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

总结 动物模型中的成像在生物医学研究中起着关键作用， 了解疾病过程，以及评估新疗法的转化研究。体内成像， 特别是提供了对动物伤害最小的益处， 纵向变化。磁共振成像（MRI）是最广泛使用的体内成像之一 由于其对多种生物学参数的出色敏感性和不同的灵活性， 动物模型，如啮齿动物、雪貂和非人灵长类动物。核磁共振成像不仅被用于 了解神经退行性疾病，还有衰老、癌症、成瘾和心血管疾病。 然而，尽管研究界希望模拟临床试验和进行高通量 研究中，动物模型中MRI的自动化分析明显落后于最先进的工具， 可用于人体成像分析。一个主要原因是大多数动物MRI采集是两个- 三维的高平面内分辨率，但厚切片，而最强大的图像分析工具的工作 最适合各向同性采集。因此，许多研究人员已经辞职进行分析， 包括费力的手工描绘。 我们的团队最近开发了一种新颖的算法，利用深度学习来提取各向同性的空间分辨率 从标准各向异性MRI采集，无需外部高分辨率训练， 数据，这通常是不可用的，很难获得。能够回顾性地恢复各向同性空间 这些二维MRI采集的分辨率允许与高分辨率MRI相比显著降低成本。 分辨率各向同性采集。此外，它还为更高级的分析提供了可能性， 关键的图像处理算法，如配准和分割，以更准确地执行， 更大的自动化。因此，我们建议在这次R21申请中执行以下特定目的：1） 优化和评估我们基于深度学习的动物MRI无监督超分辨率方法; 2） 开发和评估高维数据的超分辨率算法; 3）公开发布 开发工具。我们的总体假设是，所提供的工具将使更敏感的 成像生物标志物，从而增加统计能力并减少动物研究的规模和成本。的 所提出的分辨率增强与用于图像分析的最新技术的组合还将 通过消除对费力的和可能不一致的手动描绘的需要来增加再现性。 此外，这些努力将使临床前和临床试验能够以几乎相同的方式实施。 分析管道。本申请是根据PAR 19-369“动物开发”提交的。 模型和相关生物材料的研究。

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.”