Very premature children are at risk of learning difficulties in childhood that are underpinned by slow processing of information and poor everyday memory. We wish to find out how measureable these are in infancy and whether they relate to brain growth after birth, In collaboration with the UCL Institute for Women’s Health (G. Kendall, N. Marlow, N. Robertson) we are developing novel robust MR biomarkers to speed up clinical translation of neuroprotective agents which will have a positive benefit to the neurocognitive outcome of the premature children (led by Andrew Melbourne, with current PhD students Eliza Orasanu and Zach Eaton-Rosen). This project encompasses MR imaging data from both the very young (SPARKS) and from adolescents (EPICure@19) in order to better understand both the dynamic changes in brain development after premature birth and the long-term developmental consequences seen in early adulthood.
Imaging biomarkers now play a key role in both basic and applied research and are increasingly seen as important in diagnosis, drug discovery and therapeutic trials. In collaboration with the Dementia Research Centre (N. Fox, M. Rossor, J. Schott) we are developing novel imaging biomarkers for Alzheimer’s disease using MR and PET images, enabling the robust and automatic measure of disease progression and disease modification.
Based on the successful development and clinical translation of novel imaging biomarkers in dementia, we are translating our technology into multiple sclerosis (MS) in collaboration with the NMR Research Unit (ION). The aim of this collaboration is to develop novel image analysis pipeline for MS. This work is part of our overall activity to provide a consistent software platform to neuroimaging biomarkers development within the BRC Computational Imaging Infrastructure High Impact Initiative.
The Quantitative Neuroradiology Intitative (QNI) aims to automatically derive robust, objective measurements from clinical neuroimages. Our vision is to translate the best novel neuromaging methods into neuroradiology practice, to support diagnosis, follow-up and treatment. The QNI platform is a modular system for delivering automated analysis results into the reporting workflow, with an initial focus (during 2015-2016) on brain parcellation to support the diagnosis and monitoring of dementia.
While Magnetic Resonance Imaging (MRI) provides high-resolution anatomical information, Positron Emission Tomography (PET) provides functional information. The combined images have applications in neurology, oncology, or cardiology. In collaboration with S. Arridge, D. Atkinson and the UCLH Institute of Nuclear Medicine (B. Hutton), we are developing tools to reconstruct and analyse data acquired by simultaneous PET/MRI scanners.
Our research is focused on the development of new segmentation and classification techniques that allow the diagnosis and follow up of cardiac and vascular pathologies. Using our in-house segmentation algorithms, we seek to combine the information provided by multiple image modalities to improve the segmentation and posterior analysis of different structures of interest.
In collaboration with Centre of Advanced Biomedical Imaging (CABI, Lead by Professor Mark Lythgoe), our team develops imaging techniques to investigate the anatomical, structural and functional contributions of genes to human diseases and development using genetically modified mice. Our research effort in this large and multidisciplinary project is to provide image analysis capability for high-throughput high-resolution imaging (M. Modat, J. Cardoso, N. Powell, D. Ma).