Computational Collaborative Project in Synergistic PET-MR Reconstruction

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.

Medical imaging has had a phenomenal impact upon healthcare over the last 30 years, with inestimable socioeconomic benefits for the UK. Multi-modality imaging has been of particular benefit, as witnessed by the impact of PET-CT on the treatment of cancer due to its combination of functional and anatomical information. We are now on the threshold of another revolution: PET and MR have recently been combined into a single system, combining the remarkable molecular sensitivity of PET with the enormous flexibility and diverse imaging capabilities of MR. It is expected that the UK government will announce a major investment in PET-MR as part of its strategy to tackle dementia, a disease of which around 800,000 people in the UK suffer. PET-MR is uniquely suitable to study dementia and provide early diagnosis. However, there are significant challenges to be solved, many of them computational, before the full potential of PET-MR will be realised.
This is the crucial clinical context of this proposed CCP, which concerns the way medical imaging data from both the PET and MR imaging modalities is actually used to produce the end point images which are of such utility to clinical researchers, clinicians and ultimately of benefit to patients.
The following are expected beneficiaries beyond the academic community:
1) Patients
The CCP will accelerate research into novel algorithms for improved reconstructed image quality. 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. This will lead to faster translation of successful algorithms into clinical research and practice. This ultimately delivers benefit to patients, such as those suffering from cancer, heart conditions, or brain disorders. In addition, the lower dose associated with PET-MR over PET-CT, especially when combined with synergistic algorithms to optimise image quality, has the benefit of reduced radiation risk to all patients and in particular paediatrics and those undergoing repeat examinations.
2) Pharmaceutical industry and advanced imaging centres
PET and MR are increasingly used in trials for new therapeutic agents. Synergistic reconstruction promises enhanced image quality and more accurate quantification on PET-MR devices. By providing early access to these new methods, such as sophisticated motion correction techniques, the CCP will enable advanced imaging centres to assist pharmaceutical companies in exploratory drug trials by detecting smaller effects. In the long term, the platform will enable pooling of data from multiple centres / scanners, increasing statistical power in later-stage drug trials, and hence reduce the huge costs associated with testing new therapies.
3) Imaging industry
PET-MR has not yet demonstrated a clear diagnostic benefit over conventional PET-CT or MR only, restricting the market size for PET-MR systems. However, the synergistic PET-MR developments of this CCP could well showcase PET-MR capabilities in a diagnostic context, which could be highly profitable to the manufacturers of the new generation of PET-MR scanners. The publically accessible knowledge and Open Source software will benefit SMEs by reducing the cost of creating new products, for instance for pre-clinical imaging or for dedicated reconstruction hardware. Finally, the training provided to young UK researchers will enlarge the skill base for future recruitment into the imaging industry.
4) 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 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.