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Details of Grant 

EPSRC Reference: EP/T026693/1
Title: CCP in Synergistic Reconstruction for Biomedical Imaging
Principal Investigator: Thielemans, Professor KF
Other Investigators:
Ehrhardt, Dr MJ Tsoumpas, Dr C Atkinson, Dr D
Reader, Professor AJ Matthews, Dr JC
Researcher Co-Investigators:
Project Partners:
Bruker GE (General Electric Company) Imanova Limited
Leeds Test Objects Mediso National Physical Laboratory
Siemens
Department: Medicine
Organisation: UCL
Scheme: Standard Research - NR1
Starts: 01 April 2020 Ends: 31 March 2025 Value (£): 476,024
EPSRC Research Topic Classifications:
Medical Imaging
EPSRC Industrial Sector Classifications:
Healthcare
Related Grants:
Panel History:
Panel DatePanel NameOutcome
11 Dec 2019 CCP Networking 2019 Announced
Summary on Grant Application Form
Biomedical imaging has a crucial role in (pre)clinical research, drug development, medical diagnosis and assessment of therapy response. Often, the images 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. Increasingly, images from multiple types of systems such as Magnetic Resonance (MR), radionuclide imaging using Positron Emission Tomography (PET) or Single Photon Emission Computed Tomography (SPECT) and X-ray Computed Tomography (CT) are analysed together.

Image quality is critically dependent on image reconstruction methods. Development and testing of novel algorithms on patient data require considerable expertise and effort in software implementation. In our previous CCP on synergistic reconstruction for PET-MR, we created a network of UK and international researchers working towards integrating image reconstruction of data from integrated, simultaneous, PET-MR scanners. New multi-modality systems are now available or under development, for instance SPECT-MR or even tri-modality PET-SPECT-CT systems. At the same time, top-of-the-range multi-modality systems are expensive and instead combining single-modality scans from different time-points and systems can provide more cost-effective solutions in some cases.

Synergistic image reconstruction aims to exploit the commonalities between the data from the different modalities and time points. However, cross-modality methods are particularly challenging. We will therefore extend the network to exploit synergy in multi-modal, multi-contrast, multi-time point information for biomedical applications, concentrating on the logistical and computational aspects of synergistic image reconstruction.

The Open Source Software 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, accelerating innovative developments in image reconstruction, and ultimately enabling the possibility of synergistic image reconstruction by establishing validated pipelines for processing raw data of multiple data-sets.
Key Findings
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