The aim of study is to determine if deep learning enables MR Neuropathy of peripheral nerves in shorter scan times without compromising data quality.

点击翻译按钮获取中文摘要

The overall area is development of imaging methodology and software in relation to advanced PET/MRI. It falls within the AI technologies and healthcare technologies areas of EPSRC remit. The application area is detection of neuropathy of the peripheral nerves, but the work proposed in this project is method development and optimization with respect to imaging, including use of deep learning techniques. The aim is to use new deep learning methods to push the spatial resolution boundaries for model-based diffusion-tensor imaging, a technique that can give fairly specific and non-invasive information on the microstructure of individual fibre bundles. There is currently a technical barrier with respect to low signal levels as used in established approaches. In other application areas such as spinal cord & brain there is emerging evidence that incorporating prior knowledge in the image reconstruction process using deep learning techniques can help to overcome this barrier. If successful, it will be possible to test the clinical potential of the advances in a patient cohort.