Real-time scanner-based automated fetal weight estimation and volumetry reporting in fetal MRI

Motivation: Fetal MRI, an important complementary modality for antenatal diagnosis, suffers from a lack of interactivity and a need for offline processing to achieve volumetric information. However, only information available during the scan can trigger further scans to provide a faster and more individualized diagnosis. Goal(s): Real-time automatic weight estimation and volumetry on the MR scanner during the time of acquisition. Approach: Deep learning networks have been combined with real-time scanner interfacing on high-performance computers to enable real-time volumetric measurements of the fetal body, head, placenta and amniotic fluid in bSSFP images. Results: Successful prospective real-time results were acquired, highlighting the achieved novel capacity. Impact: Real-time fetal weight estimation and volumetry using AI on the scanner enables faster and more individual fetal MRI - hence paving the way for enhanced antenatal diagnosis.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/2405
Primary Topic
Fetal and Pediatric Neurological Disorders
Type
article
Field-Weighted Citation Impact
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article

Real-time scanner-based automated fetal weight estimation and volumetry reporting in fetal MRI

Jacqueline Matthew, Sarah McElroy, Alena Uus, Jana Hutter et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Fetal and Pediatric Neurological Disorders
article

Real-time scanner-based automated fetal weight estimation and volumetry reporting in fetal MRI

Jacqueline Matthew, Sarah McElroy, Alena Uus, Jana Hutter, Jordina Aviles Verdera, Wendy V. Norman, David Lloyd, Mary Rutherford, Joseph V. Hajnal, Lisa Story, Kamilah St Clair, Sara Neves Silva, Simi Bansal, Hadi Waheed
article en

Abstract

Motivation: Fetal MRI, an important complementary modality for antenatal diagnosis, suffers from a lack of interactivity and a need for offline processing to achieve volumetric information. However, only information available during the scan can trigger further scans to provide a faster and more individualized diagnosis. Goal(s): Real-time automatic weight estimation and volumetry on the MR scanner during the time of acquisition. Approach: Deep learning networks have been combined with real-time scanner interfacing on high-performance computers to enable real-time volumetric measurements of the fetal body, head, placenta and amniotic fluid in bSSFP images. Results: Successful prospective real-time results were acquired, highlighting the achieved novel capacity. Impact: Real-time fetal weight estimation and volumetry using AI on the scanner enables faster and more individual fetal MRI - hence paving the way for enhanced antenatal diagnosis.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 65%
Fetal and Pediatric Neurological Disorders
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Real-time scanner-based automated fetal weight estimation and volumetry reporting in fetal MRI — Jacqueline Matthew, Sarah McElroy, et al. · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (2025) | TGRS Research Map | TGRS