CNNs and GANs in MRI-based cross-modality medical image estimation
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 31 of its 91 references that are in the map. First posted on arXiv 4 Jun 2021.
Builds on
31 of 91 references.
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19 more
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Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.
2 papers.