  Detector-based spectral computed tomography is a recent dual-energy CT (DECT)
technology that offers the possibility of obtaining spectral information. From
this spectral data, different types of images can be derived, amongst others
virtual monoenergetic (monoE) images. MonoE images potentially exhibit
decreased artifacts, improve contrast, and overall contain lower noise values,
making them ideal candidates for better delineation and thus improved
diagnostic accuracy of vascular abnormalities.
  In this paper, we are training convolutional neural networks~(CNN) that can
emulate the generation of monoE images from conventional single energy CT
acquisitions. For this task, we investigate several commonly used
image-translation methods. We demonstrate that these methods while creating
visually similar outputs, lead to a poorer performance when used for automatic
classification of pulmonary embolism (PE). We expand on these methods through
the use of a multi-task optimization approach, under which the networks achieve
improved classification as well as generation results, as reflected by PSNR and
SSIM scores. Further, evaluating our proposed framework on a subset of the
RSNA-PE challenge data set shows that we are able to improve the Area under the
Receiver Operating Characteristic curve (AuROC) in comparison to a na\"ive
classification approach from 0.8142 to 0.8420.
