Enhancing Compressed Sensing 4D Photoacoustic Tomography by Simultaneous Motion Estimation

A crucial limitation of current high-resolution 3D photoacoustic tomography (PAT) devices that employ sequential scanning is their long acquisition time. In previous work, we demonstrated how to use compressed sensing techniques to improve upon this: images with good spatial resolution and contrast can be obtained from suitably sub-sampled PAT data acquired by novel acoustic scanning systems if sparsity-constrained image reconstruction techniques such as total variation regularization are used. Now, we show how a further increase of image quality can be achieved for imaging dynamic processes in living tissue (4D PAT). The key idea is to exploit the additional temporal redundancy of the data by coupling the previously used spatial image reconstruction models with sparsity-constrained motion estimation models. While simulated data from a two-dimensional numerical phantom will be used to illustrate the main properties of this recently developed joint-image-reconstruction-and-motion-estimation framework, measured data from a dynamic experimental phantom will also be used to demonstrate their potential for challenging, large-scale, real-world, three-dimensional scenarios. The latter only becomes feasible if a carefully designed combination of tailored optimization schemes is employed, which we describe and examine in more detail

Approximate k-space models and Deep Learning for fast photoacoustic reconstruction

We present a framework for accelerated iterative reconstructions using a fast and approximate forward model that is based on k-space methods for photoacoustic tomography. The approximate model introduces aliasing artefacts in the gradient information for the iterative reconstruction, but these artefacts are highly structured and we can train a CNN that can use the approximate information to perform an iterative reconstruction. We show feasibility of the method for human in-vivo measurements in a limited-view geometry. The proposed method is able to produce superior results to total variation reconstructions with a speed-up of 32 times

Developing Real-Time Implementations of Non-Linear Beamformers for Enhanced Optical Ultrasound Imaging

Free-hand optical ultrasound (OpUS) imaging is an emerging ultrasound imaging paradigm that utilises an array of fiber-optic sources and a single fiber-optic detector to achieve video-rate, real-time imaging with a flexible probe that is immune to electromagnetic interference. Due to the use of only a single detector, such probes have limited channel counts, resulting in significant imaging artefacts and limited contrast when imaging is performed with a conventional Delay-and-Sum (DAS) beamformer. Non-linear beamforming can help improve the imaging quality by exploiting cross-channel coherence across the aperture, at the expense of significantly increased computational complexity. In this work, GPU implementations of different non-linear beamformers were implemented and tailored specifically to OpUS array devices and tested on both simulated and experimental data

Accelerated High-Resolution Photoacoustic Tomography via Compressed Sensing

Current 3D photoacoustic tomography (PAT) systems offer either high image quality or high frame rates but are not able to deliver high spatial and temporal resolution simultaneously, which limits their ability to image dynamic processes in living tissue. A particular example is the planar Fabry-Perot (FP) scanner, which yields high-resolution images but takes several minutes to sequentially map the photoacoustic field on the sensor plane, point-by-point. However, as the spatio-temporal complexity of many absorbing tissue structures is rather low, the data recorded in such a conventional, regularly sampled fashion is often highly redundant. We demonstrate that combining variational image reconstruction methods using spatial sparsity constraints with the development of novel PAT acquisition systems capable of sub-sampling the acoustic wave field can dramatically increase the acquisition speed while maintaining a good spatial resolution: First, we describe and model two general spatial sub-sampling schemes. Then, we discuss how to implement them using the FP scanner and demonstrate the potential of these novel compressed sensing PAT devices through simulated data from a realistic numerical phantom and through measured data from a dynamic experimental phantom as well as from in-vivo experiments. Our results show that images with good spatial resolution and contrast can be obtained from highly sub-sampled PAT data if variational image reconstruction methods that describe the tissues structures with suitable sparsity-constraints are used. In particular, we examine the use of total variation regularization enhanced by Bregman iterations. These novel reconstruction strategies offer new opportunities to dramatically increase the acquisition speed of PAT scanners that employ point-by-point sequential scanning as well as reducing the channel count of parallelized schemes that use detector arrays.Comment: submitted to "Physics in Medicine and Biology

Infrared temperature measurements on fast moving targets: a novel calibration approach

