The Challenge of Fetal Cardiac MRI Reconstruction Using Deep Learning
Denis Prokopenko Kerstin Hammernik Thomas Roberts David F A Lloyd Daniel Rueckert Joseph V Hajnal
Dynamic free-breathing fetal cardiac MRI is one of the most challenging
modalities, which requires high temporal and spatial resolution to depict rapid
changes in a small fetal heart. The ability of deep learning methods to recover
undersampled data could help to optimise the kt-SENSE acquisition strategy and
improve non-gated kt-SENSE reconstruction quality. In this work, we explore
supervised deep learning networks for reconstruction of kt-SENSE style acquired
data using an extensive in vivo dataset. Having access to fully-sampled
low-resolution multi-coil fetal cardiac MRI, we study the performance of the
networks to recover fully-sampled data from undersampled data. We consider
model architectures together with training strategies taking into account their
application in the real clinical setup used to collect the dataset to enable
networks to recover prospectively undersampled data. We explore a set of
modifications to form a baseline performance evaluation for dynamic fetal
cardiac MRI on real data. We systematically evaluate the models on
coil-combined data to reveal the effect of the suggested changes to the
architecture in the context of fetal heart properties. We show that the
best-performers recover a detailed depiction of the maternal anatomy on a large
scale, but the dynamic properties of the fetal heart are under-represented.
Training directly on multi-coil data improves the performance of the models,
allows their prospective application to undersampled data and makes them
outperform CTFNet introduced for adult cardiac cine MRI. However, these models
deliver similar qualitative performances recovering the maternal body very well
but underestimating the dynamic properties of fetal heart. This dynamic feature
of fast change of fetal heart that is highly localised suggests both more
targeted training and evaluation methods might be needed for fetal heart
application.