Advanced Image Analysis Tools for Super-Resolved MRI in Small Animals

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SUMMARY Imaging in animal models plays a key role in biomedical research, enabling both foundational studies for understanding disease processes, as well as translational studies evaluating novel therapies. In vivo imaging, in particular, offers the benefits of minimal harm to the animal and opportunities for measuring developmental, longitudinal changes. Magnetic resonance imaging (MRI) is one of the most extensively used in vivo imaging modalities because of its excellent sensitivity to a multitude of biological parameters and flexibility with different animal models, such as rodents, ferrets, and non-human primates. MRI has been used not only to advance understanding of neurodegenerative diseases, but also aging, cancer, addiction, and cardiovascular disorders. However, despite the research community’s desire for emulating clinical trials and performing high-throughput studies, automated analysis of MRI in animal models has significantly lagged state-of-the-art tools that are available in the analysis of human imaging. A major reason is that most animal MRI acquisitions are two- dimensional with high in-plane resolution but thick slices, whereas the most powerful image analysis tools work best on isotropic acquisitions. As a result, many researchers have been resigned to performing analyses involving laborious, manual delineations. Our team has recently developed a novel algorithm that uses deep learning to extract isotropic spatial resolution from a standard anisotropic MRI acquisition possessing without the need for external high-resolution training data, which is typically unavailable and difficult to procure. The ability to retrospectively recover isotropic spatial resolution from these two-dimensional MRI acquisitions allows for significantly reduced costs compared to high- resolution isotropic acquisitions. Moreover, it opens up the possibilities for more advanced analyses by enabling key image processing algorithms, such as registration and segmentation, to be more accurately performed and with greater automation. We therefore propose to perform the following Specific Aims in this R21 application: 1) Optimize and evaluate our deep learning-based unsupervised super-resolution approach for animal MRI; 2) Develop and evaluate a super-resolution algorithm for higher-dimensional data; 3) Publicly release the developed tools. Our overarching hypothesis is that the provided tools will enable significantly more sensitive imaging biomarkers, thereby increasing statistical power and reducing the size and cost of animal studies. The combination of the proposed resolution enhancement with state-of-the-art techniques for image analysis will also increase reproducibility by obviating the need for laborious, and potentially inconsistent manual delineations. Furthermore, these efforts will enable both pre-clinical and clinical trials to be implemented with nearly identical analysis pipelines. This application is being submitted in response to PAR 19-369, “Development of Animal Models and Related Biological Materials for Research.”