Generative Adversarial Networks in Brain Imaging: A Narrative Review

Laino, Maria Elena; Cancian, Pierandrea; Politi, Letterio Salvatore; Della Porta, Matteo Giovanni; Saba, Luca; Savevski, Victor

doi:10.3390/jimaging8040083

Open AccessReview

Generative Adversarial Networks in Brain Imaging: A Narrative Review

by

Maria Elena Laino

^1,*

,

Pierandrea Cancian

¹,

Letterio Salvatore Politi

²

,

Matteo Giovanni Della Porta

³,

Luca Saba

⁴ and

Victor Savevski

¹

Artificial Intelligence Center, Humanitas Clinical and Research Center—IRCCS, Via Manzoni 56, 20089 Rozzano, Italy

²

Department of Radiology, Humanitas Clinical and Research Center—IRCCS, Via Manzoni 56, 20089 Rozzano, Italy

³

Department of Hematology, Humanitas Clinical and Research Center—IRCCS, Via Manzoni 56, 20089 Rozzano, Italy

⁴

Department of Radiology, University of Cagliari, 09124 Cagliari, Italy

^*

Author to whom correspondence should be addressed.

J. Imaging 2022, 8(4), 83; https://doi.org/10.3390/jimaging8040083

Submission received: 21 December 2021 / Revised: 8 March 2022 / Accepted: 15 March 2022 / Published: 23 March 2022

(This article belongs to the Special Issue Bio-Medical Multimodal Methods for Diagnosis, Prognosis, and Outcome Prediction)

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Abstract

Artificial intelligence (AI) is expected to have a major effect on radiology as it demonstrated remarkable progress in many clinical tasks, mostly regarding the detection, segmentation, classification, monitoring, and prediction of diseases. Generative Adversarial Networks have been proposed as one of the most exciting applications of deep learning in radiology. GANs are a new approach to deep learning that leverages adversarial learning to tackle a wide array of computer vision challenges. Brain radiology was one of the first fields where GANs found their application. In neuroradiology, indeed, GANs open unexplored scenarios, allowing new processes such as image-to-image and cross-modality synthesis, image reconstruction, image segmentation, image synthesis, data augmentation, disease progression models, and brain decoding. In this narrative review, we will provide an introduction to GANs in brain imaging, discussing the clinical potential of GANs, future clinical applications, as well as pitfalls that radiologists should be aware of.

Keywords: generative adversarial networks; brain imaging; MRI; CT; PET; fMRI

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MDPI and ACS Style

Laino, M.E.; Cancian, P.; Politi, L.S.; Della Porta, M.G.; Saba, L.; Savevski, V. Generative Adversarial Networks in Brain Imaging: A Narrative Review. J. Imaging 2022, 8, 83. https://doi.org/10.3390/jimaging8040083

AMA Style

Laino ME, Cancian P, Politi LS, Della Porta MG, Saba L, Savevski V. Generative Adversarial Networks in Brain Imaging: A Narrative Review. Journal of Imaging. 2022; 8(4):83. https://doi.org/10.3390/jimaging8040083

Chicago/Turabian Style

Laino, Maria Elena, Pierandrea Cancian, Letterio Salvatore Politi, Matteo Giovanni Della Porta, Luca Saba, and Victor Savevski. 2022. "Generative Adversarial Networks in Brain Imaging: A Narrative Review" Journal of Imaging 8, no. 4: 83. https://doi.org/10.3390/jimaging8040083

APA Style

Laino, M. E., Cancian, P., Politi, L. S., Della Porta, M. G., Saba, L., & Savevski, V. (2022). Generative Adversarial Networks in Brain Imaging: A Narrative Review. Journal of Imaging, 8(4), 83. https://doi.org/10.3390/jimaging8040083

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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