  In this work, we present a method for synthetic CT (sCT) generation from
zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the
image, with a particular focus on the accurate bone density value prediction.
We propose a loss function that favors a spatially sparse region in the image.
We harness the ability of a multi-task network to produce correlated outputs as
a framework to enable localisation of region of interest (RoI) via
classification, emphasize regression of values within RoI and still retain the
overall accuracy via global regression. The network is optimized by a composite
loss function that combines a dedicated loss from each task. We demonstrate how
the multi-task network with RoI focused loss offers an advantage over other
configurations of the network to achieve higher accuracy of performance. This
is relevant to sCT where failure to accurately estimate high Hounsfield Unit
values of bone could lead to impaired accuracy in clinical applications. We
compare the dose calculation maps from the proposed sCT and the real CT in a
radiation therapy treatment planning setup.
