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Details of Grant 

EPSRC Reference: EP/M022587/1
Title: Computational Collaborative Project in Synergistic PET-MR Reconstruction
Principal Investigator: Thielemans, Dr KF
Other Investigators:
Matthews, Dr JC Reader, Professor Atkinson, Dr D
Prieto, Dr C
Researcher Co-Investigators:
Project Partners:
Imanova Limited
Department: Medicine
Organisation: UCL
Scheme: Standard Research - NR1
Starts: 01 April 2015 Ends: 31 March 2020 Value (£): 261,180
EPSRC Research Topic Classifications:
Med.Instrument.Device& Equip. Medical Imaging
EPSRC Industrial Sector Classifications:
Healthcare
Related Grants:
Panel History:
Panel DatePanel NameOutcome
24 Nov 2014 CCP Networking Call Announced
Summary on Grant Application Form
Magnetic Resonance (MR) and radionuclide imaging using Positron Emission Tomography (PET) have established roles in medical diagnosis, clinical research and drug development. In recognition of the complementary nature of these two modalities, which have historically been used separately, integrated PET-MR scanners have been designed and marketed by manufacturers. These devices open-up exciting avenues to exploit the synergy between these two modalities in many areas, including dementia, cardiology, and investigation of dynamic processes such as the uptake of contrast agents by tumours.

Both modalities are tomographic: from the measured data, (stacks of) slices or volumes representing anatomical and functional properties of the patient can be reconstructed using sophisticated algorithms. Image quality is critically dependent on image reconstruction methods. Development and testing of novel algorithms on patient data requires considerable expertise and effort in software implementation. We will establish a new Collaborative Computational Project (CCP) to connect researchers working at different sites and on the different modalities of PET and/or MR in the area of image reconstruction, concentrating on the logistical and computational aspects of integrated PET-MR.

The platform to be provided by this CCP will be an enabling technology which removes the frequent obstacles encountered when working with the raw medical imaging datasets acquired by PET and MR scanners. It will be straightforward to work with data in a standardized format, massively aiding and accelerating innovative developments in image reconstruction and processing for PET-MR, and ultimately enabling the possibility of synergistic image reconstruction.

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