Multimodal Learning To Improve Cardiac Late Mechanical Activation
  Detection From Cine MR Images
Jiarui Xing Nian Wu Kenneth Bilchick Frederick Epstein Miaomiao Zhang
This paper presents a multimodal deep learning framework that utilizes
advanced image techniques to improve the performance of clinical analysis
heavily dependent on routinely acquired standard images. More specifically, we
develop a joint learning network that for the first time leverages the accuracy
and reproducibility of myocardial strains obtained from Displacement Encoding
with Stimulated Echo (DENSE) to guide the analysis of cine cardiac magnetic
resonance (CMR) imaging in late mechanical activation (LMA) detection. An image
registration network is utilized to acquire the knowledge of cardiac motions,
an important feature estimator of strain values, from standard cine CMRs. Our
framework consists of two major components: (i) a DENSE-supervised strain
network leveraging latent motion features learned from a registration network
to predict myocardial strains; and (ii) a LMA network taking advantage of the
predicted strain for effective LMA detection. Experimental results show that
our proposed work substantially improves the performance of strain analysis and
LMA detection from cine CMR images, aligning more closely with the achievements
of DENSE.