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Wright, R.* ; Gomez, A.* ; Zimmer, V.A.* ; Toussaint, N.* ; Khanal, B.* ; Matthew, J.* ; Skelton, E.* ; Kainz, B.* ; Rueckert, D.* ; Hajnal, J.V.* ; Schnabel, J.A.

Fast fetal head compounding from multi-view 3D ultrasound.

Med. Image Anal. 89:102793 (2023)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
The diagnostic value of ultrasound images may be limited by the presence of artefacts, notably acoustic shadows, lack of contrast and localised signal dropout. Some of these artefacts are dependent on probe orientation and scan technique, with each image giving a distinct, partial view of the imaged anatomy. In this work, we propose a novel method to fuse the partially imaged fetal head anatomy, acquired from numerous views, into a single coherent 3D volume of the full anatomy. Firstly, a stream of freehand 3D US images is acquired using a single probe, capturing as many different views of the head as possible. The imaged anatomy at each time-point is then independently aligned to a canonical pose using a recurrent spatial transformer network, making our approach robust to fast fetal and probe motion. Secondly, images are fused by averaging only the most consistent and salient features from all images, producing a more detailed compounding, while minimising artefacts. We evaluated our method quantitatively and qualitatively, using image quality metrics and expert ratings, yielding state of the art performance in terms of image quality and robustness to misalignments. Being online, fast and fully automated, our method shows promise for clinical use and deployment as a real-time tool in the fetal screening clinic, where it may enable unparallelled insight into the shape and structure of the face, skull and brain.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Compounding ; Deep Learning ; Fast ; Fetal ; Fusion ; Laplacian Pyramid ; Multi View ; Online ; Pose ; Registration ; Reinforcement Learning ; Us ; Ultrasound; Prenatal-diagnosis; 3-dimensional Ultrasound; Learning Framework; Registration; Biometry; Reconstruction; Disease; Suture; Agent; Fetus
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 1361-8415
e-ISSN 1361-8415
Zeitschrift Medical Image Analysis
Quellenangaben Band: 89, Heft: , Seiten: , Artikelnummer: 102793 Supplement: ,
Verlag Elsevier
Verlagsort Radarweg 29, 1043 Nx Amsterdam, Netherlands
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-507100-001
Förderungen King's College London
National Institute for Health Research (NIHR) Biomedical Research Centre at Guy's and St Thomas' NHS Foundation Trust
Wellcome/EPSRC Centre for Medical Engineering
Wellcome Trust IEH Award
DOI 10.1016/j.media.2023.102793
WOS ID 001097808400001
Scopus ID 85165528184
PubMed ID 37482034
Erfassungsdatum 2023-10-06
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