activity
20242026
most citedAutOmatic floW planning for fetaL MRI (OWL)

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

physics.med-ph2026

PRIME-SVR: Physics-infoRmed Implicit Multi-Echo Slice-to-Volume Reconstruction for Fetal T2 mapping

Busra Bulut, Maik Dannecker, Thomas Sanchez +14

Slice-to-volume reconstruction (SVR) is the standard method for obtaining high-resolution (HR) 3D fetal brain volumes from motion-corrupted 2D MRI slice stacks acquired in multiple…

physics.med-ph2025

Real-time MRI-based fetal femur length measurement

Johannes Barcsay, Sara Neves Silva, Jordina Aviles Verdera +4

Purpose: To develop and evaluate a real-time method for automatic planning and measurement of fetal femur length - an important indicator of antenatal growth - during MRI. While ro…

physics.med-ph2025

From Offline to Inline Without Pain: A Practical Framework for Translating Offline MR Reconstructions to Inline Deployment Using the Gadgetron Platform

Zihan Ning, Yannick Brackenier, Sarah McElroy +23

Purpose: To develop and validate a practical framework to overcome common issues in inline deployment of established offline MR reconstruction, including (1) delay from lengthy rec…

cs.CV2025

Physics-Informed Joint Multi-TE Super-Resolution with Implicit Neural Representation for Robust Fetal T2 Mapping

Busra Bulut, Maik Dannecker, Thomas Sanchez +9

T2 mapping in fetal brain MRI has the potential to improve characterization of the developing brain, especially at mid-field (0.55T), where T2 decay is slower. However, this is cha…

physics.med-ph2024

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…

physics.med-ph2024★ 1 cited

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…