Spatio‐temporal image correlation (STIC): new technology for evaluation of the fetal heart
Open accessAbstract
Spatio‐temporal image correlation (STIC) is a new approach for clinical assessment of the fetal heart. It offers an easy to use technique to acquire data from the fetal heart and to aid in visualization with both two‐dimensional and three‐dimensional (3D) cine sequences. The acquisition is performed in two steps: first, images are acquired by a single, automatic volume sweep. Second, the system analyzes the image data according to their spatial and temporal domain and processes an online dynamic 3D image sequence that is displayed in a multiplanar reformatted cross‐sectional display and/or a surface rendered display. The examiner can navigate within the heart, re‐slice, and produce all of the standard image planes necessary for a comprehensive diagnosis. The advantages of STIC for use in evaluation of the fetal heart are as follows: the technique delivers a temporal resolution which corresponds to a B‐mode frame rate of approximately 80 frames/s; it provides the examiner with an unlimited number of images for review; it allows for correlation between image planes that are perpendicular to the main image acquisition plane; it may shorten the evaluation time when complex heart defects are suspected; it enables the reconstruction of a 3D rendered image that contains depth and volume which may provide additional information that is not available from the thin multiplanar image slices (e.g. for pulmonary veins, septal thickness); it lends itself to storage and review of volume data by the examiner or by experts at a remote site; it provides the examiner with the ability to review all images in a looped cine sequence. Copyright © 2003 ISUOG. Published by John Wiley & Sons, Ltd.
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TAPSE reflects longitudinal function and is an indicator of disease progression.
“This distance has been shown to reflect longitudinal function and be an indicator of disease progression [2].”
From Convolutional Neural Networks for Automated Fetal Cardiac Assessment using 4D B-Mode Ultrasound (2019) · cites this paper as [2]
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Convolutional Neural Networks for Automated Fetal Cardiac Assessment using 4D B-Mode Ultrasound
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- Authors
- DeVore, G. R., Falkensammer, P., Sklansky, M. S., & Platt, L. D.
- Affiliations
- General Electric (Austria) (AT)
- University of Southern California (US)
- Corresponding author
- DeVore, G. R.
- Venue
- Ultrasound in Obstetrics & Gynecology
- Publisher
- Wiley
- Published
- 2003-09-04
- Volume
- 22
- Issue
- 4
- Pages
- 380-387
- ISSN
- 0960-7692 (ISSN-L)
- 1469-0705
- OpenAlex
- W2039207139
- Cited by
- 362
- References
- 23
- Citations by year
- 2026: 7 · 2025: 7 · 2024: 5 · 2023: 15 · 2022: 13 · 2021: 7 · 2020: 12 · 2019: 18 · 2018: 11 · 2017: 16 · 2016: 18 · 2015: 12 · 2014: 14 · 2013: 24 · 2012: 19
- FWCI
- 12.39
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Ultrasound in Obstetrics and Gynecology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Epidemiology › Congenital Heart Disease Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Prenatal Screening and Diagnostics
- Keywords
- spatiotemporal image correlation, fetal echocardiography, three-dimensional ultrasound imaging, multiplanar reconstruction, surface rendering, congenital heart disease, temporal resolution
- MeSH terms
- Female, Fetal Heart, Gestational Age, Heart Defects, Congenital, Heart Rate, Fetal, Humans, Image Processing, Computer-Assisted, Pregnancy, Ultrasonography, Prenatal, Artifacts, Echocardiography, Three-Dimensional
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