  Magnetic resonance imaging of whole fetal body and placenta is limited by
different sources of motion affecting the womb. Usual scanning techniques
employ single-shot multi-slice sequences where anatomical information in
different slices may be subject to different deformations, contrast variations
or artifacts. Volumetric reconstruction formulations have been proposed to
correct for these factors, but they must accommodate a non-homogeneous and
non-isotropic sampling, so regularization becomes necessary. Thus, in this
paper we propose a deep generative prior for robust volumetric reconstructions
integrated with a diffeomorphic volume to slice registration method.
Experiments are performed to validate our contributions and compare with a
state of the art method in a cohort of $72$ fetal datasets in the range of
$20-36$ weeks gestational age. Results suggest improved image resolution and
more accurate prediction of gestational age at scan when comparing to a state
of the art reconstruction method. In addition, gestational age prediction
results from our volumetric reconstructions compare favourably with existing
brain-based approaches, with boosted accuracy when integrating information of
organs other than the brain. Namely, a mean absolute error of $0.618$ weeks
($R^2=0.958$) is achieved when combining fetal brain and trunk information.
