CCP in Synergistic Reconstruction for Biomedical Imaging

Lead Research Organisation: University College London
Department Name: Medicine


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.

Planned Impact

Medical imaging has had significant impact upon healthcare over the last 40 years. Multi-modality imaging has been of particular benefit, for example as witnessed by the impact of PET-CT on the treatment of cancer due to its combination of functional and anatomical information. Now, the forefront of imaging research focuses on the opportunities that combined PET-MR systems can provide. In the UK, eight scanners have been installed with primary focus to study dementias, both as a tool to understand disease processes and to can provide early diagnosis.

Imaging also transformed preclinical research by providing biology and pharmacology researchers the building blocks to fundamental knowledge. Successful imaging techniques in this arena beyond PET-CT, include also SPECT-CT and multi-sequence MRI which allow to study a wide range of diseases at multiple time points during their evolution or therapeutic processes.

Our previous CCP network developed open source software for PET-MR data and in this renewal, we plan to integrate this more closely with the now-installed systems and expand to other joint imaging modalities such as SPECT-CT and multi-sequence MRI. The proposed network will strongly benefit its academic partners and is open to all. The following are expected beneficiaries beyond the academic community:

- Patients: The CCP will accelerate research into novel algorithms for improved reconstructed image quality, faster scanning and dose reduction. Furthermore, researchers will be able to use our software platform to evaluate new algorithms on a much larger number of patient data sets than currently possible. In collaboration with clinical researchers, we will establish proof-of-concept processing pipelines for specific studies. This will enable us to bridge the gap from theory to translation of successful algorithms into clinical research and practice. This delivers benefit to patients, such as those suffering from cancer, heart conditions, or brain disorders.

- Pharmaceutical industry & advanced imaging centres: PET and MR are increasingly used in trials for new therapeutic agents and in understanding of disease. Synergistic reconstruction promises enhanced image quality and more accurate quantification as well as the synthesis of information from multiple imaging time points and modalities. In the long term, these improvements will help increase statistical power at early-stage drug trials, and hence reduce the huge costs associated with testing new therapies.

- Imaging industry: The synergistic developments of this CCP could showcase capabilities of multimodality imaging in a diagnostic context, which could be highly profitable to the manufacturers of a new generation of scanners. The publicly accessible knowledge and Open Source software will reduce the cost of creating new products, for instance for preclinical imaging. Finally, the training provided to young UK researchers will enlarge the skill base for future recruitment into the imaging industry.

- Developing researchers: The training of use in standardised UK-wide software and its exploitation for modelling and algorithms to respond to the needs of clinical researchers (academia and industry) will help the development of interdisciplinary researchers. The seminars and training from leading experts will enhance the research skills of participants in the network, providing ample opportunity to pursue careers in, for example, advanced imaging sciences, including fields beyond the medical arena.


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