Heart tissue can be imaged in real-time during keyhole procedures using a new optical ultrasound needle developed by researchers at UCL and Queen Mary University of London (QMUL). 

The revolutionary technology has been successfully used for minimally invasive heart surgery in pigs, giving an unprecedented, high-resolution view of soft tissues up to 2.5 cm in front of the instrument, inside the body. 


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We are delighted to congratulate Carole Sudre on securing a well-deserved Biomedical Junior Fellowship from Alzheimer’s Society. This fellowship aims to better understand how white matter and neurovascular diseases influence the development and progression of dementia.

Carole completed her studies at Ecole Polytechnique, France with a major in Applied Mathematics and then a Msc in Biomedical Engineering from ETH Zurich. Following a short internship in the Translational Imaging Group (TIG), UCL in 2011, she returned to UCL to undertake her PhD with TIG and the Dementia Research Centre.

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A free workshop led by Dr Prashant Jha taking place during the November reading week.


In July 2017 we were delighted to welcome Dr Dan Marcus from Washington University to work with the Translational Imaging Group on a 5 month research placement.


Dan is Director of the Neuroinformatics Research Group (NRG), the Neuroimaging Informatics and Analysis Centre and the Director of XNAT from the NRG lab, all at Washington University.

Dan is one of our long-term collaborators and his groups XNAT system is being used for much of the groups research output. XNAT allows us to build and test image processing pipelines which enable the development of robust code for novel algorithms which help diagnose and treat conditions such as Alzheimer’s, epilepsy and more.

XNAT is an imaging informatics platform first released in 2004, and we wanted to hear more from the researcher who helped to develop this pioneering tool responsible for over 13 years of safe medical data sharing.


We are excited to announce that NiftyNet, the first open-source deep-learning software library dedicated to medical imaging, has been released and is available for anyone to use.


NiftyNet is a convolutional neural networks (CNNS) platform for research in medical image analysis and image-guided therapy, and its modular structure is designed for sharing networks and pre-trained models. It currently supports medical image segmentation and generative adversarial networks.


Screen_Shot_2017-09-04_at_16.16.45.pngGIFT-Surg had a successful meeting presenting work at the International Federation of Placenta Associations (IFPA) meeting this year in Manchester presenting work on both placental microCT and placental MRI.

The cooperation between clinicians and engineers is a key condition to delivering healthcare engineering innovations. Dr Carole Sudre (UCL, London) and Dr Beatriz Gómez-Ansón (Hospital de Sant Pau, Barcelona) teamed up to develop more efficient medical imaging assessment.


From raw data to novel diagnosis techniques 

Big data and machine learning are often seen as having great potential for innovation in the medical sector. But what’s the path from building databases to revolutionising medical diagnosis?



The Translational Imaging Group, in collaboration with the Queen's Square Multiple Sclerosis Centre, recently published work in Magnetic Resonance in Medicine on spinal cord T1 mapping.