Clinical Academic Group for Medical Physics
Our clinical academic group conducts a range of medical physics and clinical engineering research within the hospital and university. A particular focus is on molecular imaging and molecular radiotherapy (also called theranostics). The group aims to maximise the amount of information available from these types of images either through extraction of imaging features (radiomics) or through different image reconstruction methods to improve image quality and quantification.
Our group hosts MSc students from the MSc program, of which Daniel McGowan is Course Director. These projects include analysing data from imaging studies which he is a co-investigator on such as ATOM and ARCADIAN. Some of these students have gone on to publish their work, for example Bourigalt et al. EJNMMI Research (2021) and Bourigalt et al. Scientific Reports (2022)
Our group collaborates with industry partners such as GE HealthCare and Hermes Medical Solutions.
The group has tested a range of tools which are now in widespread clinical practice. The most recent example is work on deep learning image enhancement which has been commercialised as Precision-DL by GE HealthCare
Joy Roach - Clinical Research Fellow (DPhil) (Oncology)
Anissa Alloula - DPhil student (BDI)
Lara Bonney - DPhil student (Dunn School)
Kelley Ferreira - DPhil student (Oncology)
Mona Furukawa - DPhil student (BDI)
Mark Macsuka - DPhil student (Oncology)
Charlotte Ibbeson - DPhil student (BDI)
Amie Roberts - PhD student (Cardiff University)
Hermione Warr - DPhil student (Engineering Science)
Ella Cook - MSc student (Oncology)
Ke Li - MSc student (Oncology)
Meghi Dedja - Research Scientist (OUH)
Matthew Walker - Senior Clinical Scientist (OUH)
Andrew Knapton - Clinical Scientist (Research) (OUH)
Michael Barnard - DClinSci student (OUH)
Georgina de Vries - DClinSci student (OUH)
- Mehranian A, et al. (2022) Deep learning–based time-of-flight (ToF) image enhancement of non-ToF PET scans, European Journal of Nuclear Medicine and Molecular Imaging, 49, pp. 3740-3749.
- Mehranian A, et al. (2022) Image enhancement of whole-body oncology [18F]-FDG PET scans using deep neural networks to reduce noise, European Journal of Nuclear Medicine and Molecular Imaging, 49, pp. 539-549.
- Skwarski M/McGowan DR, et al. (2021) Mitochondrial Inhibitor Atovaquone Increases Tumor Oxygenation and Inhibits Hypoxic Gene Expression in Patients with Non–Small Cell Lung Cancer, Clinical Cancer Research, 27(9), pp. 2459-2469.
- Walker, et al. (2020) Data-Driven Respiratory Gating Outperforms Device-Based Gating for Clinical 18F-FDG PET/CT, Journal of Nuclear Medicine, 61(11), pp. 1678-1683.
- McGowan, et al. (2018) Whole tumor kinetics analysis of 18F-fluoromisonidazole dynamic PET scans of non-small cell lung cancer patients, and correlations with perfusion CT blood flow , EJNMMI Research, 8, 73.
- Teoh E/McGowan DR, et al. (2015) Phantom and Clinical Evaluation of the Bayesian Penalized Likelihood Reconstruction Algorithm Q.Clear on an LYSO PET/CT System, Journal of Nuclear Medicine , 56(9), pp. 1447-1452.