No abstract

To estimate diffusion tensor MRI (DTI) measures, such as fractional anisotropy and fiber orientation, reliably, a large number of diffusion-encoded images is needed, preferably cardiac gated to reduce pulsation artifacts. However, the concomitant longer acquisition times increase the chances of subject motion adversely affecting the estimation of these measures. While correcting for motion artifacts improves the accuracy of DTI, an often overlooked step in realigning the images is to reorient the B-matrix so that orientational information is correctly preserved. To the best of our knowledge, most research groups and software packages currently omit this reorientation step. Given the recent explosion of DTI applications including, for example, neurosurgical planning (in which errors can have drastic consequences), it is important to investigate the impact of neglecting to perform the B-matrix reorientation. In this work, a systematic study to investigate the effect of neglecting to reorient the B-matrix on DTI data during motion correction is presented. The consequences for diffusion fiber tractography are also discussed. Reliable calculation of diffusion measures, such as fractional anisotropy (FA) and fiber orientation, is important for quantitative diffusion tensor MRI (DTI) analyses, and has been studied extensively with respect to image noise (1-5), eddy current induced distortion artifacts, (6-15), and data reproducibility (16-18). Previous research has also shown that the choice of gradient sampling scheme can affect the precision of the FA and mean diffusivity (MD), suggesting that a larger number of gradient directions (i.e., > 20-30) is required to estimate these diffusion measures robustly (19)(20)(21)(22)(23)(24)(25)(26). However, longer acquisition times increase the chances of subject motion, potentially nullifying the benefit of optimizing the gradient acquisition scheme.When correcting for subject motion, an important step in realigning the diffusion weighted (DW) images is to reorient the corresponding B-matrix so that orientational information is correctly preserved (6). This reorientation step is often neglected, however, when estimating the diffusion tensor and its contribution to the accuracy and precision of DTI measures has not yet been investigated systematically.Given the recent proliferation of clinical DTI applications, it is important to understand the consequences of neglecting to reorient the B-matrix during motion correction, especially for neurosurgical planning, in which tractography is being introduced as a tool for assessing white matter (WM) architectural configurations (27)(28)(29). In this work, we systematically investigated the effect of omitting the B-matrix reorientation on the estimation of diffusion measures, such as the FA, MD, and first eigenvector (FE), during motion correction. The adverse consequences for diffusion fiber tractography (both deterministic and probabilistic approaches) are also demonstrated. METHODS Definitions and Notationst with a constant b-value (...

Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations.

It has long been recognized that the diffusion tensor model is inappropriate to characterize complex fiber architecture, causing tensor-derived measures such as the primary eigenvector and fractional anisotropy to be unreliable or misleading in these regions. There is however still debate about the impact of this problem in practice. A recent study using a Bayesian automatic relevance detection (ARD) multicompartment model suggested that a third of white matter (WM) voxels contain crossing fibers, a value that, whilst already significant, is likely to be an underestimate. The aim of this study is to provide more robust estimates of the proportion of affected voxels, the number of fiber orientations within each WM voxel, and the impact on tensor-derived analyses, using large, high-quality diffusionweighted data sets, with reconstruction parameters optimized specifically for this task. Two reconstruction algorithms were used: constrained spherical deconvolution (CSD), and the ARD method used in the previous study. We estimate the proportion of WM voxels containing crossing fibers to be $90% (using CSD) and 63% (using ARD). Both these values are much higher than previously reported, strongly suggesting that the diffusion tensor model is inadequate in the vast majority of WM regions. This has serious implications for downstream processing applications that depend on this model, particularly tractography, and the interpretation of anisotropy and radial/axial diffusivity measures. Hum Brain Mapp 34:2747-2766,