JUNO SIGNED
Joint Volumetric Reconstruction and Automated Analysis of the Fetal Heart from Cardiovascular Magnetic Resonance Images
Total Cost €
0EC-Contrib. €
0Partnership
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Project "JUNO" data sheet
The following table provides information about the project.
| Coordinator |
IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE
Organization address contact info |
| Coordinator Country | United Kingdom [UK] |
| Project website | https://www.doc.ic.ac.uk/ |
| Total cost | 183˙454 € |
| EC max contribution | 183˙454 € (100%) |
| Programme |
1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility) |
| Code Call | H2020-MSCA-IF-2014 |
| Funding Scheme | MSCA-IF-EF-ST |
| Starting year | 2015 |
| Duration (year-month-day) | from 2015-11-16 to 2017-11-15 |
Partnership
Take a look of project's partnership.
| # | ||||
|---|---|---|---|---|
| 1 | IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE | UK (LONDON) | coordinator | 183˙454.00 |
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Project objective
Recent advancements in cardiovascular magnetic resonance (CMR) have finally made possible static and dynamic in-vivo imaging of the fetal heart. This new capability has the potential to provide a fundamental new tool for structural and functional assessment of the fetal cardiovascular system, with groundbreaking clinical consequences. In fact, congenital heart diseases (CHDs) and intrauterine growth restriction (IUGR, which induces cardiovascular remodeling) are among the leading causes of infant mortality worldwide. Fetal CMR imaging may potentially allow more accurate diagnosis of these conditions, and thus improve postnatal outcomes thanks to better in-utero therapy administration, delivery and perinatal intervention planning. Unfortunately, fetal CMR is currently limited to the acquisition of a single slice in time, allowing only qualitative and operator-dependent evaluation of the fetal heart. The JUNO project aims at improving the present capabilities of fetal CMR by tackling its limitations with an image processing approach. The specific goals are (1) development of a method for super resolution volumetric reconstruction of the fetal heart, using image registration techniques applied to a set of single-slice acquisitions; (2) development of automated segmentation methods, based on deformable models and atlases, for the identification of structures such as ventricular contours and main vessels’ boundaries; (3) extraction of quantitative functional parameters (e.g. stroke volume and ejection fraction) from datasets acquired from healthy, CHDs- and IUGR-affected fetuses, to test the feasibility of objective detection of these conditions. By achieving these goals, JUNO will provide an innovative set of methods allowing for the first time quantitative, noninvasive, functional assessment of the fetal cardiovascular system, and thus address a long standing clinical need for such methodology.
Publications
| year | authors and title | journal | last update |
|---|---|---|---|
| 2017 |
W Bai, O Oktay, M Sinclair, H Suzuki, M Rajchl, G Tarroni, B Glocker, A King, PM Matthews, D Rueckert Semi-supervised Learning for Network-Based Cardiac MR Image Segmentation published pages: 253-260, ISSN: , DOI: |
Medical Image Computing and Computer-Assisted Intervention (MICCAI), LNCS 10434 | 2019-06-13 |
| 2017 |
G Tarroni, O Oktay, A Schuh, W Bai, A de Marvao, D O\'Regan, S Cook, D Rueckert Slice Realignment for Motion-Corrupted Stacks of Short-Axis Cine Cardiac MR Images based on 3D Probabilistic Edge Maps published pages: , ISSN: , DOI: |
Proc. Intl. Soc. Mag. Reson. Med. 25 | 2019-06-13 |
| 2017 |
G Tarroni, O Oktay, W Bai, A Schuh, H Suzuki, J Passerat-Palmbach, B Glocker, A de Marvao, D O\'Regan, S Cook, D Rueckert Learning-Based Heart Coverage Estimation for Short-Axis Cine Cardiac MR Images published pages: 73-82, ISSN: , DOI: |
Functional Imaging and Modelling of the Heart (FIMH), LNCS 10263 | 2019-06-13 |
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