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.

Purpose: To investigate previously unreported effects of signal drift as a result of temporal scanner instability on diffusion MRI data analysis and to propose a method to correct this signal drift.Methods: We investigated the signal magnitude of non‐diffusion‐weighted EPI volumes in a series of diffusion‐weighted imaging experiments to determine whether signal magnitude changes over time. Different scan protocols and scanners from multiple vendors were used to verify this on phantom data, and the effects on diffusion kurtosis tensor estimation in phantom and in vivo data were quantified. Scalar metrics (eigenvalues, fractional anisotropy, mean diffusivity, mean kurtosis) and directional information (first eigenvectors and tractography) were investigated.Results: Signal drift, a global signal decrease with subsequently acquired images in the scan, was observed in phantom data on all three scanners, with varying magnitudes up to 5% in a 15‐min scan. The signal drift has a noticeable effect on the estimation of diffusion parameters. All investigated quantitative parameters as well as tractography were affected by this artifactual signal decrease during the scan.Conclusion: By interspersing the non‐diffusion‐weighted images throughout the session, the signal decrease can be estimated and compensated for before data analysis; minimizing the detrimental effects on subsequent MRI analyses. Magn Reson Med 77:285–299, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.

Radiofrequency (RF) coils are an essential MRI hardware component. They directly impact the spatial and temporal resolution, sensitivity, and uniformity in MRI. Advances in RF hardware have resulted in a variety of designs optimized for specific clinical applications. RF coils are the “antennas” of the MRI system and have two functions: first, to excite the magnetization by broadcasting the RF power (Tx‐Coil) and second to receive the signal from the excited spins (Rx‐Coil). Transmit RF Coils emit magnetic field pulses ( ) to rotate the net magnetization away from its alignment with the main magnetic field (B₀), resulting in a transverse precessing magnetization. Due to the precession around the static main magnetic field, the magnetic flux in the receive RF Coil ( ) changes, which generates a current I. This signal is “picked‐up” by an antenna and preamplified, usually mixed down to a lower frequency, digitized, and processed by a computer to finally reconstruct an image or a spectrum. Transmit and receive functionality can be combined in one RF Coil (Tx/Rx Coils). This review looks at the fundamental principles of an MRI RF coil from the perspective of clinicians and MR technicians and summarizes the current advances and developments in technology.Level of Evidence: 1Technical Efficacy: Stage 6 J. Magn. Reson. Imaging 2018;48:590–604.