Department of Electrical and Computer Engineering, University of Iowa Iowa City, IA, USA.
Department of Psychiatry, University of North Carolina at Chapel Hill Chapel Hill, NC, USA.
Front Neuroinform. 2014 Jan 30;8:4. doi: 10.3389/fninf.2014.00004. eCollection 2014.
In the last decade, diffusion MRI (dMRI) studies of the human and animal brain have been used to investigate a multitude of pathologies and drug-related effects in neuroscience research. Study after study identifies white matter (WM) degeneration as a crucial biomarker for all these diseases. The tool of choice for studying WM is dMRI. However, dMRI has inherently low signal-to-noise ratio and its acquisition requires a relatively long scan time; in fact, the high loads required occasionally stress scanner hardware past the point of physical failure. As a result, many types of artifacts implicate the quality of diffusion imagery. Using these complex scans containing artifacts without quality control (QC) can result in considerable error and bias in the subsequent analysis, negatively affecting the results of research studies using them. However, dMRI QC remains an under-recognized issue in the dMRI community as there are no user-friendly tools commonly available to comprehensively address the issue of dMRI QC. As a result, current dMRI studies often perform a poor job at dMRI QC. Thorough QC of dMRI will reduce measurement noise and improve reproducibility, and sensitivity in neuroimaging studies; this will allow researchers to more fully exploit the power of the dMRI technique and will ultimately advance neuroscience. Therefore, in this manuscript, we present our open-source software, DTIPrep, as a unified, user friendly platform for thorough QC of dMRI data. These include artifacts caused by eddy-currents, head motion, bed vibration and pulsation, venetian blind artifacts, as well as slice-wise and gradient-wise intensity inconsistencies. This paper summarizes a basic set of features of DTIPrep described earlier and focuses on newly added capabilities related to directional artifacts and bias analysis.
在过去的十年中，人类和动物大脑的扩散磁共振成像（dMRI）研究被用于神经科学研究中调查多种病理学和与药物相关的影响。一项又一项的研究将白质（WM）退化确定为所有这些疾病的关键生物标志物。用于研究 WM 的首选工具是 dMRI。然而，dMRI 固有地具有低信噪比，并且其采集需要相对较长的扫描时间；实际上，高负载有时会使扫描仪硬件的物理性能超过极限。结果，许多类型的伪影会影响扩散图像的质量。在没有质量控制（QC）的情况下使用这些包含伪影的复杂扫描可能会导致后续分析中出现相当大的误差和偏差，从而对使用它们的研究结果产生负面影响。然而，dMRI QC 仍然是 dMRI 社区中一个未被充分认识的问题，因为没有用户友好的工具可以全面解决 dMRI QC 问题。因此，目前的 dMRI 研究通常在 dMRI QC 方面做得很差。对 dMRI 进行彻底的 QC 将减少测量噪声并提高神经影像学研究的可重复性和灵敏度；这将使研究人员能够更充分地利用 dMRI 技术的优势，并最终推动神经科学的发展。因此，在本文中，我们提出了我们的开源软件 DTIPrep，作为一种用于彻底进行 dMRI 数据 QC 的统一、用户友好的平台。这些包括涡流、头部运动、床振动和脉动、百叶窗伪影以及切片和梯度强度不一致引起的伪影。本文总结了 DTIPrep 之前描述的一组基本功能，并重点介绍了与方向伪影和偏差分析相关的新添加功能。
