Medical imaging, for example MRI, is generally considered to be a slow imaging method. This affects the number of patients that can be scanned but in some cases it also affects diagnosis, e.g. imaging of the abdomen which needs to be done in a breath-hold to reduce motion induced artefacts. Sparse acquisition allows to reduce scanning time, but also to improve spatial resolution. The latter can bring benefits in a number of areas including diagnosis certain hepatobiliary diseases such as early cholangiopathies where current methods are unable to resolve the fine details that are essential to making a clinical diagnosis. These techniques will also help in providing high-resolution structural images of the brain which are essential to aid in the diagnosis and monitoring of patients with a range of neurodegenerative disorders.
In electron tomography, imaging of biological samples and nanoparticles with high resolution would offer critical advantages. The former is essential for example in the characterisation of virus structures, while the latter can be vital for example in the development of advanced nanoparticles for drug delivery or photo-thermal cancer treatment. However, both biological samples and nanoparticles are beam sensitive. This means that at a high resolution only low electron dosage can be used, which yields noisy images.
We are developing techniques for sparse acquisitions with direct applications to MRI and electron tomography in order to reduce the scanning time and at the same time to improve image resolution and quality. Achieving this requires an understanding of the mathematical compressed sampling phenomena in such applications as well as of the physical setup and the conditions it imposes in the mathematical models.
This project could appeal to a candidate with experience and interests in compressed sensing, sampling theory, inverse problems, sparse regularisation and image processing.