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. 2022 Aug 11;14:923673. doi: 10.3389/fnagi.2022.923673

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Copyright © 2022 Liu, Zhu, Sun, Zhang, Feng, Gjerswold-Selleck, Sikka, Zhu, Liu, Nuriel, Wei, Wu, Vaughan, Laine, Provenzano, Small and Guo.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Overview of the studies conducted. We first performed proof-of-concept studies in mice to validate our hypothesis that deep learning can extract information equivalent to Gadolinium-based contrast agent (GBCA) contrast enhancement from a single-modal non-contrast MRI scan, and then conducted extensive analyses in humans to scrutinize the capability of this proposed approach. Study: A study conducted; Aim: The purpose of the study; Source: where the imaging data come from; MRI: modality/type of data used in the study; Model: specific DeepContrast model used in the study. AD: Alzheimer's disease; ADNI: Alzheimer's Disease Neuroimaging Initiative dataset; CBV: cerebral blood volume; Gd-Uptake: GBCA contrast uptake maps; T2W: T2-weighted scans; T2W-CE: T2-weighted contrast-enhanced scans; T1W: T1-weighted scans; T1W-CE: T1-weighted contrast-enhanced scans.