An automated pipeline for quantitative T2* fetal body MRI and segmentation at low field

Kelly Payette; Alena Uus; Jordina Aviles Verdera; Carla Avena Zampieri; Megan Hall; Lisa Story; Maria Deprez; Mary A. Rutherford; Joseph V. Hajnal; Sebastien Ourselin; Raphael Tomi-Tricot; Jana Hutter

arXiv · 2308.04903

An automated pipeline for quantitative T2* fetal body MRI and segmentation at low field

Kelly Payette·Alena Uus·Jordina Aviles Verdera·Carla Avena Zampieri·Megan Hall·Lisa Story·Maria Deprez·Mary A. Rutherford·Joseph V. Hajnal·Sebastien Ourselin·Raphael Tomi-Tricot·Jana Hutter

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Abstract

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 imaging due to their reduced susceptibility-induced artefacts, increased T2* values, and wider bore (widening access for the increasingly obese pregnant population). However, the lack of standard automated image processing tools such as segmentation and reconstruction hampers wider clinical use. In this study, we introduce a semi-automatic pipeline using quantitative MRI for the fetal body at low field strength resulting in fast and detailed quantitative T2* relaxometry analysis of all major fetal body organs. Multi-echo dynamic sequences of the fetal body were acquired and reconstructed into a single high-resolution volume using deformable slice-to-volume reconstruction, generating both structural and quantitative T2* 3D volumes. A neural network trained using a semi-supervised approach was created to automatically segment these fetal body 3D volumes into ten different organs (resulting in dice values > 0.74 for 8 out of 10 organs). The T2* values revealed a strong relationship with GA in the lungs, liver, and kidney parenchyma (R^2>0.5). This pipeline was used successfully for a wide range of GAs (17-40 weeks), and is robust to motion artefacts. Low field fetal MRI can be used to perform advanced MRI analysis, and is a viable option for clinical scanning.

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Kelly Payette, Alena Uus, Jordina Aviles Verdera, Carla Avena Zampieri, Megan Hall, Lisa Story, Maria Deprez, Mary A. Rutherford, Joseph V. Hajnal, Sebastien Ourselin, Raphael Tomi-Tricot, Jana Hutter. 2023-08-09. An automated pipeline for quantitative T2* fetal body MRI and segmentation at low field. https://arxiv.org/abs/2308.04903

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