- DOI:
10.3934/ammc.2024005 - 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Alexander Denker;Ž. Kereta;I. Singh;Tom Freudenberg;T. Kluth;Peter Maass;Simon Arridge - 通讯作者:
Simon Arridge
Simon Arridge的其他文献
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{{ truncateString('Simon Arridge', 18)}}的其他基金
CONcISE: COmputatioNal Imaging as a training Network for Smart biomedical dEvices
简明:计算成像作为智能生物医学设备的培训网络
- 批准号:
EP/X030733/1 - 财政年份:2023
- 资助金额:
$ 83.81万 - 项目类别:
Research Grant
Tomographic imaging of flow and chromophore concentrations in biological tissue
生物组织中血流和发色团浓度的断层扫描成像
- 批准号:
EP/N032055/1 - 财政年份:2016
- 资助金额:
$ 83.81万 - 项目类别:
Research Grant
Dynamic Peri-operative Cerenkov Luminescence Imaging for Robotic Assisted Surgery (EDCLIRS)
用于机器人辅助手术的动态围手术期切伦科夫发光成像 (EDCLIRS)
- 批准号:
EP/N022750/1 - 财政年份:2016
- 资助金额:
$ 83.81万 - 项目类别:
Research Grant
Parameter and Structure Indentification in Optical Tomography
光学断层扫描中的参数和结构识别
- 批准号:
EP/E034950/1 - 财政年份:2007
- 资助金额:
$ 83.81万 - 项目类别:
Research Grant
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