A biophysical simulation framework for magnetic resonance microstructure imaging

磁共振微结构成像的生物物理模拟框架

• 批准号: EP/N018702/1
• 负责人: Daniel Alexander
• 金额: $ 84.79万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FN018702%2F1
• 关键词: biophysical; simulation; framework; magnetic; resonance

This project develops a simulation system for the MR signal in biological tissue and its dependence on molecular dynamics as influenced by tissue microarchitecture and composition. The system is an essential tool in the development of next-generation non-invasive imaging techniques. Specifically, it underpins the development and translation of the emerging paradigm of microstructure imaging. The paradigm uses mathematical models, which relate the MR signal to underlying tissue properties, to estimate and map those properties by fitting the models voxel-by-voxel to combinations of appropriately sensitised image data. The approach provides much greater biological specificity than standard MRI, thus enhancing diagnosis and treatment planning.The current generation of microstructure-imaging techniques is now starting to find widespread application in clinical studies. Prominent examples include NODDI for neuroimaging and VERDICT for cancer imaging, both developed by the investigators on this project. Those techniques are based entirely on diffusion MRI and their extension and refinement within that single contrast mechanism continues rapidly. However, a new generation of microstructure-imaging technique is just beginning to emerge that draws on multiple sources of MR contrast, for example combining diffusion MRI with relaxometry, susceptibility, etc. Such techniques offer great promise in the decades to come for the realisation of 'virtual histology' avoiding invasive procedures, such as biopsy, across a wide range of medical applications.EPSRC grant EP/E064280/1, which finished in 2011, developed the current state-of-the-art simulation system within the Camino toolkit. That system underpinned the early development of the microstructure-imaging paradigm, which led to current techniques like NODDI and VERDICT. However, the current system is insufficient to evaluate even current microstructure imaging techniques, because it excludes key effects that influence the diffusion MR signal. Moreover, its implementation limits the simulation to molecular diffusion as the only source of MR contrast, which fundamentally prevents its extension for validation of next-generation techniques. The new simulation system will use more sophisticated underlying models of tissue geometry and MR signal generation enabling it to support both modern diffusion-based microstructure-imaging applications and future multi-modal techniques. It provides a unique and invaluable validation tool allowing us to realise the full potential of quantitative non-invasive imaging in medicine and beyond. Within the project we demonstrate the new system by evaluating the performance of NODDI and VERDICT under a wide range of conditions. We also test two early examples of multi-modal microstructure imaging techniques paving the way for their robust development and eventual clinical translation.

本计画发展一个模拟系统，模拟生物组织中的磁振讯号，以及其对分子动力学的依赖性，因为分子动力学受组织微结构与组成的影响。该系统是开发下一代非侵入性成像技术的重要工具。具体来说，它支持的发展和翻译的新兴范式的微观结构成像。该范例使用数学模型，其将MR信号与潜在的组织特性相关联，以通过将模型逐体素拟合到适当敏感的图像数据的组合来估计和映射这些特性。该方法提供了比标准MRI更大的生物特异性，从而增强了诊断和治疗规划。当前一代的显微结构成像技术现在开始在临床研究中得到广泛应用。突出的例子包括用于神经成像的NODDI和用于癌症成像的VERDICT，两者都是由该项目的研究人员开发的。这些技术完全基于扩散MRI，并且它们在单一对比机制内的扩展和改进继续迅速进行。然而，新一代的微结构成像技术才刚刚开始出现，它利用了多种MR对比源，例如将扩散MRI与弛豫测量法、磁化率等相结合。这种技术在未来几十年内为实现“虚拟组织学”提供了巨大的希望，避免了在广泛的医疗应用中进行侵入性手术，例如活检。EPSRC授权EP/E064280/1，该项目于2011年完成，在Camino工具包中开发了当前最先进的模拟系统。该系统支撑了微结构成像范式的早期发展，这导致了NODDI和VERDICT等当前技术。然而，当前系统不足以评估甚至当前的微结构成像技术，因为其排除了影响扩散MR信号的关键效应。此外，其实现将模拟限制为分子扩散作为MR对比度的唯一来源，这从根本上阻止了其扩展以验证下一代技术。新的模拟系统将使用更复杂的组织几何形状和MR信号生成的基础模型，使其能够支持现代基于扩散的微结构成像应用和未来的多模态技术。它提供了一个独特而宝贵的验证工具，使我们能够充分发挥定量非侵入性成像在医学及其他领域的潜力。在该项目中，我们通过评估NODDI和VERDICT在各种条件下的性能来展示新系统。我们还测试了两个早期的多模态显微结构成像技术的例子，为它们的稳健发展和最终的临床翻译铺平了道路。

项目成果

SPHERIOUSLY? The challenges of estimating sphere radius non-invasively in the human brain from diffusion MRI.

• DOI: 10.1016/j.neuroimage.2021.118183
• 发表时间: 2021-08-15
• 期刊: NeuroImage
• 影响因子: 5.7
• 作者: Afzali M;Nilsson M;Palombo M;Jones DK
• 通讯作者: Jones DK

Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank.

• DOI: 10.1016/j.neuroimage.2017.10.034
• 发表时间: 2018-02-01
• 期刊: NeuroImage
• 影响因子: 5.7
• 作者: Alfaro-Almagro F;Jenkinson M;Bangerter NK;Andersson JLR;Griffanti L;Douaud G;Sotiropoulos SN;Jbabdi S;Hernandez-Fernandez M;Vallee E;Vidaurre D;Webster M;McCarthy P;Rorden C;Daducci A;Alexander DC;Zhang H;Dragonu I;Matthews PM;Miller KL;Smith SM
• 通讯作者: Smith SM

An optimized framework for quantitative magnetization transfer imaging of the cervical spinal cord in vivo.

• DOI: 10.1002/mrm.26909
• 发表时间: 2018-05
• 期刊: Magnetic resonance in medicine
• 影响因子: 3.3
• 作者: Battiston M;Grussu F;Ianus A;Schneider T;Prados F;Fairney J;Ourselin S;Alexander DC;Cercignani M;Gandini Wheeler-Kingshott CAM;Samson RS
• 通讯作者: Samson RS

VERDICT-AMICO: Ultrafast fitting algorithm for non-invasive prostate microstructure characterization.

结论 - amico：非侵入性前列腺微结构表征的超快拟合算法。

• DOI: 10.1002/nbm.4019
• 发表时间: 2019-01
• 期刊: NMR in biomedicine
• 影响因子: 2.9
• 作者: Bonet-Carne E;Johnston E;Daducci A;Jacobs JG;Freeman A;Atkinson D;Hawkes DJ;Punwani S;Alexander DC;Panagiotaki E
• 通讯作者: Panagiotaki E

Optimal framework for quantitative Magnetization Transfer imaging of small structures

小结构定量磁化转移成像的最佳框架

• 发表时间: 2017
• 作者: Battiston M