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MRI（fMRI）等成像方法来观察大脑如何工作。这种非侵入性技术已经改变了医生和神经科学家回答有关大脑如何组织和处理信息的问题的方式，健康的大脑如何运作，或者它如何与疾病和疾病相互作用。然而，由于运动和生理波动会降低图像质量，特别是随着技术进步导致成像具有更高的空间分辨率和更高的磁场强度，从而扩展了我们的MRI系统的能力，因此fMRI数据很容易受到损坏。几乎每个人都有在光线不足的情况下尝试捕获移动物体图像的经验（例如，人们在一个昏暗的房间），往往导致模糊和可怕的照片。现在想象一下，试图用一台操作非常缓慢和间接的相机（即MRI扫描仪）拍摄一个活生生的，呼吸的人脑，它不会静止不动。即使是静止的头部，呼吸和心跳等生理因素也会导致大脑内部的脉冲，移动并导致不必要的图像损坏。这在大脑的下部尤其成问题，例如脑干，它涉及重要的生理功能，例如处理疼痛和调节血压。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中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。第一种方法通过称为“门控”的过程修改数据采集，该过程使成像与心动周期的某个部分同步。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美的，提出了一个很大的机会，显着提高效率和质量的功能脑成像data.This建议带来了新的发展，多维（“张量”）信号处理承担这个问题。基于张量的方法使我们能够以更高的维度来表示和操作信号，从而使我们能够解析数据中的更多特征。例如，一部黑白白色电影可能具有对应于空间和时间的维度，但彩色电影具有空间、时间和颜色的维度，其中额外的维度允许我们捕获关于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，以有效地分离，而不是将所有这些信号贡献混合在一起。我们将联合收割机结合新的复杂的方法来获取原始的磁共振成像数据与先进的图像重建技术，以开发一种产生成像的技术以时间有效的方式获得没有生理损坏的数据。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.