Abstract #0279

Automatic planning of T2-weighted fetal scans at 0.55T using fetal brain landmark detection

Sara Neves Silva^1,2, Jordina Aviles Verdera^1,2, Sarah McElroy^1,3, Kathleen Colford^1,2, Michela Cleri^2,4, Valéry Ozenne⁵, Megan Hall^1,6, Lisa Story^1,6, Mary Rutherford^1,2, Kuberan Pushparajah², Jo Hajnal^1,2, and Jana Hutter^1,2

¹Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom, ²Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom, ³MR Research Collaborations, Siemens Healthcare Limited, Camberley, United Kingdom, ⁴London Collaborative Ultra High Field System (LoCUS), King's College London, London, United Kingdom, ⁵CNRS, CRMSB, UMR 5536, IHU Liryc, Université de Bordeaux, Bordeaux, France, ⁶Department of Women & Children’s Health, King's College London, London, United Kingdom

Synopsis

Keywords: Data Acquisition, Data Acquisition, Fetal

Motivation: Fetal MRI plays an important role in clinical and research settings. The variability of the fetal position and extensive fetal motion, however, create challenges limiting the use of fetal MRI mainly to specialist centres.

Goal(s): Real-time fully automatic planning of true radiological fetal brain planes for anatomical TSE scans.

Approach: Deep-learning based detection of key landmarks in the fetal brain on a whole-uterus EPI scan enables the subsequent automatic calculation of the radiological plane for the TSE scan.

Results: Prospective results on three fetal MRI scans on a clinical low-field 0.55T MRI scanner illustrate the ability of the framework to perform diagnostic planning.

Impact: Fully automated planning of radiological planes for low-field fetal MRI demonstrates time efficiency and carries the potential to significantly widen accessibility to fetal MRI beyond specialist centres.

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