Pushing the limits of in vivo diffusion MRI for the Human Connectome Project

By Kawin Setsompop, R. Kimmlingen, Eva Eberlein, Thomas Witzel, Julien Cohen-Adad, J. McNab, Boris Keil, M. Dylan Tisdall, P. Hoecht, P. Dietz and 14 more. NeuroImage, 2013

568 citations recorded by the source corpus.

Subfields: Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/8bc33a0ed1d9fe864bd48e355d9872396094adb6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging (2016, background): “A multi-band factor of 3 was used, the echo times were optimized for each b-value and were 61, 69, 79 and 90 ms respectively resulting in repetition times of 2, 2.6, 3 and 3.2 s. 3 T HCP data (D) These data have been described in great detail in S. Sotiropoulos et al. (2013).”
SANDI: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI (2019, unknown): “Additional scan details can be found in (Setsompop et al., 2013).”
The Human Connectome Project’s Neuroimaging Approach (2016, background): “The resulting higher angular resolution and effective SNR enables multiple fiber populations to be more robustly identified in each imaging voxel, helping to resolve crossing fiber pathways and improve tractography 5,45 .”
Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited (2014, methodology): “Future improvements in the accuracy of diffusion tractography will require innovations in MRI hardware, sequence design, data acquisition strategies, and tractography algorithms (28, 42).”
The Human Connectome Project and Beyond: Initial Applications of 300 mT/m Gradients (2013, methodology): “A detailed description of the novel hardware employed by theMGH-UCLA consortia and a characterization of the improvements in data quality provided by this hardware can found in Setsompop et al. (2013).”
Advances in diffusion MRI acquisition and processing in the Human Connectome Project (2013, methodology): “Finally, it is useful to note the complementary aims of the efforts described here, by the “WU-Minn” HCP consortium, and the efforts of the “MGH-UCLA” HCP consortium (McNab et al., 2013; Setsompop et al., 2013).”
Self‐supervised learning of physics‐guided reconstruction neural networks without fully sampled reference data (2019, background): “ronounced in acquisitions, such as diffusion MRI with echo-planar imaging, where the signal decays quickly with T 2 *, thus prohibiting use of fullysampled acquisitions especially at high resolutions (41,42). In several other scenarios such as whole-heart coronary MRI or high-resolution anatomical b”
Denoising of diffusion MRI using random matrix theory (2016, background): “While the diffusion acquisition is covered in detail in (Setsompop et al., 2013), a brief summary is given here.”
Imaging brain microstructure with diffusion MRI: practicality and applications (2019, background): “Recent work on the Boston Connectom scanner [261] shows promising results in axon diameter mapping in live subjects both in health e.”