  Transthoracic echo is one of the most common means of cardiac studies in the
clinical routines. During the echo exam, the sonographer captures a set of
standard cross sections (echo views) of the heart. Each 2D echo view cuts
through the 3D cardiac geometry via a unique plane. Consequently, different
views share some limited information. In this work, we investigate the
feasibility of generating a 2D echo view using another view based on
adversarial generative models. The objective optimized to train the
view-conversion model is based on the ideas introduced by LSGAN, PatchGAN and
Conditional GAN (cGAN). The size and length of the left ventricle in the
generated target echo view is compared against that of the target ground-truth
to assess the validity of the echo view conversion. Results show that there is
a correlation of 0.50 between the LV areas and 0.49 between the LV lengths of
the generated target frames and the real target frames.
