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.

Muscle architecture is the main determinant of the mechanical behavior of skeletal muscles. This study explored the feasibility of diffusion tensor imaging (DTI) and fiber tracking to noninvasively determine the in vivo three-dimensional (3D) architecture of skeletal muscle in mouse hind leg. In six mice, the hindlimb was imaged with a diffusion-weighted (DW) 3D fast spin-echo (FSE) sequence followed by the acquisition of an exerciseinduced, T 2 -enhanced data set. The data showed the expected fiber organization, from which the physiological cross-sectional area (PCSA), fiber length, and pennation angle for the tibialis anterior (

Diffusion tensor imaging (DTI)-based muscle fiber tracking enables the measurement of muscle architectural parameters, such as pennation angle (θ) and fiber tract length (Lft), throughout the entire muscle. Little is known, however, about the repeatability of either the muscle architectural measures or the underlying diffusion measures. Therefore, the goal of this study was to investigate the repeatability of DTI fiber tracking-based measurements and θ and Lft. Four DTI acquisitions were performed on two days that allowed for between acquisition, within day, and between day analyses. The eigenvalues and fractional anisotropy were calculated at the maximum cross-sectional area of, and fiber tracking was performed in, the tibialis anterior muscle of nine healthy subjects. The between acquisitions condition had the highest repeatability for the DTI indices and the architectural parameters. The overall inter class correlation coefficients (ICC’s) were greater than 0.6 for both θ and Lft and the repeatability coefficients were θ <10.2° and Lft < 50 mm. In conclusion, under the experimental and data analysis conditions used, the repeatability of the diffusion measures is very good and repeatability of the architectural measurements is acceptable. Therefore, this study demonstrates the feasibility for longitudinal studies of alterations in muscle architecture using DTI-based fiber tracking, under similar noise conditions and with similar diffusion characteristics.

After femoral artery ligation, the diffusion-tensor indexes changed dynamically in association with the severity and location of muscle damage.

Purpose To determine the minimum water percentage in a muscle region of interest that would allow diffusion tensor (DT-) MRI data to reflect the diffusion properties of pure muscle accurately. Materials and Methods Proton density-weighted images with and without fat saturation were obtained at the mid-thigh in four subjects. Co-registered DT-MR images were used to calculate the diffusion tensor’s eigenvalues and fractional anisotropy. Results The eigenvalues transitioned monotonically as a function of water signal percentage from values near to those expected for pure fat to those for pure muscle. Also, the fractional anisotropy transitioned monotonically from 0.50 (fat) to 0.20 (muscle). For water signal percentages >55%, none of the diffusion indices differed significantly from those for regions of >90% muscle. Conclusion Accounting for the T1 and T2 values of muscle and fat and the pulse sequence properties, it is concluded that, as a conservative estimate, regions must contain at least 76% muscle tissue to reflect the diffusion properties of pure muscle accurately.