<br/>The process by which we develop new biomedical imaging paradigms in molecular and cellular imaging has changed. What once used to begin with new chemistry and biology – probe development and disease models – now begins with data analysis and informatics, working backwards to inform how we design and perform our experiments. In molecular and cellular MRI and CT imaging, data analysis methods guide nanomaterial properties and imaging paradigms, including material composition and methods for in vivo delivery of nanoparticles and therapeutics. For MRI, our laboratory has been using machine learning for intelligent and automatic in vivo detection of transplanted cells in MRI. The deep convolutional neural network (CNN) architecture we employ to enumerate transplanted cells by MRI requires discrimination of individual cells by MRI, which in turn prescribes the delivery methods for magnetically labeled cells, ultimately dictating the composition of the magnetic nanoparticles themselves. For multi-color CT, our material decomposition data analysis scheme drives experimental procedures, requiring specific methodologies for nanoparticle synthesis and incorporation into targeted imaging probes and CT-visible tissue engineered scaffolds.

Nanoinformatics as a driver for nanoparticle synthesis and biomedical imaging paradigms in MRI and CT

2019 Fall National Meeting

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