Contrast-free Deep Myocardial Tissue Characterization with Cardiac MR Fingerprinting

心血管疾病（CVD）是西方世界发病率和死亡率的主要单一原因，每年在英国造成65000多人死亡。磁共振成像（MRI）是心血管疾病风险评估、治疗指导和治疗监测的重要无创工具。磁弛豫特性（如T1和T2弛豫时间）的定量制图已经开发出来，目的是标准化心肌组织特性的定量测量，实现病变和健康组织的无创表征和分化。一些临床研究表明，组织特异性参数，如T1、T1rho、T2和T2*松弛时间，以及细胞外体积（ECV）和脂肪分数（FF），有可能改善CVD的评估。然而，定量心脏MRI仍然面临着一些挑战。一个主要的限制是，尽管有希望定量组织表征，但由于一些模型简化和MR系统相关的混淆因素，这些地图通常是特定于地点和供应商的。这些图是通过不同的MRI序列（注射造影剂之前/之后）和可能在不同的运动状态下（由于生理运动）依次获得的。此外，由于参数间依赖关系引入的误差，一次映射单个参数可能导致不准确的量化。所有这些都导致扫描时间长（限制了切片和估计参数的数量），并对参数图的再现性、分析和解释产生负面影响。心脏磁共振指纹（MRF）最近成为一种快速、同时量化多种组织特性（例如T1和T2）的方法。然而，还需要一些发展来实现心脏MRF对多个参数的稳健和可重复的无造影剂心肌组织表征。目前心脏MRF方法的局限性包括：1)仅量化T1和T2（以及最近的FF），然而大量额外的心肌组织信息（例如T1rho， T2*）可以进一步了解潜在的CVD， 2)加速心脏MRF所需的图像重建方法导致计算时间长，目前阻碍了临床翻译。3) MRF中字典生成和匹配的计算量随着定量参数的数量呈指数增长，目前能够同时量化的心脏MRF参数很少。4)在体内观察到T1和T2相对于传统制图技术的偏差，这可能是由几个混杂因素造成的，这些因素目前未包括在心脏MRF模型中；5)可重复性和可重复性研究有限，这是为定量心脏MRI提供标准化框架的基础步骤。拟议的项目将通过开发一种新颖，稳健和全面的多参数定量心脏MRF方法来克服这些问题，从而实现可重复的T1， T2, T1rho， T2*和FF同时进行一次有效扫描。此外，我们将研究所提出的方法是否提供了在不需要额外的对比后成像的情况下获得全面心肌组织特征的可能性。将研究基于深度学习（DL）的运动校正、重建、字典生成和匹配，以实现在~15-18秒/片的时间内获取多个精确地图，以及考虑MRF框架中几个参数和混杂因素所需的计算可扩展性。所提出的方法将在两个不同临床研究机构的标准化幻影、健康受试者和CVD患者中进行验证。

Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality in the Western world, causing over 65.000 deaths every year in England. Magnetic Resonance Imaging (MRI) is an important non-invasive tool for risk assessment, guidance of therapy and treatment monitoring of CVD. Quantitative mapping of magnetic relaxation properties (such as T1 and T2 relaxation times) have been developed with the aim of standardizing the quantitative measurement of myocardial tissue properties, enabling non-invasive characterization and differentiation of diseased and healthy tissue. Several clinical studies have shown the potential of tissue specific parameters such as T1, T1rho, T2 and T2* relaxation times as well as extracellular volume (ECV) and fat fraction (FF) to improve the assessment of CVD. However, quantitative cardiac MRI still suffers from several challenges. A major limitation is that despite promising quantitative tissue characterization these maps are usually site- and vendor-specific due to several model simplifications and MR system-related confounding factors. These maps are acquired sequentially with different MRI sequences (before/after contrast injection) and potentially at different motion states (due to physiological motion). Furthermore, mapping a single parameter at a time can lead to inaccurate quantification due to errors introduced by inter-parameter dependencies. All of the above results in long scan times (limiting the number of slices and parameters estimated) and negatively affects reproducibility, analysis and interpretation of the parametric maps.Cardiac Magnetic Resonance Fingerprinting (MRF) has recently emerged as an approach to rapidly and simultaneously quantify multiple tissue properties (e.g. T1 and T2). However, several developments are yet needed to enable robust and reproducible contrast-free myocardial tissue characterization of multiple parameters with cardiac MRF. Limitations of current cardiac MRF approaches include: 1) quantification of only T1 and T2 (and more recently FF), however a wealth of additional myocardial tissue information (e.g. T1rho, T2*) could enable further understanding of the underlying CVD, 2) image reconstruction methods required to accelerate cardiac MRF result in long computational times, currently impeding clinical translation. 3) The computational burden of dictionary generation and matching required in MRF increases exponentially with the number of quantitative parameters, thus only few simultaneous parameters are currently quantified with cardiac MRF. 4) Biases in T1 and T2 with respect to conventional mapping techniques have been observed in-vivo, which may be explained by several confounding factors, which are currently not included in the cardiac MRF model, and 5) repeatability and reproducibility studies are limited, which is a fundamental step to provide a standardised framework for quantitative cardiac MRI.The proposed project will overcome these problems by developing a novel, robust and comprehensive multiparametric quantitative cardiac MRF approach to enable reproducible simultaneous T1, T2, T1rho, T2* and FF mapping from a single and efficient scan. Furthermore, we will investigate whether the proposed approach offers the possibility of deriving comprehensive myocardial tissue characterization without the need of additional post-contrast imaging. Deep-learning (DL) based motion correction, reconstruction, dictionary generation and matching will be investigated to enable the acquisition of multiple accurate maps in ~15-18s/slice as well as the computational scalability needed to account for several parameters and confounding factors in the MRF framework. The proposed approach will be validated in standardised phantoms, healthy subjects and patients with CVD in two different clinical research Institutions.