  Dealing with missing modalities in Magnetic Resonance Imaging (MRI) and
overcoming their negative repercussions is considered a hurdle in biomedical
imaging. The combination of a specified set of modalities, which is selected
depending on the scenario and anatomical part being scanned, will provide
medical practitioners with full information about the region of interest in the
human body, hence the missing MRI sequences should be reimbursed. The
compensation of the adverse impact of losing useful information owing to the
lack of one or more modalities is a well-known challenge in the field of
computer vision, particularly for medical image processing tasks including
tumour segmentation, tissue classification, and image generation. Various
approaches have been developed over time to mitigate this problem's negative
implications and this literature review goes through a significant number of
the networks that seek to do so. The approaches reviewed in this work are
reviewed in detail, including earlier techniques such as synthesis methods as
well as later approaches that deploy deep learning, such as common latent space
models, knowledge distillation networks, mutual information maximization, and
generative adversarial networks (GANs). This work discusses the most important
approaches that have been offered at the time of this writing, examining the
novelty, strength, and weakness of each one. Furthermore, the most commonly
used MRI datasets are highlighted and described. The main goal of this research
is to offer a performance evaluation of missing modality compensating networks,
as well as to outline future strategies for dealing with this issue.
