Deep learning of MRI contrast enhancement for mapping cerebral blood volume from single-modal non-contrast scans of aging and Alzheimer's disease brains.

While MRI contrast agents such as those based on Gadolinium are needed for high-resolution mapping of brain metabolism, these contrast agents require intravenous administration, and there are rising concerns over their safety and invasiveness. Furthermore, non-contrast MRI scans are more commonly performed than those with contrast agents and are readily available for analysis in public databases such as the Alzheimer's Disease Neuroimaging Initiative (ADNI). In this article, we hypothesize that a deep learning model, trained using quantitative steady-state contrast-enhanced structural MRI datasets, in mice and humans, can generate contrast-equivalent information from a single non-contrast MRI scan. The model was first trained, optimized, and validated in mice, and was then transferred and adapted to humans. We observe that the model can substitute for Gadolinium-based contrast agents in approximating cerebral blood volume, a quantitative representation of brain activity, at sub-millimeter granularity. Furthermore, we validate the use of our deep-learned prediction maps to identify functional abnormalities in the aging brain using locally obtained MRI scans, and in the brain of patients with Alzheimer's disease using publicly available MRI scans from ADNI. Since it is derived from a commonly-acquired MRI protocol, this framework has the potential for broad clinical utility and can also be applied retrospectively to research scans across a host of neurological/functional diseases.

Bioinspired Hydrogel Interferometer for Adaptive Coloration and Chemical Sensing.

Living organisms ubiquitously display colors that adapt to environmental changes, relying on the soft layer of cells or proteins. Adoption of soft materials into an artificial adaptive color system has promoted the development of material systems for environmental and health monitoring, anti-counterfeiting, and stealth technologies. Here, a hydrogel interferometer based on a single hydrogel thin film covalently bonded to a reflective substrate is reported as a simple and universal adaptive color platform. Similar to the cell or protein soft layer of color-changing animals, the soft hydrogel layer rapidly changes its thickness in response to external stimuli, resulting in instant color change. Such interference colors provide a visual and quantifiable means of revealing rich environmental metrics. Computational model is established and captures the key features of hydrogel stimuli-responsive swelling, which elucidates the mechanism and design principle for the broad-based platform. The single material-based platform has advantages of remarkable color uniformity, fast response, high robustness, and facile fabrication. Its versatility is demonstrated by diverse applications: a volatile-vapor sensor with highly accurate quantitative detection, a colorimetric sensor array for multianalyte recognition, breath-controlled information encryption, and a colorimetric humidity indicator. Portable and easy-to-use sensing systems are demonstrated with smartphone-based colorimetric analysis.