Student: James Bland
1st supervisor: Andrew Reader, King’s College London
2nd supervisor: Claudia Prieto, King’s College London
Simultaneous acquisition of medical image data for looking at the function and anatomy of the human body is now possible thanks to the arrival of simultaneous PET-MR imaging technology, with scanners such as the Siemens Biograph mMR. Both PET and MR provide powerful medical imaging capabilities for the brain, for cancer and for the heart, and their combination into a single scanner has been an exciting advance. St Thomas’ hospital in London now hosts the second such system in the UK. The advantages compared to the conventional method (PET-CT) are notable: reduced radiation dose, acquisition of data with the patient in the same position, reduced scan time compared to separate acquisitions, and availability of multi-parametric anatomical and functional data from MR. The process of image reconstruction is critical to these imaging methods, as it has significant impact on the noise level of end-point images.
The field of PET image reconstruction has developed over the last four decades, covering Fourier methods all the way through to the more recent statistical parameter estimation methods (such as maximum likelihood (ML)) . Accurate modelling of the medical imaging scanner and the noise in the raw data, along with inclusion of prior information  has notably improved image quality. These improvements in image quality help research into many diseases, as well as assist in improved diagnoses for patients.
Likewise, the field of MR reconstruction has developed from Fourier methods using complete measured data, and has progressed onwards to handling and better modelling of incomplete data  in order to accelerate image acquisition, for faster patient throughput and greater patient comfort.
This project seeks to advance both the PET and MR image reconstruction fields by, where possible and appropriate, unifying the developments achieved thus far into a common image reconstruction framework. In particular, such a framework would provide scope for estimation of parameters which depend jointly on both the PET and MR data resulting in the potential of synergistic reconstruction of both datasets. Such developments will, just as with former advances, provide researchers with improved images of the brain, which is the particular application focus of this research project. Ultimately these methods will also translate into improved clinical imaging capabilities, delivering diagnostic benefits to patients suffering from brain disorders, including dementia, epilepsy and depression.
 Reader, A.J. and Verhaeghe, J. (2014) 4D image reconstruction for emission tomography. In press Phys Med Biol
 Bai, B., et al. (2013) Magnetic Resonance-Guided Positron Emission Tomography Image
Reconstruction. Seminars in Nuclear Medicine 43, 30-44
 Fessler, J.A. (2010) Model-Based Image Reconstruction for MRI. IEEE Signal Processing Magazine 27, 81-89