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Joint Volumetric Reconstruction and Automated Analysis of the Fetal Heart from Cardiovascular Magnetic Resonance Images

Total Cost €


EC-Contrib. €






 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.

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

Project "JUNO" data sheet

The following table provides information about the project.


Organization address
city: LONDON
postcode: SW7 2AZ

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
 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


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 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.


year authors and title journal last update
List of publications.
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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