  Mainstream deep models for three-dimensional MRI synthesis are either
cross-sectional or volumetric depending on the input. Cross-sectional models
can decrease the model complexity, but they may lead to discontinuity
artifacts. On the other hand, volumetric models can alleviate the discontinuity
artifacts, but they might suffer from loss of spatial resolution due to
increased model complexity coupled with scarce training data. To mitigate the
limitations of both approaches, we propose a novel model that progressively
recovers the target volume via simpler synthesis tasks across individual
orientations.
