AA Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Neuroimage. 2013 Oct 15;80:220-33. doi: 10.1016/j.neuroimage.2013.05.078. Epub 2013 May 24.
Perhaps more than any other "-omics" endeavor, the accuracy and level of detail obtained from mapping the major connection pathways in the living human brain with diffusion MRI depend on the capabilities of the imaging technology used. The current tools are remarkable; allowing the formation of an "image" of the water diffusion probability distribution in regions of complex crossing fibers at each of half a million voxels in the brain. Nonetheless our ability to map the connection pathways is limited by the image sensitivity and resolution, and also the contrast and resolution in encoding of the diffusion probability distribution. The goal of our Human Connectome Project (HCP) is to address these limiting factors by re-engineering the scanner from the ground up to optimize the high b-value, high angular resolution diffusion imaging needed for sensitive and accurate mapping of the brain's structural connections. Our efforts were directed based on the relative contributions of each scanner component. The gradient subsection was a major focus since gradient amplitude is central to determining the diffusion contrast, the amount of T2 signal loss, and the blurring of the water PDF over the course of the diffusion time. By implementing a novel 4-port drive geometry and optimizing size and linearity for the brain, we demonstrate a whole-body sized scanner with G(max) = 300 mT/m on each axis capable of the sustained duty cycle needed for diffusion imaging. The system is capable of slewing the gradient at a rate of 200 T/m/s as needed for the EPI image encoding. In order to enhance the efficiency of the diffusion sequence we implemented a FOV shifting approach to Simultaneous MultiSlice (SMS) EPI capable of unaliasing 3 slices excited simultaneously with a modest g-factor penalty allowing us to diffusion encode whole brain volumes with low TR and TE. Finally we combine the multi-slice approach with a compressive sampling reconstruction to sufficiently undersample q-space to achieve a DSI scan in less than 5 min. To augment this accelerated imaging approach we developed a 64-channel, tight-fitting brain array coil and show its performance benefit compared to a commercial 32-channel coil at all locations in the brain for these accelerated acquisitions. The technical challenges of developing the over-all system are discussed as well as results from SNR comparisons, ODF metrics and fiber tracking comparisons. The ultra-high gradients yielded substantial and immediate gains in the sensitivity through reduction of TE and improved signal detection and increased efficiency of the DSI or HARDI acquisition, accuracy and resolution of diffusion tractography, as defined by identification of known structure and fiber crossing.
也许比其他任何“组学”研究都更甚,使用扩散 MRI 绘制活人大脑主要连接通路图所获得的准确性和详细程度,取决于所使用成像技术的能力。目前的工具非常出色;允许在大脑的每 50 万个体素中形成复杂交叉纤维区域的水扩散概率分布的“图像”。尽管如此,我们的连接通路绘图能力受到图像灵敏度和分辨率以及扩散概率分布的对比度和分辨率的限制。我们的人类连接组计划(HCP)的目标是通过从头开始重新设计扫描仪来解决这些限制因素,以优化高 b 值、高角分辨率扩散成像,从而实现大脑结构连接的敏感和准确映射。我们的努力基于每个扫描仪组件的相对贡献。梯度部分是一个主要关注点,因为梯度幅度是决定扩散对比度、T2 信号损失量以及在扩散时间过程中水 PDF 模糊度的核心。通过实施新型的 4 端口驱动几何形状并针对大脑优化尺寸和线性度,我们展示了一种全身尺寸的扫描仪,每个轴的 G(max) = 300 mT/m,能够满足扩散成像所需的持续工作周期。该系统能够以 EPI 图像编码所需的 200 T/m/s 的速率旋转梯度。为了提高扩散序列的效率,我们实现了一种用于 Simultaneous MultiSlice (SMS) EPI 的视野移动方法,该方法能够以适度的 g 因子惩罚同时激发 3 个切片,从而能够以低 TR 和 TE 对整个大脑体积进行扩散编码。最后,我们将多切片方法与压缩采样重建相结合,对 q 空间进行充分欠采样,以在不到 5 分钟的时间内完成 DSI 扫描。为了增强这种加速成像方法,我们开发了一种 64 通道、紧密贴合的大脑阵列线圈,并展示了与商业 32 通道线圈相比在大脑所有位置的性能优势,适用于这些加速采集。还讨论了开发整体系统的技术挑战,以及 SNR 比较、ODF 指标和纤维跟踪比较的结果。超高梯度通过降低 TE 和提高信号检测以及增加 DSI 或 HARDI 采集的效率、扩散轨迹的准确性和分辨率,对灵敏度产生了实质性和直接的增益,这是通过识别已知结构和纤维交叉来定义的。