  Accelerating the acquisition of magnetic resonance imaging (MRI) is a
challenging problem, and many works have been proposed to reconstruct images
from undersampled k-space data. However, if the main purpose is to extract
certain quantitative measures from the images, perfect reconstructions may not
always be necessary as long as the images enable the means of extracting the
clinically relevant measures. In this paper, we work on jointly predicting
cardiac motion estimation and segmentation directly from undersampled data,
which are two important steps in quantitatively assessing cardiac function and
diagnosing cardiovascular diseases. In particular, a unified model consisting
of both motion estimation branch and segmentation branch is learned by
optimising the two tasks simultaneously. Additional corresponding fully-sampled
images are incorporated into the network as a parallel sub-network to enhance
and guide the learning during the training process. Experimental results using
cardiac MR images from 220 subjects show that the proposed model is robust to
undersampled data and is capable of predicting results that are close to that
from fully-sampled ones, while bypassing the usual image reconstruction stage.