In this study, an infrared system is developed for accurate measurements of surface temperature and heat transfer on fast moving targets. The system was designed for the Oxford Turbine Research Facility, a world-leading experimental facility delivering highly engine representative, scalable heat transfer results for aerospace research. Infrared thermography is employed to acquire temperature maps of high-pressure turbine blades, allowing assessment of surface thermal conditions including heat transfer coefficient, adiabatic wall temperature, Nusselt number, cooling effectiveness, and metal effectiveness. Achieving accurate infrared thermography measurements in rotating turbomachinery experimental conditions is arduous due to reflections from the surroundings, low emissivity of metallic parts, and motion blur resulting from high speed. To overcome these challenges, calibration procedures were developed against a traceable standard using a bespoke steady experimental facility. A method to determine the reflected temperature from surroundings was also validated. Correction for all measurement disturbances is demonstrated to within the accuracy of the primary measurement thermocouple. Finally, the developed calibration method was validated on a fast-moving rotating geometry demonstrating accurate correction for all measurement disturbances, without the need for an in situ calibration. A detailed uncertainty analysis for each calibration step is also presented

Contrast agents for molecular photoacoustic imaging.

Photoacoustic imaging (PAI) is an emerging tool that bridges the traditional depth limits of ballistic optical imaging and the resolution limits of diffuse optical imaging. Using the acoustic waves generated in response to the absorption of pulsed laser light, it provides noninvasive images of absorbed optical energy density at depths of several centimeters with a resolution of ∼100 μm. This versatile and scalable imaging modality has now shown potential for molecular imaging, which enables visualization of biological processes with systemically introduced contrast agents. Understanding the relative merits of the vast range of contrast agents available, from small-molecule dyes to gold and carbon nanostructures to liposome encapsulations, is a considerable challenge. Here we critically review the physical, chemical and biochemical characteristics of the existing photoacoustic contrast agents, highlighting key applications and present challenges for molecular PAI.This work was supported by CRUK (Career Establishment Award no. C47594/A16267 to J.W. and S.E.B., Core Funding C14303/A17197 to J.W. and S.E.B.), the European Commission (CIG FP7-PEOPLE- 2013-CIG-630729 to J.W. and S.E.B.), the EPSRC-CRUK Cancer Imaging Centre in Cambridge and Manchester (C197/A16465 to J.W. and S.E.B.), King’s College London and University College London Comprehensive Cancer Imaging Centre Cancer Research UK & Engineering and Physical Sciences Research Council, in association with the Medical Research Council and the Department of Health, UK (P.B.), and the European Union (project FAMOS FP7 ICT, contract 317744 to P.B.).This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/nmeth.392

Model based learning for accelerated, limited-view 3D photoacoustic tomography

Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed-up. In this work we present a deep neural network that is specifically designed to provide high resolution 3D images from restricted photoacoustic measurements. The network is designed to represent an iterative scheme and incorporates gradient information of the data fit to compensate for limited view artefacts. Due to the high complexity of the photoacoustic forward operator, we separate training and computation of the gradient information. A suitable prior for the desired image structures is learned as part of the training. The resulting network is trained and tested on a set of segmented vessels from lung CT scans and then applied to in-vivo photoacoustic measurement data

Infrared temperature measurements on high pressure turbine blades in the Oxford Turbine Research Facility: calibration and image processing techniques

This paper presents the calibration method and error evaluation for an infrared (IR) measurement system implemented on the upgraded Oxford Turbine Research Facility (OTRF). The OTRF is a world leading turbine test facility capable of matching engine representative conditions of Reynolds and Mach numbers, non-dimensional speed and gas-to-wall temperature ratio. Infrared measurements will provide temperature maps on high-pressure (HP) turbine blades permitting full surface evaluation of metal effectiveness at engine representative conditions for the first time. The test environment presents significant challenges for accurate IR thermography measurements. High temperature of neighboring components causes high reflected radiation to reach the IR detector offsetting the measurement, whilst high blade velocity (~200 ms‑1) challenges clear image acquisition. Using a bespoke calibration facility, an IR thermography calibration procedure was assessed to evaluate the surface emissivity of the target, the transmissivity of the optical path, and the erroneous reflected radiation. A MATLAB code was developed to address the blurring problem caused by the target high speed. The results of this study will allow an advanced and highly accurate IR measurement system to be implemented in the upgraded OTRF