Editorial for “Super‐Resolution Cine Image Enhancement for Fetal Cardiovascular Magnetic Resonance Imaging”

Editorial for “Super-Resolution Cine Image Enhancement for Fetal Cardiovascular Magnetic Resonance Imaging” Magnetic resonance imaging (MRI) is an important diagnostic tool in cardiovascular diseases. Its efficacity could also improve the prenatal diagnosis and the therapeutic management of cardiac congenital malformities. However, due to the long acquisition time and the fetal motion, images lack in quality. Although several correction techniques have emerged, they take a long time and cannot provide a real-time visualization of the small structures of the fetal heart. Thus, the need for faster image acquisition and reconstruction has been well observed by the authors who included 28 pregnant women in a prospective study, which analyzed different types of correction of images recorded by MRI. This study proposed two types of super-resolution neural networks working with interpolation in the phase-encoding direction, one which is based on a convolutional neural network (denoted phasrresnet) and one also based on a conditional generative adversarial model (denoted phasrGAN). Two independent physicians with experience in cardiovascular MR have independently evaluated the image. It is no surprise that the examiners have regarded the images made by super-resolution working with neural networks as better than bicubic interpolation, k-space zero-padding, nearest-neighbor interpolation, or standard clinical acquisition. Generative adversarial networks (GANs) appeared as a new deep learning method in 2014. The basic GAN model is composed of an input vector, a generator, and a discriminator. The mechanism involves learning of the generative model of data distribution through adversarial methods. Since its discovery, GAN has been used in multiple fields, providing new research ideas and new discoveries in artificial intelligence with multiple applications in medicine and medical imaging. There are some very promising directions of research for super-resolution imaging. In oncology, MRI-guided radiotherapy improved by super resolution with a machine learning framework improved the tumor contour quality without significant acquisition or post-processing delay. Also, in nodules affecting the lung, the proposed multiview 2D network is an effective algorithm for detecting nodules that are isolated, linked to a vessel, or attached to the lung wall. Although neural networks provide a better resolution, there is a risk of introducing non-existing pathology and removing existing pathophysiological changes. The controversial aspects of GANs are as follows: the fact that training of GANs is a challenge due to establishment of the equilibrium; mode collapse; the problem of diminished gradients being possible that the discriminator performs in an outstanding manner making the generator’s gradient diminished with slow learning or no learning at all; the lack of benchmarking evaluation criteria for assessment of the stability of the GAN; overfitting due to the unbalance between both discriminator and the generator models; the lack of good objective functions making it very challenging to perform a comprehensive evaluation of models. Those aspects as well as the increased costs and the novelty of the technique make super-resolution imaging still impossible to implement in current clinical practice. Nevertheless, studies like this one are constantly helping build imaging models that could help diagnose diseases more precisely. This paper is a real example that a principle discovered a few years ago can not only help further research in the field of artificial intelligence and deep learning methods but can also apply these findings in the field of prenatal imaging, with improved prognosis of millions of children suffering from congenital heart disease.