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Details of Grant 

EPSRC Reference: EP/L001381/1
Title: Three-dimensional quantitative x-ray phase imaging
Principal Investigator: Olivo, Professor A
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Diamond Light Source European Synch Radiation Facility - ESRF Imperial College London
Nikon QinetiQ
Department: Medical Physics and Biomedical Eng
Organisation: UCL
Scheme: Standard Research
Starts: 01 July 2013 Ends: 30 June 2015 Value (£): 229,086
EPSRC Research Topic Classifications:
Medical Imaging
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
22 May 2013 Developing Leaders Meeting - CAF Announced
Summary on Grant Application Form
X-Ray Phase Contrast Imaging (XPCI) is one of the most exciting new methods emerged in x-ray science over recent years. It generates image contrast based on refraction and interference phenomena rather than x-ray attenuation, which enhances the visibility of all details in an image. Moreover, features classically considered "x-ray invisible" can be detected by XPCI. This has transformative power in many applications, from medicine to industrial testing, through biology, cultural heritage, material science, security inspections, and many other fields. It is worth remembering that the use of x-rays is all pervasive, both in science and in society, and all areas where x-ray imaging is used can strongly benefit from XPCI.

The problem up to a few years ago was that XPCI was considered restricted to large, specialized and expensive facilities called synchrotrons - only approximately 50 of which exist in the world. However, my research group has recently solved this problem by developing a method that enables XPCI to be performed with conventional x-ray sources, like those used in hospitals. This will allow taking XPCI out of ultra-specialized labs and into "real-world" applications, and negotiations with various companies are indeed underway to take the technology into commercial exploitation.

This project aims at developing the next generation of this technology. At the moment, our XPCI method works only in 2D, "planar" imaging applications. Although this is useful in itself, and is effectively employed in some areas (e.g. mammography or baggage scanning at airports), many other applications require the full 3D ("tomographic") reconstruction of the imaged sample. This is a well known problem in medicine, where for example some diseases cannot be diagnosed with a simple "x-ray" but require a CT (computed tomography) scan; the same principle also applies to many other areas, where full 3D knowledge of the sample is essential to the decision-making process that follows. Examples are in the development of new drugs, the effect of which is often assessed through high-resolution 3D images of the small animals on which they are tested, or in the testing of sophisticated mechanical parts or of new "composite" materials.

This project therefore aims at the development of a quantitative, full 3D version of our XPCI method. This requires overcoming a number of obstacles, some of which have a very technical nature. For example, in order to make x-ray imaging systems sensitive to x-ray phase, we use masks, which cover parts of the imaged object. Although this does not create a problem in planar imaging, because the portions of the sample which are covered are smaller than the smallest element the imaging system can resolve (the detector pixel), it does result in significant artifacts when a 3D volume is reconstructed, because of a problem known as undersampling. This is also encountered in other disciplines (for example nuclear medicine), and researchers have developed new, more sophisticated reconstruction tools which allow solving or at least mitigating this problem. We therefore plan to adapt these new reconstruction tools to the specific requirements of our XPCI method, so that reliable and quantitative 3D "phase" reconstruction can be performed.

Initially, this will be based on an extensive simulation phase during which different algorithms will be tested on various datasets, which will enable identifying the most promising ones. This will be followed by an experimental phase in which we will test the algorithms on real experimental data: this will allow selecting the best solution and fine-tuning it. Finally, there will be a demonstration phase in which the optimized 3D method will be applied to real scientific problems, among which for example the 3D visualization of small damage in articular cartilage (notoriously invisible to conventional x-ray methods), or of intrusion/defects in new-generation composite materials.
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