Abstract: Positron Emission Tomography (PET) is the most specific and sensitive method for imaging and quantitatively measuring molecular interactions in humans and animals. PET has improved healthcare both in terms of early diagnosis and successful guidance of therapy of numerous diseases. This powerful ‘molecular imaging’ technique provides absolute quantitative measurements of the temporal and spatial distribution of highly specific positron emitting radiotracers, which are administered into the body, yielding insights to general molecular pathways. The ability of PET to advance clinical research and healthcare has been proven, for example, with ground breaking discoveries in the human brain and its well established clinical use in oncology and cardiology. However, the conventional PET scanning technology is limited because the scanner covers only 15% of the patient’s body. This means that: Only a small region of the body can be studied at one time Detection is highly inefficient: less than 6% of the total radioactive signal emitted from the body is recorded Non-negligible amounts of radiotracer are administered, thus important potential clinical applications are not permitted due to radiation dose considerations Multi-organ dynamic imaging is difficult and inefficient: any ‘full-body’ survey of a molecular target is restricted to combining multiple images of the body
<p>A novel approach is necessary to address these limitations and enable new cardiovascular applications for PET. This project will allow reconstruction of three-dimensional images with sufficient quality even with ten times lower amounts of injected radioactivity. Furthermore, the proposed design will be optimised for cardiovascular patients as envisaged in the personalised medicine paradigm.</p> <p><strong>Mini Project 1: (1 page) list supervising staff member and training benefits</strong></p> <p><strong>Supervisor: </strong>Dr Charalampos Tsoumpas</p> <p><strong>Training Benefits: </strong>The student will develop different skills and particularly the basics of image reconstruction as well as how to use software and programming languages that will be essential for the PhD project. Among these are: SIRF, STIR , Python, LINUX. Furthermore, the student will join the EPSRC sponsored “Collaborative Computational Project in Positron Emission Tomography and Magnetic Resonance imaging”: https://www.ccppetmr.ac.uk, and the users’ and developers community of STIR library: http://stir.sourceforge.net and attend the regular scientific and software development meetings.</p> <p>The student will investigate different reconstruction algorithms that are available in STIR library. They will install a dedicated virtual box on a computer. This virtual box will have preinstalled packages of Ubuntu Linux, Python, STIR and SIRF. The latter will include a list of examples and exercises which the student will execute. Then, the student will collaborate with PhD students who are members of the lab and will develop additional exercises in STIR library. These additional examples will include ToF reconstruction with different algorithms and different ToF settings. Another example will include exercises for PET scanners with big detector gaps.</p> <p>The proposed timeframe is December to January:</p> <p>1) Installation of the virtual box and the accompanying packages: 1 week</p> <p>2) Execution of existing exercises in STIR and SIRF packages: 1 week</p> <p>3) Development of ToF exercise: 2 weeks</p> <p>4) Development of exercises for scanners with big gaps: 2 weeks</p> <p><strong>References:</strong></p> <p> K. Thielemans, C. Tsoumpas, S. Mustafovic, T. Beisel, P. Aguiar, N. Dikaios, M. W. Jacobson (2012) STIR: Software for tomographic image reconstruction release 2, Phys. Med. Biol. 57 867-883.</p> <p><strong>Mini Project 2: (1 page) list supervising staff member and training benefits Supervisor: </strong>Dr George Kastis, <em>Academy of Athens</em></p> <p><strong>Training Benefits: </strong>The student will learn basic mathematical concepts for analytical image reconstruction and will develop new C++ code in STIR library .</p> <p>The spline reconstruction technique (SRT) is a new, fast, analytic algorithm developed at the Centre of Pure and Applied Mathematics of the Academy of Athens in Collaboration with Professor Fokas who is based at the University of Cambridge . It is based on a novel numerical implementation of a certain analytic representation of the inverse Radon transform . It involves the numerical evaluation of the Hilbert transform of the sinogram via an approximation in terms of “custom-made” cubic splines. SRT provides images of higher resolution, higher contrast, and lower bias than filtered back projection (FBP). This improvement of SRT in comparison with FBP is related to the fact that SRT is formulated in the physical space, whereas FBP is formulated in the Fourier space.</p> <p>In a recent study by Conti et al. , it was demonstrated that ToF FBP has improved performance over ToF Ordered Subsets Expectation Maximisation (OSEM) algorithm, which is the algorithm used predominantly in the clinical systems. In particular, it was shown that the ToF gain in ToF FBP can be used as a sensitivity amplifier, reducing the number of counts necessary to produce an image of the same characteristics. On the other hand, it was observed that there were some limitations in the ToF gain of ToF OSEM, especially at low count cases. ToF can be applied to SRT in a manner similar to FBP. Specifically, ToF can be applied by confidence weighting each projection during backward projection for each TOF bin.</p> <p>Thus, the perspective student in a two-month lab placement in Athens, Greece, could:</p> <p>1a) Familiarise with the SRT algorithm in STIR library (1 week)</p> <p>1b) Prepare SRT exercises as part of STIR teaching material (1 week)</p> <p>2) Develop ToF reconstruction for SRT (2 weeks)</p> <p>3) Explore the performance of 2D SRT with gaps (4 weeks)</p> <p><strong>References:</strong></p> <p> K. Thielemans, C. Tsoumpas, S. Mustafovic, T. Beisel, P. Aguiar, N. Dikaios, M. W. Jacobson (2012) STIR: Software for tomographic image reconstruction release 2, Phys. Med. Biol. 57 867-883.</p> <p> G. A. Kastis, A. Gaitanis, A. Samartzis, A.S. Fokas (2015) The SRT reconstruction algorithm for semi-quantification in PET imaging, Med. Phys. 42 5970-5982.</p> <p> A. S. Fokas, A. Iserles, V. Marinakis (2006) Reconstruction algorithm for single photon emission computed tomography and its numerical implementation, J. R. Soc. Interface 3 45-54.</p> <p> M. Conti, L. Eriksson, V. Westerwoudt (2013) Estimating image quality for future generations of TOF PET scanners, IEEE Trans. Nucl. Sci. 60(1), 87–94.</p>
<p>Please note these are not standalone projects and applicants must apply to the PhD academy directly.</p> <p>Applications can be made at any time. To apply for this project applicants should complete a<a href="https://medicinehealth.leeds.ac.uk/downloads/download/129/faculty_graduate_school_-_application_form"> Faculty Application Form</a> and send this alongside a full academic CV, degree transcripts (or marks so far if still studying) and degree certificates to the Faculty Graduate School <a href="mailto:email@example.com">firstname.lastname@example.org</a></p> <p>We also require 2 academic references to support your application. Please ask your referees to send these <a href="https://medicinehealth.leeds.ac.uk/downloads/download/130/faculty_graduate_school_-_scholarship_reference_form">references</a> on your behalf, directly to <a href="mailto:email@example.com">firstname.lastname@example.org</a></p> <p>If you have already applied for other projects using the Faculty Application Form this academic session you do not need to complete this form again. Instead you should email <a href="mailto:email@example.com">firstname.lastname@example.org</a> to inform us you would like to be considered for this project.</p> <p>If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below).</p> <p><em>We welcome applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.</em></p>
A degree in biological sciences, dentistry, medicine, midwifery, nursing, psychology or a good honours degree in a subject relevant to the research topic. A Masters degree in a relevant subject may also be required in some areas of the Faculty. For entry requirements for all other research degrees we offer, please contact us.
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