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Cross-modality image estimation involves the generation of images of onemedical imaging modality from that of another modality. Convolutional neuralnetworks (CNNs) have been shown to be useful in identifying, characterising andextracting image patterns. Generative adversarial networks (GANs) use CNNs asgenerators and estimated images are discriminated as true or false based on anadditional network. CNNs and GANs within the image estimation framework may beconsidered more generally as deep learning approaches, since imaging data tendsto be large, leading to a larger number of network weights. Almost all researchin the CNN/GAN image estimation literature has involved the use of MRI datawith the other modality primarily being PET or CT. This review provides anoverview of the use of CNNs and GANs for MRI-based cross-modality medical imageestimation. We outline the neural networks implemented, and detail networkconstructs employed for CNN and GAN image-to-image estimators. Motivationsbehind cross-modality image estimation are provided as well. GANs appear toprovide better utility in cross-modality image estimation in comparison withCNNs, a finding drawn based on our analysis involving metrics comparingestimated and actual images. Our final remarks highlight key challenges facedby the cross-modality medical image estimation field, and suggestions forfuture research are outlined.

CNNs and GANs in MRI-based cross-modality medical image estimation