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

摘要 动物模型的成像在生物医学研究中发挥着关键作用，使基础研究成为可能 了解疾病过程，以及评估新疗法的转化性研究。活体成像， 特别是，提供了对动物最小伤害的好处，并提供了测量发育的机会， 纵向变化。磁共振成像(MRI)是目前应用最广泛的活体成像技术之一 由于其对多种生物参数的出色敏感性以及对不同生物参数的灵活性 动物模型，如啮齿动物、雪貂和非人类灵长类动物。核磁共振技术不仅被用于 了解神经退行性疾病，但也包括衰老、癌症、成瘾和心血管疾病。 然而，尽管研究界希望效仿临床试验并执行高通量 研究表明，动物模型中磁共振成像的自动分析明显落后于最先进的工具，这些工具是 可用于人体成像的分析。一个主要原因是，大多数动物磁共振成像采集是两个- 具有较高的面内分辨率但切片较厚的三维图像，而最强大的图像分析工具 在各向同性收购方面表现最好。因此，许多研究人员已经听天由命地执行分析 牵涉到费力的手工勾画。 我们的团队最近开发了一种使用深度学习来提取各向同性空间分辨率的新算法 来自标准的各向异性MRI采集，无需外部高分辨率训练 数据，这些数据通常是无法获得的，很难获得。回溯恢复各向同性空间的能力 这些二维MRI采集的分辨率可显著降低成本，相比 解析各向同性收购。此外，它还通过以下方式为更高级的分析打开了可能性 关键的图像处理算法，如配准和分割，将被更准确地执行和 自动化程度更高。因此，我们建议在此R21应用程序中实现以下具体目标：1) 优化和评估我们基于深度学习的动物磁共振无监督超分辨率方法；2) 开发和评估用于高维数据的超分辨率算法；3)公开发布 开发的工具。我们的主要假设是，所提供的工具将使更敏感的 成像生物标志物，从而增加统计能力，减少动物研究的规模和成本。这个 将拟议的分辨率增强与最先进的图像分析技术相结合也将 通过消除繁琐的、可能不一致的手动描绘来提高重现性。 此外，这些努力将使临床前和临床试验能够在几乎相同的情况下实施 分析管道。本申请是根据标准杆19-369，“动物发育”提交的 用于研究的模型和相关的生物材料。

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.”