  In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution
segmentation of the heart is essential for detailed description of its
anatomical structures. However, due to the limit of acquisition duration and
respiratory/cardiac motion, stacks of multi-slice 2D images are acquired in
clinical routine. The segmentation of these images provides a low-resolution
representation of cardiac anatomy, which may contain artefacts caused by
motion. Here we propose a novel latent optimisation framework that jointly
performs motion correction and super resolution for cardiac image
segmentations. Given a low-resolution segmentation as input, the framework
accounts for inter-slice motion in cardiac MR imaging and super-resolves the
input into a high-resolution segmentation consistent with input. A multi-view
loss is incorporated to leverage information from both short-axis view and
long-axis view of cardiac imaging. To solve the inverse problem, iterative
optimisation is performed in a latent space, which ensures the anatomical
plausibility. This alleviates the need of paired low-resolution and
high-resolution images for supervised learning. Experiments on two cardiac MR
datasets show that the proposed framework achieves high performance, comparable
to state-of-the-art super-resolution approaches and with better cross-domain
generalisability and anatomical plausibility.
