Towards Reliable Diffusion MRI of Moving Organs

弥散磁共振成像（DMRI）是一种临床成像技术，具有独特的潜力，可以在不使用电离辐射或侵入性程序的情况下提供临床相关信息。尽管DMRI经常用于大脑成像，但由于该拨款将解决一系列技术挑战，目前它还没有广泛用于身体的其他部位。我们的方法将是在一个统一的数学框架中描述的挑战，并解决这个问题，使用新的采集和图像重建技术。扩散加权MR图像中的强度源自水分子可以扩散的距离。这些扩散距离受局部细胞环境的影响，并且环境的变化反映在图像中。扩散MRI可以揭示结构的方向性，例如心肌细胞的方向以及由于癌性肿瘤引起的细胞密度和组织的变化。心脏病和癌症是世界范围内非常重要的健康问题，DMRI可以提供关键的诊断或治疗信息。心脏病是导致死亡的主要原因（全世界约30%），癌症是第三大死因（约12%），其中肺癌和肝癌最为常见。心肌的正常功能取决于心脏壁内纤维的复杂取向。能够在体内对这些进行成像可能会增强对心力衰竭、先天性缺陷或心脏病发作后重塑等疾病的诊断、治疗和手术计划。在癌症中，DMRI有可能改善诊断，帮助肿瘤定位和分级，支持治疗选择，更好地识别治疗后的残留和复发疾病，甚至预测治疗结果并减少诊断错误。然而，由于五个主要技术限制，DMRI目前在大脑之外的作用有限：器官的运动，由于组织（如脂肪，骨骼和空气）的不同磁特性引起的磁场变化，由于固有的MR扫描仪缺陷引起的磁场不准确性，长扫描持续时间以及如何解释数据的问题。之前，我们已经使用清晰的数学框架描述了复杂的非刚性运动对MR图像的影响，并使用它来校正运动。在这项授权中，我们建议扩展这些方法，以包括限制DMRI的挑战。采取这一总体观点，就可以集体解决挑战，而不是按顺序解决。这些技术将提高DMRI的可靠性，从而扩大其临床应用。同时，所述技术允许更有效地使用扫描时间，以编码更多信息或缩短扫描持续时间。一般形式主义提供了一种新的方式来看待这个问题，并有助于利用计算数学其他分支提供的工具。为了支持这种方法，我们需要测量由MRI扫描仪不准确引起的磁场缺陷。这些将由实地测绘硬件提供，这将是英国首次安装。此外，将探索压缩传感和组织扩散新模型等新技术，以减少总体扫描时间，提高准确性并提供更好的数据解释。

Diffusion Magnetic Resonance Imaging (DMRI) is a clinical imaging technique that has the unique potential to provide clinically-relevant information without the use of ionising radiation or invasive procedures. Although DMRI is often used in brain imaging, it is not currently widely used in other parts of the body because of a series of technical challenges that this grant will address. Our approach will be to describe the challenges in a unified mathematical framework and solve this using new acquisition and image reconstruction techniques. The intensities in diffusion weighted MR images originate from the distances that water molecules can diffuse. These diffusion distances are affected by the local cellular environment and changes in the environment are reflected in images. Diffusion MRI can reveal the directionality of structures, such as the orientation of cardiac muscle cells and changes in cell density and organisation due to cancerous tumours. Cardiac disease and cancer are very significant health issues worldwide for which DMRI may provide key diagnostic or therapeutic information. Cardiac disease is the main cause of death (ca.30% worldwide), and cancer the third (ca.12%) with lung and liver cancer among the most common.The correct functioning of the cardiac muscle is dependent on the complex orientations of the fibres within the heart wall. Being able to image these in-vivo could lead to enhanced diagnosis, treatment and surgery planning for conditions such as heart failure, congenital defects or remodelling following a heart attack. In cancer, DMRI has the potential to improve diagnosis, aid localisation and grading of tumours, support treatment selection, better identify residual and recurrent disease following treatment, and even predict treatment outcome and reduce diagnostic errors. However, DMRI currently has a limited role outside of the brain because of five main technological restrictions; motion of organs, magnetic field variations due to the different magnetic properties of tissues such as fat, bone and air, magnetic field inaccuracies due to inherent MR scanner imperfections, long scan durations and the question of how to interpret the data. Previously we have described the effect of complex non-rigid motion on MR images using a clear mathematical framework and used this to correct for motion. In this grant, we propose to extend these methods to include the challenges limiting DMRI. Taking this general view allows the challenges to be solved collectively rather than sequentially. The techniques will improve the reliability of DMRI and thus widen its clinical uptake. At the same time, the techniques permit more efficient use of scan time, either to encode more information or to shorten scan durations. A general formalism provides a new way of viewing the problem and lends itself to making use of the tools available from other branches of computational mathematics. To support this approach, we will need measurements of magnetic field imperfections caused by MRI scanner inaccuracies. These will be provided by field mapping hardware, which will be installed for the first time in the UK. In addition, novel techniques such as compressed sensing and new models of tissue diffusion will be explored to reduce overall scan times, improve accuracy and provide better interpretation of data.