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Improving absorbed dose prediction for Molecular Radiotherapy with Machine Learning

   Institute of Nuclear Medicine

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  Prof Kris Thielemans, Dr Ana Denis-Bacelar  No more applications being accepted  Funded PhD Project (UK Students Only)

About the Project

The successful candidate will join the UCL CDT in Intelligent, Integrated Imaging in Healthcare (i4health) cohort as well as the postgraduate institute (PGI) at NPL, and benefit from the activities and events organised by these centres. The student will be primarily located at INM in the University College Hospital, near the UCL Bloomsbury Campus. Imaging facilities include SPECT-CT, PET-CT and PET-MRI scanners. A substantial proportion of the student’s time will be spent at the NPL Teddington Campus for collaboration and experiments.

UCL Institute of Nuclear Medicine: https://www.ucl.ac.uk/nuclear-medicine/research/medicalphysics

National Physical Laboratory: https://www.npl.co.uk/medical-physics/nuclear-medicine


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 quantify absorbed doses to the tumours and organs at risk. Nevertheless, personalised treatments have not yet made it into routine clinical use. This is partly due to a lack of standardisation and knowledge on the uncertainties in dosimetry calculations as well as increased resources needed, leading to a lack of evidence from large randomised clinical trials.

Machine learning techniques are under investigation for nuclear medicine dosimetry but have not yet been implemented clinically due to the lack of validation and knowledge on their potential benefits. In particular, deep learning models have been proposed as a way to increase the speed of the absorbed dose calculation step and decrease the need for imaging resources.

Research Aims

The aim is to contribute towards the implementation of personalised treatment planning into clinical practice through the provision of accurate voxel-wise absorbed dose maps from SPECT and CT images. The student will compare conventional methods for absorbed dose map generation with state-of-the-art Machine Learning methods. As well as accuracy of the various methods, sensitivity to acquisition protocol and reconstruction method will be investigated, with a view to optimise protocols.

The project will include the development and validation of a simulation framework for generating a realistic ground truth dataset using open source software GATESTIR / OpenDose / Dositest,  based on existing clinical data from theragnostic studies at UCLH. Some experiments using phantoms will have to be performed at UCLH and NPL.

How to apply:

Candidates must meet the UCL graduate entry requirements which include holding at least an upper second-class degree or equivalent qualifications in a relevant subject area such as physics, biomedical engineering, computer science or applied mathematics. A Master’s degree in a relevant discipline and additional research experience would be an advantage. A Master’s degree in a relevant discipline, additional research and/or programming experience would be an advantage. Depending on experience the student will be entered into either a 4-year PhD or a 1-year MRes+3-year PhD programme.

Please complete the following steps to apply.

  • Send an expression of interest and current CV to Prof Kris Thielemans and [Email Address Removed]. Please use the subject title: Project Code 22012
  • Make a formal application to via the UCL application portal https://www.ucl.ac.uk/prospective-students/graduate/apply . Please select the programme code MPhil Medical Imaging RRDMEISING01 and enter Improving absorbed dose prediction for Molecular Radiotherapy with Machine Learning 22012 under ‘Name of Award 1’.

Application Deadline - Sunday 1 May 2022

Application Process:

  • After the deadline, all applicants that specified Project 22012 and with a Portico application will be considered for interview.
  • Candidates will normally be invited for interview within two-weeks of the deadline. If you have not been contacted within this time-period, you have unfortunately not been successful in being shortlisted.
  • The interview panel will normally consist of the supervision team on the project and the CDT Director.
  • The interview will normally consist of a short presentation (5-10mins) by the candidate followed by questions from a panel.
  • The successful candidate will be informed by email and given a week to confirm whether they wish to accept the PhD place and funding.
  • Note that applications without specifying the project they are applying for and/or making a formal Portico application will be automatically rejected.
  • Once accepted, a formal UCL offer of admission will be sent to the applicant as well as an offer of studentship funding.

Funding Notes

4-year funded PhD studentship is available in the UCL Institute of Nuclear Medicine, a joint institute between UCL and UCLH, in collaboration with The Medical Physics group of the UK National Physical Laboratory. Funding will be at least the UCL minimum. Stipend details can be found here.
This studentship is strictly for those who pay Home fees. We cannot consider overseas students.
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