TY - JOUR
T1 - An automated pipeline for quantitative T2* fetal body MRI and segmentation at low field
AU - Payette, Kelly
AU - Uus, Alena
AU - Verdera, Jordina Aviles
AU - Zampieri, Carla Avena
AU - Hall, Megan
AU - Story, Lisa
AU - Deprez, Maria
AU - Rutherford, Mary A
AU - Hajnal, Joseph V
AU - Ourselin, Sebastien
AU - Tomi-Tricot, Raphael
AU - Hutter, Jana
N1 - Funding Information:
Acknowledgments. The authors thank all the participating families as well as the midwives and radiographers involved in this study. This work was supported by the the NIH (Human Placenta Project-grant 1U01HD087202-01), Wellcome Trust Sir Henry Wellcome Fellowship (201374/Z/16/Z and /B), UKRI FLF (MR/T018119/1), the NIHR Clinical Research Facility (CRF) at Guy’s and St Thomas’ and by core funding from the Wellcome/EPSRC Centre for Medical Engineering [WT203148/ Z/16/Z]. For the purpose of Open Access, the Author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. The views expressed are those of the authors and not necessarily those of the NHS or the NIHR.
Funding Information:
The authors thank all the participating families as well as the midwives and radiographers involved in this study. This work was supported by the the NIH (Human Placenta Project-grant 1U01HD087202-01), Wellcome Trust Sir Henry Wellcome Fellowship (201374/Z/16/Z and /B), UKRI FLF (MR/T018119/1), the NIHR Clinical Research Facility (CRF) at Guy’s and St Thomas’ and by core funding from the Wellcome/EPSRC Centre for Medical Engineering [WT203148/ Z/16/Z]. For the purpose of Open Access, the Author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. The views expressed are those of the authors and not necessarily those of the NHS or the NIHR.
Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
PY - 2023/10/1
Y1 - 2023/10/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85174725081&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-43990-2_34
DO - 10.1007/978-3-031-43990-2_34
M3 - Conference paper
C2 - 37608939
VL - 14226
SP - 358
EP - 367
JO - Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
JF - Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
ER -
