Abstract #3617

Automated reconstruction of 3D+time fetal cardiac MRI from stacks of Doppler-gated slices

Arnaud Boutillon^1,2, Naomi Clarke¹, Tomas Woodgate³, Alina Schneider², Johaness Steinweg³, Rachael Franklin², Anthony Price^2,4, Tomas Roberts², Alena Uus³, Jo Hajnal^1,2, Kuberan Pushparajah³, David Lloyd³, and Maria Deprez^1,3

¹Biomedical Computing Research Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ²Imaging Physics and Engineering Research Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ³Early Life Imaging Research Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ⁴Medical Physics and Clinical Engineering, Guys and St Thomas’ NHS Foundation Trust, London, United Kingdom

Synopsis

Keywords: Fetal, Fetal

Motivation: Visualization of the fetal cardiac dynamics is essential for prenatal diagnosis of congenital heart diseases. Recently, 3D+time MRI was reconstructed from Doppler-gated 2D acquisitions, but this reconstruction requires lengthy manual input and long processing time.

Goal(s): We aim at developing a novel automatic and computationally efficient approach combining Doppler-gated cine imaging and motion-corrected slice-to-volume reconstruction (SVR).

Approach: We propose an automatic deep learning segmentation module of the thorax and a new 3D+time SVR algorithm specifically adapted to Doppler-gated sequences.

Results: The segmentation network produced robust predictions. The proposed reconstruction algorithm is ten times faster than the original approach while preserving image quality.

Impact: This preliminary study opens new potential for the use of Doppler-gated imaging in conjunction with SVR for 3D+time fetal cardiac imaging in a clinical context through the development of a fast, reliable, and automated reconstruction pipeline.

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