Parameter and Structure Indentification in Optical Tomography

Lead Research Organisation: University College London
Department Name: Computer Science


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.


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Description Imaging of functional and structural information in the brain is possible using infra red light. Sufficient signal is transmitted through the complete width of the head of newborn babies to localise functional activity for example in the motor cortex.
Algorithmic methods developed allow solution of non-linear imaging problems in a few minutes on standard desktop computers.
Exploitation Route The results open the door to portable non-invasive imaging devices that can operate in a clinical environment.
Sectors Healthcare

Description The methods and software developed are used in NeoLab at Addenbrookes Cambridge for monitoring of seizures in premature babies.
First Year Of Impact 2011
Sector Healthcare
Impact Types Societal

Description Healthcare Engineering
Amount £874,937 (GBP)
Funding ID EP/J021318/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 10/2012 
End 03/2016
Title TOAST++ 
Description Toast++ is a software suite for image reconstruction in diffuse optical tomography (DOT). It contains a forward solver module using the finite element method for simulating the propagation of light in highly scattering, inhomogeneous biological tissues, such as the brain. The inverse solver module uses an iterative, model-based approach to reconstruct the unknown distributions of absorption and scattering coefficients in the volume of interest from boundary measurements of light transmission. Toast++ consists of a set of libraries written in C++ for sparse linear algebra, finite element computation, and nonlinear image reconstruction. Several command line applications for forward modelling and inverse solution are included. Users who need additional functionality can write their own applications and link to the core Toast++ libraries. In addition, Toast++ contains bindings for Matlab and Python. This provides a set of functions for accessing the Toast methods from within these scripting environments, without loss of performance. Using Toast++ from within Matlab or Python provides a user-friendly way for quickly adapting Toast to a specific reconstruction problem. It allows rapid prototyping, debugging and visualisation. The Toast++ sources are distributed with a GPL license. In addition to the sources, binary distributions for various computing platforms can be downloaded. Toast++ toolbox is being developed by Martin Schweiger and Simon Arridge at University College London. 
Type Of Technology Software 
Year Produced 2014 
Open Source License? Yes  
Impact Is being widely used for functional Near Infra Red Spectroscopic Imaging Also used in small animal imaging including fluoresence lifetime imaging.