Multi-parametric anthropomorphic MRI Phantoms technology for reliable and reproducible structural and quantitative MRI

摘要 我们的目标是开发用于定量和结构MRI（qMRI）的地面真实幻影的工具。qMRI旨在 获取可以用物理单位测量的物理或化学变量的地图，并在 组织区域和受试者之间。相比之下，大多数临床MRI采集仅是定性的，即“加权”的。 图像，而不是量化。虽然qMRI有潜力改善精确诊断和医学， 传统上受到诸如成像速度、计算实用性等重大障碍的阻碍， MR测量的再现性和可重复性。扫描仪和人类受试者之间的差异 以及生物组织中缺乏基本事实，从根本上挑战了生物组织的开发、测试和 qMRI技术的标准化。美国国家标准与技术研究院（NIST） 致力于标准化qMRI的研讨会。由此产生的建议文件着重列出了 突出的需求。拟议的项目旨在通过编写材料， 制造定量拟人MRI体模的技术、工具和工艺。当前状态- 用于制造MRI体模的现有技术解决方案通常使用离散的隔室或几何形状 充满了代表单一物理参数的化学溶液。相比之下，我们提出的新方法 将使得能够制造真实地模拟3D中组织的对比度异质性的体模。这些将包括 质子密度、T1、T2、T2* 弛豫时间、磁化率、扩散、脂肪分数、空气组织场- 不均匀性、相对电导率、电容率和磁导率。如果成功，这将是 这是第一次在组织模拟体模中完成这样一组全面的MRI参数。 基于我们在定量解剖学模拟切片体模方面的初步工作，我们提出了两种方法： (a)薄片的定量3D堆叠。这种方法成本低廉，易于在实验室中重现， 设备和技能。(b)一种先进的无边界全3D幻影方法，将通过 喷墨3D打印水凝胶和塑料，并将实现真正的高分辨率3D结构， 模仿人体解剖学的异质性。通过与领先的工业合作伙伴合作，我们将验证和 传播我们的技术我们的建议是出于对MRI中定量测量的日益增长的需求 由精准医学和使用数据科学工具发现生物标志物驱动。随着方法的兴起 例如指纹识别和加速重建，定量MRI（qMRI）比以往任何时候都更接近临床。 所提出的定量MRI体模将模拟组织结构和对比机制的复杂性 以确保qMRI的准确性。如果成功，该项目将大大促进发展 qMRI的临床翻译，使MRI准确，精确和定量-从而实现精确度 诊断和发现，将直接改善医疗保健。

Abstract We aim to develop tools for ground-truth phantoms for quantitative and structural MRI (qMRI). qMRI aims to acquire maps of physical or chemical variables that can be measured in physical units and compared between tissue regions and among subjects. In contrast, most clinical MRI acquisitions are only qualitative, i.e. “weighted images”, and not quantitative. While qMRI has the potential to improve precision diagnostics and medicine, it has been traditionally hampered by significant barriers such as imaging speed, computational practicalities, and reproducibility and repeatability of MR measurements. The variability between scanners and human subjects and the lack of ground truth in biological tissues fundamentally challenge the development, testing and standardization of qMRI techniques. The National Institute of Standards and Technology (NIST) hosted workshops working towards standardizing qMRI. The resulting recommendation paper highlighted a list of outstanding needs. The proposed project aims to address these unmet needs by developing materials, technology, tools and processes for manufacturing quantitative anthropomorphic MRI phantoms. Current state- of-the-art solutions for manufacturing MRI phantoms often use discrete compartments or geometrical shapes filled with chemical solutions representing a single physical parameter. In contrast, our proposed novel approach will enable fabrication of phantoms that truly mimic the contrast heterogeneity of tissue in 3D. These will include proton density, T1, T2, T2* relaxation times, magnetic susceptibility, diffusion, fat fraction, air-tissue field- inhomogeneity, relative conductivity, electric permittivity and magnetic permeability. If successful, this will be the first time that such a comprehensive set of MRI parameters is accomplished in a tissue-mimicking phantom. Based on our preliminary work on quantitative anatomy mimicking slice phantoms, we propose two approaches: (a) Quantitative 3D stack of thin slices. This approach is inexpensive, easy to reproduce by labs with moderate equipment and skills. (b) An advanced approach of boundaryless fully 3D phantoms that will be fabricated via inkjet 3D printing of hydrogels and plastics and would enable true high resolution 3D structures with heterogeneity that mimics human anatomy. In collaboration with leading industrial partners, we will validate and disseminate our technology. Our proposal is motivated by a rising need for quantitative measurements in MRI driven by precision medicine and the use of data science tools for biomarker discovery. With the rise of methods such as fingerprinting, and accelerated reconstruction, quantitative MRI (qMRI) is closer to the clinics than ever. The proposed quantitative MRI phantom will mimic the complexity of tissue structure and contrast mechanism that are necessary to ensure the accuracy of qMRI. If successful, the project will greatly facilitate the development and clinical translation of qMRI, making MRI accurate, precise, and quantitative – thus enabling precision diagnostic and discoveries that will directly improve healthcare.