  Cross-modality image estimation involves the generation of images of one
medical imaging modality from that of another modality. Convolutional neural
networks (CNNs) have been shown to be useful in identifying, characterising and
extracting image patterns. Generative adversarial networks (GANs) use CNNs as
generators and estimated images are discriminated as true or false based on an
additional network. CNNs and GANs within the image estimation framework may be
considered more generally as deep learning approaches, since imaging data tends
to be large, leading to a larger number of network weights. Almost all research
in the CNN/GAN image estimation literature has involved the use of MRI data
with the other modality primarily being PET or CT. This review provides an
overview of the use of CNNs and GANs for MRI-based cross-modality medical image
estimation. We outline the neural networks implemented, and detail network
constructs employed for CNN and GAN image-to-image estimators. Motivations
behind cross-modality image estimation are provided as well. GANs appear to
provide better utility in cross-modality image estimation in comparison with
CNNs, a finding drawn based on our analysis involving metrics comparing
estimated and actual images. Our final remarks highlight key challenges faced
by the cross-modality medical image estimation field, and suggestions for
future research are outlined.
