  Artificial intelligence (AI) and Machine Learning (ML) have shown great
potential in improving the medical imaging workflow, from image acquisition and
reconstruction to disease diagnosis and treatment. Particularly, in recent
years, there has been a significant growth in the use of AI and ML algorithms,
especially Deep Learning (DL) based methods, for medical image reconstruction.
DL techniques have shown to be competitive and often superior over conventional
reconstruction methods in terms of both reconstruction quality and
computational efficiency. The use of DL-based image reconstruction also
provides promising opportunities to transform the way cardiac images are
acquired and reconstructed. In this chapter, we will review recent advances in
DL-based reconstruction techniques for cardiac imaging, with emphasis on
cardiac magnetic resonance (CMR) image reconstruction. We mainly focus on
supervised DL methods for the application, including image post-processing
techniques, model-driven approaches and k-space based methods. Current
limitations, challenges and future opportunities of DL for cardiac image
reconstruction are also discussed.
