Deformable Slice-to-Volume Registration for Motion Correction of Fetal Body and Placenta MRI
Alena Uus,Tong Zhang,Laurence H. Jackson,Thomas A. Roberts,Mary A. Rutherford,Joseph V. Hajnal,Maria Deprez +6 more
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TLDR
A Deformable SVR (DSVR), a novel approach for non-rigid motion correction of fetal MRI based on a hierarchical deformable S VR scheme to allow high resolution reconstruction of the fetal body and placenta is proposed.Abstract:
In in-utero MRI, motion correction for fetal body and placenta poses a particular challenge due to the presence of local non-rigid transformations of organs caused by bending and stretching. The existing slice-to-volume registration (SVR) reconstruction methods are widely employed for motion correction of fetal brain that undergoes only rigid transformation. However, for reconstruction of fetal body and placenta, rigid registration cannot resolve the issue of misregistrations due to deformable motion, resulting in degradation of features in the reconstructed volume. We propose a Deformable SVR (DSVR), a novel approach for non-rigid motion correction of fetal MRI based on a hierarchical deformable SVR scheme to allow high resolution reconstruction of the fetal body and placenta. Additionally, a robust scheme for structure-based rejection of outliers minimises the impact of registration errors. The improved performance of DSVR in comparison to SVR and patch-to-volume registration (PVR) methods is quantitatively demonstrated in simulated experiments and 20 fetal MRI datasets from 28–31 weeks gestational age (GA) range with varying degree of motion corruption. In addition, we present qualitative evaluation of 100 fetal body cases from 20–34 weeks GA range.read more
Citations
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Analysis of 3-Dimensional Arch Anatomy, Vascular Flow, and Postnatal Outcome in Cases of Suspected Coarctation of the Aorta Using Fetal Cardiac Magnetic Resonance Imaging
David F. A. Lloyd,David F. A. Lloyd,Milou P. M. van Poppel,Kuberan Pushparajah,Kuberan Pushparajah,Trisha V. Vigneswaran,Vita Zidere,Johannes K. Steinweg,Joshua F.P. van Amerom,Thomas A. Roberts,Alexander Schulz,Marietta Charakida,Owen Miller,Gurleen Sharland,Mary A. Rutherford,Joseph V. Hajnal,John M. Simpson,Reza Razavi,Reza Razavi +18 more
TL;DR: In this article, the authors identify fetuses at risk of severe neonatal coarctation of the aorta (CoA) in clinical practice with a high rate of false positives.
Journal ArticleDOI
Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging
TL;DR: A new real-time image-based motion tracking method based on deep learning that learns to predict fetal motion directly from acquired images, based on a recurrent neural network that infers motion parameters from anatomical features extracted from sequences of acquired slices.
Journal ArticleDOI
Fetal body MRI and its application to fetal and neonatal treatment: an illustrative review
Joseph R. Davidson,Joseph R. Davidson,Alena Uus,Jacqueline Matthew,Alexia Egloff,Maria Deprez,Iain Yardley,Paolo De Coppi,Paolo De Coppi,Paolo De Coppi,Anna L. David,Jim Carmichael,Mary A. Rutherford +12 more
TL;DR: In this paper, a review of the current literature on the latest developments in antenatal imaging for diagnosis and prognostication of congenital anomalies is coupled with illustrative cases in true radiological planes with viewable three-dimensional video models that show the potential of post-acquisition reconstruction protocols.
Journal ArticleDOI
Antenatal thymus volumes in fetuses that delivered <32 weeks' gestation: An MRI pilot study.
Lisa Story,Lisa Story,Tong Zhang,Alena Uus,Jana Hutter,Alexia Egloff,Deena L. Gibbons,Alison Ho,Mudher Al-Adnani,Caroline L. Knight,Caroline L. Knight,Iakovos Theodoulou,Maria Deprez,Paul T. Seed,Rachel M. Tribe,Andrew Shennan,Mary A. Rutherford +16 more
TL;DR: To generate magnetic resonance imaging ‐derived fetal thymus volumes standardized for fetal weight, it is hoped that the presence of chorioamnionitis and funisitis at delivery with thymic volumes in utero in fetuses that subsequently deliver preterm birth will be correlated.
Journal ArticleDOI
APPLAUSE: Automatic Prediction of PLAcental health via U-net Segmentation and statistical Evaluation
Maximilian Pietsch,Alison Ho,Alessia Bardanzellu,Aya Zeidan,Lucy C Chappell,Joseph V. Hajnal,Mary A. Rutherford,Jana Hutter +7 more
TL;DR: In this paper, the authors proposed a fully automatic pipeline to predict the biological age and health of the placenta based on a free-breathing rapid (sub-30 second) T2* scan in two steps: automatic segmentation using a U-Net and a Gaussian process regression model to characterize placental maturation and health.
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