Ewch i’r prif gynnwys

Healthcare technologies

Multi-scale, multi-parametric data acquisition and analysis for healthcare.


Our research can be described under the following headings:

Magnetic resonance imaging and spectroscopy (MRI/S)

Magnetic resonance spectroscopy and imaging holds the promise to identify specific metabolites using techniques from quantum control which are commonly used in Nuclear Magnetic Resonance (to identify chemical composition, molecule structures, etc.)

However, practical considerations of imaging and spectroscopy in patients (complex biological environments at room temperature compared to the highly controlled environments of Nuclear Magnetic Resonance) cause many uncertainties which makes it hard to obtain reliable data or any usable data at all.

Recent approaches from robust quantum control deep learning and advances in sensing technology help to improve these techniques to obtain more reliable information about specific metabolites. This in turn can help to identify biomarkers for early diagnosis of diseases such as cancer and dementia. It also builds the basis to obtain quantified data to build functional models of biochemical processes.

We are developing control techniques to compute custom magnetic resonance imaging and spectroscopy pulse sequences, based on user-defined targets, such as quantifying specific metabolites. Quantification of the data acquired with such techniques is done using traditional spectral analysis techniques as well as deep learning.

Medical image segmentation

We work on medical image segmentation using deep learning. We work on techniques for brain tumour and stroke lesion segmentation and develop techniques to detect early signs of Alzheimer's disease based on MRI, CT and PET images. We further devise methods for computer-aided diagnosis of early-stage prostate cancer and lung cancer diagnosis and treatment planning based on CT.

Human task performance

We aim to develop computational models that can automatically and reliably predict the task performance of the radiologist in the interpretation (such as lesion detection) of medical images. These models will be used either to support the human to augment diagnostic efficiency, or to train the human towards improved diagnostic accuracy.

Cwrdd â'r tîm

Lead researcher

Staff academaidd

Dr Jing Wu

Dr Jing Wu

+44 (0)2920 688810

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