EU H2020 Project "JUNO (Joint Volumetric Reconstruction and Automated Analysis of the Fetal Heart..)": description, partecipants, costs and EC-fundings

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JUNO project word cloud

Explore the words cloud of the JUNO project. It provides you a very rough idea of what is the project "JUNO" about.

vivo recent chds resonance capability restriction these qualitative dependent made static operator limitations datasets volume stroke imaging utero volumetric acquisition identification intervention therapy acquired structures vessels structural limited noninvasive detection segmentation outcomes postnatal quantitative induces contours extraction unfortunately resolution magnetic causes planning fetal advancements diseases innovative evaluation potentially congenital tool dynamic ventricular infant clinical mortality automated atlases healthy intrauterine deformable time cardiovascular techniques fundamental single reconstruction registration accurate goals super boundaries juno heart first groundbreaking remodeling image tackling acquisitions cmr methodology fraction worldwide thanks ejection fact fetuses slice functional models feasibility perinatal diagnosis administration iugr

Project "JUNO" data sheet

The following table provides information about the project.

Coordinator	IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE Organization address address: SOUTH KENSINGTON CAMPUS EXHIBITION ROAD city: LONDON postcode: SW7 2AZ website: http://www.imperial.ac.uk/ contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.
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

#	participants	country	role	EC contrib. [€]
1	IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE	UK (LONDON)	coordinator	183˙454.00

IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE

UK (LONDON)

coordinator

183˙454.00

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

List of 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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