Contrast-free Deep Myocardial Tissue Characterization with Cardiac MR Fingerprinting

使用心脏 MR 指纹识别进行无对比深层心肌组织表征

• 批准号: EP/V044087/1
• 负责人: Claudia Prieto
• 金额: $ 118.82万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV044087%2F1
• 关键词: Contrast; free; Deep; Myocardial; Tissue

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.

心血管疾病（CVD）是西方世界发病率和死亡率的主要原因，每年在英格兰造成65.000多人死亡。磁共振成像（MRI）是用于风险评估，治疗指导和CVD治疗监测的重要非侵入性工具。已经开发了磁性弛豫特性（例如T1和T2松弛时间）的定量映射，以标准化心肌组织特性的定量测量，从而实现非侵入性表征和患病和健康组织的分化。一些临床研究表明，组织特异性参数的潜力，例如T1，T1RHO，T2和T2*松弛时间以及细胞外体积（ECV）和脂肪分数（FF），以改善CVD的评估。但是，定量心脏MRI仍面临一些挑战。一个主要的限制是，尽管有希望的定量组织表征，但由于几种模型的简化以及与MR系统相关的混杂因素，这些图通常是位点和供应商特异性的。这些图是通过不同的MRI序列（在对比度注射之前/之后）依次获得的，并且可能在不同的运动状态下（由于生理运动）。此外，一次映射单个参数可能会导致由于参数依赖性引入的误差而导致的量化不准确。上述所有结果都会导致长时间的扫描时间（限制了切片和参数的数量），并对参数映射的可重复性，分析和解释产生负面影响。Cardiac磁共振指纹（MRF）最近已成为一种快速且同时量化多个组织特性（例如T1和T2）的方法。但是，还需要几个发展来实现具有心脏MRF多个参数的鲁棒和可再现的无对比度心肌组织表征。当前心脏MRF方法的局限性包括：1）仅对T1和T2（以及最近的FF）进行量化，但是大量其他心肌组织信息（例如T1RHO，T2*）可以进一步了解基本CVD，2）2）图像重建方法，以使心脏MRF在长期计算的临床中产生了不计算的临床。 3）MRF中所需的字典生成和匹配的计算负担与定量参数的数量成倍增加，因此目前只有少数同时参数使用心脏MRF量化。 4）在体内已经观察到了T1和T2中关于常规映射技术的偏差，可以通过几个混杂因素来解释，这些因素目前尚未包括在心脏MRF模型中，5）可重复性和可重复性研究受到限制，这是通过Quartifative Quartive and Comportion Comportion Comportion Comportion Comportion Comportion Comportion的基本步骤。定量心脏MRF方法可以从单个且有效的扫描中启用可重现的同时T1，T2，T1RHO，T2*和FF映射。此外，我们将研究所提出的方法是否提供了无需额外对比后成像而导致全面的心肌组织表征的可能性。将研究基于深度学习（DL）的运动校正，重建，词典生成和匹配，以实现〜15-18/切片中的多个准确地图的获取，以及在MRF框架中所需的几个参数和混淆因素所需的计算可扩展性。所提出的方法将在两个不同的临床研究机构中的标准化幻象，健康受试者和CVD患者中进行验证。

项目成果

Current Applications and Future Development of Magnetic Resonance Fingerprinting in Diagnosis, Characterization, and Response Monitoring in Cancer.

• DOI: 10.3390/cancers13194742
• 发表时间: 2021-09-22
• 期刊: Cancers
• 影响因子: 5.2
• 作者: Ding H;Velasco C;Ye H;Lindner T;Grech-Sollars M;O'Callaghan J;Hiley C;Chouhan MD;Niendorf T;Koh DM;Prieto C;Adeleke S
• 通讯作者: Adeleke S

KomaMRI.jl: An Open-Source Framework for General MRI Simulations with GPU Acceleration

KomaMRI.jl：具有 GPU 加速功能的通用 MRI 模拟开源框架

• DOI: 10.48550/arxiv.2301.02702
• 发表时间: 2023
• 作者: Castillo-Passi C

Single-heartbeat cardiac cine imaging via jointly regularized nonrigid motion-corrected reconstruction

通过联合正则化非刚性运动校正重建的单心跳心脏电影成像

• DOI: 10.1002/nbm.4942
• 发表时间: 2023
• 期刊: NMR in Biomedicine
• 影响因子: 2.9
• 作者: Cruz G

Cardiac magnetic resonance fingerprinting: Trends in technical development and potential clinical applications.

• DOI: 10.1016/j.pnmrs.2020.10.001
• 发表时间: 2021-03
• 期刊: Progress in nuclear magnetic resonance spectroscopy
• 影响因子: 6.1
• 作者: Eck BL;Flamm SD;Kwon DH;Tang WHW;Vasquez CP;Seiberlich N
• 通讯作者: Seiberlich N