Using AI to enable imaging of exotic radionuclides for Molecular Radiotherapy
Lead Research Organisation:
University College London
Department Name: Medical Physics and Biomedical Eng
Abstract
1) Brief description of the context of the research including potential impact
Molecular Radiotherapy (MRT) is a rapidly growing cancer treatment modality where molecules that bind to cancerous cells are labelled with a radionuclide and injected into the patient for targeted delivery of radiation. Multimodality imaging using CT and nuclear imaging (SPECT/PET) can be performed to personalise the treatment doses as well as quantify absorbed doses to the tumours and organs at risk (OAR). There is increased interest in the use of exotic radionuclides including alpha-emitters such as Actinium-225 that have the potential to increase dose to the tumours while minimising impact on OARs, especially when combined with novel molecules that specifically target receptors or proteins in the tumours.
2) Aims and Objectives
The aim is to develop novel image reconstruction methods to enable quantitative imaging of exotic radionuclides with low levels of gamma emissions, focusing on alpha-emitters such as Actinium-225. The approach will integrate physics-based machine learning methods into the reconstruction process, combining the use of advanced modelling of the imaging physics, data from planning scans with state-of-the-art Deep Learning. Optimisation of acquisition protocols will be investigated. Test data will include Monte Carlo simulations, phantom scans (acquired as part of this project) as well as potentially patient data obtained as part of a clinical trial.
3) Novelty of Research Methodology
Imaging these novel radionuclides is very challenging due to their low abundance of generated gamma photons. Machine learning techniques are revolutionising the field of image reconstruction in general, and for ultra-low signal data in particular.
4) Alignment to EPSRC's strategies and research areas
This project is aligned with the EPSRC strategy on "discovering and accelerating the development of new interventions" and "improving population health" by optimising imaging and dosimetry for theranostics using MRT.
5) Any companies or collaborators involved
Blue Earth Therapeutics Ltd
National Physical Laboratory (NPL)
Molecular Radiotherapy (MRT) is a rapidly growing cancer treatment modality where molecules that bind to cancerous cells are labelled with a radionuclide and injected into the patient for targeted delivery of radiation. Multimodality imaging using CT and nuclear imaging (SPECT/PET) can be performed to personalise the treatment doses as well as quantify absorbed doses to the tumours and organs at risk (OAR). There is increased interest in the use of exotic radionuclides including alpha-emitters such as Actinium-225 that have the potential to increase dose to the tumours while minimising impact on OARs, especially when combined with novel molecules that specifically target receptors or proteins in the tumours.
2) Aims and Objectives
The aim is to develop novel image reconstruction methods to enable quantitative imaging of exotic radionuclides with low levels of gamma emissions, focusing on alpha-emitters such as Actinium-225. The approach will integrate physics-based machine learning methods into the reconstruction process, combining the use of advanced modelling of the imaging physics, data from planning scans with state-of-the-art Deep Learning. Optimisation of acquisition protocols will be investigated. Test data will include Monte Carlo simulations, phantom scans (acquired as part of this project) as well as potentially patient data obtained as part of a clinical trial.
3) Novelty of Research Methodology
Imaging these novel radionuclides is very challenging due to their low abundance of generated gamma photons. Machine learning techniques are revolutionising the field of image reconstruction in general, and for ultra-low signal data in particular.
4) Alignment to EPSRC's strategies and research areas
This project is aligned with the EPSRC strategy on "discovering and accelerating the development of new interventions" and "improving population health" by optimising imaging and dosimetry for theranostics using MRT.
5) Any companies or collaborators involved
Blue Earth Therapeutics Ltd
National Physical Laboratory (NPL)
People |
ORCID iD |
| Kris Thielemans (Primary Supervisor) | |
| Catherine Gascoigne (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/S021930/1 | 01/10/2019 | 31/03/2028 | |||
| 2874500 | Studentship | EP/S021930/1 | 01/10/2023 | 30/09/2027 | Catherine Gascoigne |