Robust 3D functional imaging of the living, breathing brain

自20世纪90年代初以来，我们已经能够使用功能性核磁共振成像（fMRI）等成像方法来观察大脑的运作方式。这种非侵入性技术改变了医生和神经科学家回答有关大脑如何组织、如何处理信息、健康大脑如何运作或如何与疾病相互作用等问题的方式。然而，由于运动和生理波动降低了图像质量，特别是随着技术进步导致更高的空间分辨率和更高的磁场强度成像，扩展了我们的MRI系统的能力，功能磁共振成像数据可能容易受到损坏。几乎每个人都有过在光线不好的情况下拍摄移动物体的经历（例如，在光线昏暗的房间里的人），这通常会导致照片模糊而糟糕。现在想象一下，试图用一台运行缓慢且间接的相机（比如核磁共振扫描仪）拍摄一个活生生的、会呼吸的、不会静止不动的人脑。即使是静止不动的头部，呼吸和心跳等生理因素也会导致大脑内部的脉动、移动，从而导致不必要的图像损坏。这在大脑的下部尤其成问题，比如脑干，它参与处理疼痛和调节血压等重要的生理功能。再加上我们想要提取的大脑活动信号非常微妙，这些生理图像损坏会显著影响我们在这些临床重要的大脑区域获得的成像数据的质量。使用现有方法处理这个问题有两种主要方法。第一种方法通过一种被称为“门控”的过程来修改数据的获取，该过程与心脏周期的某个部分同步成像。第二种方法使用图像后处理来尝试“纠正”损坏的图像。然而，门控是低效的，图像后处理可能是不完善的，这为显著提高功能性脑成像数据的效率和质量提供了很大的机会。这一建议为解决这一问题带来了多维（张量）信号处理的新发展。基于张量的方法允许我们表示和操作具有更高维度的信号，允许我们解析数据中的更多特征。例如，一部黑白电影可能具有与空间和时间相对应的维度，但一部彩色电影具有空间、时间和颜色的维度，其中额外的维度使我们能够捕获有关感兴趣的信号的更多信息。对于我们的生理腐败问题，我们使用这些新工具来表示我们的3D大脑图像，不仅在时间上，而且在呼吸和心跳周期的不同点上，有效地分离，而不是把所有这些信号的贡献混合在一起。为了做到这一点，我们将把获取原始MRI数据的新复杂方法与图像重建的进步结合起来，以一种时间效率高的方式，开发出一种无生理损坏的成像数据技术。该项目汇集了广泛领域的知识和资源，从MRI系统的硬件控制到非线性信号处理和图像分析，为人类脑干的医学和神经科学研究提供更好的工具。

Since the early 1990s, we have been able to use imaging methods such as functional MRI (fMRI) to look into the brain to see how it works. This non-invasive technology has transformed the way that doctors and neuroscientists can answer questions about how the brain is organised and how it processes information, in the way a healthy brain functions, or how it interacts with illness and disease. However, fMRI data can be susceptible to corruption due to motion and physiological fluctuations that reduce image quality, particularly as technological progress leads to imaging at higher spatial resolutions and higher magnetic field strengths, stretching the capabilities of our MRI systems.Nearly everyone has had experience trying to capture images of moving objects in poor lighting conditions (e.g. people in a dimly lit room), often resulting in blurry and terrible looking photos. Now imagine trying to take pictures using a camera that operates quite slowly and indirectly (i.e. an MRI scanner), of a living, breathing human brain that won't sit still. Even for a head that is motionless, physiological factors like breathing and heart beats cause the brain inside to pulse, move and cause unwanted image corruption. This is particularly problematic in lower parts of the brain, like the brain-stem, which is involved in important physiological functions like processing pain and modulating blood pressure, for example. Coupled with the fact that the brain activity signals we want to extract are quite subtle, these physiological image corruptions can significantly impact the quality of the imaging data we can acquire in these clinically important brain regions.There are two primary ways of dealing with this problem using existing methods. The first approach modifies the acquisition of data through a process referred to as "gating", which synchronises imaging with a certain part of the cardiac cycle. The second approach uses image post-processing to try and "correct" the corrupted images. However, gating is inefficient and image post-processing can be imperfect, presenting a large opportunity for significant improvement in the efficiency and quality of functional brain imaging data.This proposal brings new developments in multi-dimensional ("tensor") signal processing to bear on this problem. Tensor-based methods allow us to represent and manipulate signals with higher dimensionality, allowing us to resolve more features in our data. For example, a black and white movie might have dimensions corresponding to space and time, but a colour movie has dimensions of space, time and colour, where the extra dimension allows us to capture more information about the signals of interest. For our physiological corruption problem, we use these new tools to represent our 3D brain images over not only time, but also across different points in the breathing and heart beat cycles, to effectively separate, rather than mix all of these signals contributions together.To do this, we will combine new sophisticated methods for acquiring the raw MRI data with advances in image reconstruction to develop a technique for producing imaging data free of physiological corruption, in a time efficient way. This project brings together knowledge and resources across a broad spectrum of fields, ranging from hardware control of MRI systems to nonlinear signal processing and image analysis, to provide better tools for medical and neuroscientific study of the human brain-stem.