Accurate, reliable, and interpretable machine learning for assessment of neonatal and pediatric brain micro-structure

项目摘要 该项目的目标是增强扩散加权磁共振成像的能力 (DMRI)用于新生儿和儿科受试者。目前，dmri是唯一可行的非侵入性探测方法。 大脑的微观结构。过去的二十年见证了更强大、更复杂的发展 基于dMRI信号的脑微结构模型。不幸的是，对这些的准确和可靠的估计 模型需要大量高质量的测量，这可能很难或不可能在 新生儿和儿科受试者。因此，迫切需要一种方法，能够准确地和 从减少的和低质量的测量中稳健地估计微结构生物标志物。要解决这个问题 需要，本研究将开发和验证数据驱动和机器学习(ML)技术方法 评估新生儿和儿科受试者的dMRI生物标记物。这些方法的潜力很大 增加了大量高质量dMRI数据集的可用性，如人类连接组项目 (HCP)数据。最近的工作，包括我们自己的研究，已经证明了ML技术具有很好的 有可能克服现有分析工具的局限性，实现更高的估计精度。 这项研究将大大扩展我们的前期工作，并产生重要的新能力， 目前还不存在。具体地说，我们将开发和验证新的方法来估计重要的微观 结构模型和生物标志物，从扩散张量到高级多隔室模型，具有 在这方面，我们工作的两个主要新颖方面将包括1)空间- 提高主题水平分析准确性的时间图谱和2)新的深层神经的发展 基于自我关注的网络架构。此外，我们会发展新技术，以加强 用于dMRI分析的ML方法的可靠性、稳健性和可解释性。这将包括以下技术 计算经过良好校准的不确定性估计，可以检测损坏、噪声和超时的技术 分布测量，以及能够解释和解释以下预测的技术 这些ML方法。我们将使用重测和自举方法以及VIA对新方法进行评估 脑解剖学和显微结构专家的评估。这项研究中开发的方法将 使定量评估新生儿和儿童的脑微结构和影响 大脑发育关键阶段的发育因素和神经功能障碍， 细节和重现性，这是目前无法企及的。

Project Summary The goal of this project is to enhance the capabilities of diffusion-weighted magnetic resonance imaging (dMRI)for neonatal and pediatric subjects. Currently, dMRI is the only viable non-invasive method for probing brain microstructure. The past two decades have witnessed development of more powerful and more complex modelsof brain microstructure based on dMRI signal. Unfortunately, accurate and reliable estimation of these models require large numbers of high-quality measurements, which may be difficult or impossible to obtain in neonatal and pediatric subjects. Therefore, there is an urgent need for methods that can accurately and robustly estimatethe micro-structural biomarkers from reduced and low-quality measurements. To address this need, this researchwill develop and validate data-driven and machine learning (ML) techniques methods for estimating dMRI biomarkers for neonatal and pediatric subjects. The potential of these methods has greatly increased by the availability of large high-quality dMRI datasets such as the Human Connectome Project (HCP) data. Recent works, including our own studies, have demonstrated that ML techniques have a great potential to overcome limitations of the existing analysis tools and to achieve superior estimation accuracy. This research will substantially extend our preliminary work and generate important new capabilities that currently do not exist. Specifically, we will develop and validate novel methods for estimating important micro- structural models and biomarkers, ranging from diffusion tensor to advanced multi-compartment models, with far fewer measurements.In this regard, the two main novel aspects of our work will include 1) the use of spatio- temporal atlases to improvethe accuracy of subject-level analysis and 2) development of new deep neural network architectures based on self-attention. Furthermore, we will develop new techniques for enhancing the reliability, robustness, and explainability of ML methods for dMRI analysis. This will include techniques for computing well-calibrated uncertainty estimations, techniques that can detect corrupt, noisy, and out-of- distribution measurements, and techniques that enable interpretation and explanation of the predictions of these ML methods. We will evaluate the new methods using test-retest and bootstrapping methods and via assessment by experts in brain anatomyand micro-structure. The methods developed in this research will enable quantitative assessment of neonatal and pediatric brain micro-structure and the impact of developmental factors and neurological disorders at thesecritical stages in brain development with accuracy, detail, and reproducibility that is currently beyond reach.