The development and implementation of Multi-Spectral CT imaging techniques and artificial intelligence methods within stereotactic radiosurgery treatment planning for oligometastases in the brain and intracranial meningioma
Dr D Darambara
Dr L Welsh
No more applications being accepted
Funded PhD Project (Students Worldwide)
The Institute of Cancer Research, London, is one of the world’s most influential cancer research institutes. We are committed to attracting and developing the best minds in the world to join us in our mission—to make the discoveries that defeat cancer.
The development and implementation of Multi-Spectral CT imaging techniques and artificial intelligence methods within stereotactic radiosurgery treatment planning for oligometastases in the brain and intracranial meningiomas
Multi Spectral CT imaging is gaining prominence within the medical sector. The ability to examine patients with high and low energy x-ray spectra in combination with increasingly more advanced post-processing algorithms results in a state-of-the-art technique. Within the X-ray imaging domain, this results in soft tissues being differentiated with an increasingly greater knowledge, which could have tremendous potential when applied to imaging in radiotherapy treatment planning.
Machine learning (ML), a branch of artificial intelligence, is a rapidly emerging field within the health care profession. Machine learning algorithms can perform repetitive well defined tasks consistently; reduce clinical interpretation time and discover patterns well beyond human perception.
Combining these two emerging techniques should push the boundaries of what is currently achievable in terms of image quality and image analysis. We aspire to apply the aforementioned techniques within a neuro-oncology setting, specifically for stereotactic radiosurgery treatment planning for oligometastases in the brain and intracranial meningiomas. It is a collaborative project between ICR, RMH and Imperial College.
Imaging protocols in neuro-oncology for radiotherapy treatment planning are typically multimodal. Magnetic Resonance (MR) sequences generally provide the spatial information and soft tissue contrast necessary to delineate Gross Tumour Volumes (GTV) and Organs At Risk (OAR). Electron density data, necessary for dosimetry, is typically obtained from a corresponding conventional CT image. Spatial uncertainties are inevitable when registering images from different modalities; these uncertainties will compound already significant GTV/ OAR delineation uncertainties that represent a leading source of error within the radiotherapy workflow. We want to explore the idea that these uncertainties can be reduced by implementing a single-modality state-of-the-art imaging technique and/or by developing and implementing Machine Learning techniques to aid with image segmentation.
Any imaging techniques developed in neuro-oncology will be highly translatable into other anatomical regions and will set the foundation for the further growth of multi-spectral CT imaging and photon-counting technologies within radiotherapy and the healthcare system generally. Moreover, multi-spectral CT imaging is an exceptionally important resource in Proton-beam-therapy due to the proton Bragg-peak and the radiosensitive nature of neurological tissues. The clinical need for ML is growing rapidly and in harmony with the development of state-of-the-art novel imaging techniques, and therefore, the project has great potential for real advances in this field.
1. Multi-spectral x-ray imaging
2. Artificial intelligence
4. Image-guided radiotherapy
5. Stereotactic radiosurgery
Students receive an annual stipend, currently £21,000 per annum, as well as having tuition fees (both UK/EU and overseas) and project costs paid for the four-year duration. We are open to applications from any eligible candidates and are committed to attracting and developing the best minds in the world.
See icr.ac.uk/phds to apply
Applications close 11:55pm UK time on Sunday 17th November 2019
Candidates must have a first class or upper second class honours BSc Honours/MSc in a relevant subject