2 citations · 6 across the 8 of their papers we have counts for
9 papers
STORK: Spatio-Temporal Observation of uterine contRactions via neural networKs
Melissa Schween, Tristan Gottwald, Jordina Aviles Verdera +3
Uterine contractions in fetal MRI are typically identified manually and discarded, limiting insights into contraction dynamics. We formalize Uterine Contractile Activity Detection…
Predicting gestational age at birth in the context of preterm birth from multi-modal fetal MRI
Diego Fajardo-Rojas, Megan Hall, Daniel Cromb +4
Preterm birth is associated with significant mortality and a risk for lifelong morbidity. The complex multifactorial aetiology hampers accurate prediction and thus optimal care. A…
PUUMA (Placental patch and whole-Uterus dual-branch U-Mamba-based Architecture): Functional MRI Prediction of Gestational Age at Birth and Preterm Risk
Diego Fajardo-Rojas, Levente Baljer, Jordina Aviles Verdera +6
Preterm birth is a major cause of mortality and lifelong morbidity in childhood. Its complex and multifactorial origins limit the effectiveness of current clinical predictors and i…
Scanner-based real-time automated volumetry reporting of the fetus, amniotic fluid, placenta and umbilical cord for fetal MRI at 0.55T
Sara Neves Silva, Alena Uus, Hadi Waheed +18
Purpose: This work aims to enable real-time automated intra-uterine volumetric reporting and fetal weight estimation for fetal MRI, deployed directly on the scanner. Methods: A mul…
Real-time fetAl brain and placental T2* mapping at 0.55T low-field MRI (RAT)
Jordina Aviles Verdera, Sara Neves Silva, Raphael Tomi-Tricot +6
Purpose: To provide real-time quantitative organ-specific information - specifically placental and brain T2* - to allow optimization of the MR examination to the individual patient…
AutOmatic floW planning for fetaL MRI (OWL)
Sara Neves Silva, Tomas Woodgate, Sarah McElroy +11
Two subsequent deep learning networks, one localizing the fetal chest and one identifying a set of landmarks on a coronal whole-uterus balanced steady-state free precession scan, w…
Fully automated planning for anatomical fetal brain MRI on 0.55T
Sara Neves Silva, Sarah McElroy, Jordina Aviles Verdera +11
Purpose: Widening the availability of fetal MRI with fully automatic real-time planning of radiological brain planes on 0.55T MRI. Methods: Deep learning-based detection of key bra…
An automated pipeline for quantitative T2* fetal body MRI and segmentation at low field
Kelly Payette, Alena Uus, Jordina Aviles Verdera +9
Fetal Magnetic Resonance Imaging at low field strengths is emerging as an exciting direction in perinatal health. Clinical low field (0.55T) scanners are beneficial for fetal imagi…
Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging
Tong Zhang, Laurence H. Jackson, Alena Uus +5
Accurately estimating and correcting the motion artifacts are crucial for 3D image reconstruction of the abdominal and in-utero magnetic resonance imaging (MRI). The state-of-art m…