The project aims to provide and validate new approaches for machine learning in prioritised task scheduled working queues in mega-kernels executed on single instruction multiple data (SIMD) computing units. A working demonstrator using a complex real world algorithm for motion correction in fetal MRI will be used and validated on real, motion corrupted MRI data. With the proposed learning strategies, it is expected to provide accurate reconstructions of the fetal anatomy in-utero and a general framework for the parallelisation of otherwise highly complex computational methods. The fundamental GPU computing methods provide a versatile framework, which will be extended with machine learning methods to automatically and intelligently define task priorities.
The project will develop image analysis technologies to quantify vascular disease burden in patients with CNS diseases to support more accurate differential diagnosis of CNS diseases in the clinic by providing clinicians with information that is clinically actionable and easy to interpret. This will also enable the design and execution of better targeted clinical trials.
This programme aims to develop intelligent techniques for the integrated acquisition, reconstruction and analysis of medical images. This will allow the transformation of a serial process of acquisition, reconstruction and analysis into an integrated pipeline with feedback between the different stages of the process.
iFIND (intelligent Fetal Imaging and Diagnosis) is an exciting multi-disciplinary project which proposes to improve antenatal ultrasound scanning. This will be done by developing new computer guided ultrasound technologies that will allow screening of fetal abnormalities in an automated and uniform fashion.
CENTER-TBI is a large European project that aims to improve the care for patients with Traumatic Brain Injury (TBI). It brings the newest technologies and many of the world's leading TBI experts together in a much needed effort to tackle the silent epidemic of TBI. International and multidisciplinary collaboration are key elements to the project in which past dogmas will be left behind and innovative approaches undertaken.
PredicND is a 4-year, 4.2 M€ European project focusing on developing tools and means for earlier, evidence-based diagnosis of a range of Neurodegenerative diseases.
This project aims to develop novel approaches for the analysis of the motion of the heart from cardiac MR imaging. Using machine learning will allow us to derive motion-based biomarkers that can be used for diagnostics.
The VPH-Dare@IT project aims to provide a systematic, multifactorial and multiscale modelling approach to understanding dementia onset and progression and enable more objective, earlier, predictive and individualised diagnoses and prognoses of dementias to cope with the challenge of an ageing society.
The Developing Human Connectome Project (dHCP) aims to produce a map of the structural and functional connectome of the developing brain. The project is funded by an ERC Synergy Grant.
PredictAD is an EU funded research project which will develop imaging biomarkers (MRI, PET FDG and PET PIB), electrical brain activity measurement and blood based markers (proteomics and metabolomics). We will also develop methods for how to combine data from different biomarkers to allow improved detection of disease progression and treatment efficacy monitoring.<br />
Traumatic brain injury (TBI) occurs when a sudden trauma causes damage to the brain – it is a major health problem and the most common cause of permanent disability in people under the age of 40 years. The TBIcare project aims to provide an objective and evidence-based solution for management of TBI by improving diagnostics and treatment decisions for an individual patient.