This TRD, Hardware and High Field, has been in existence since 2010 and has been addressing the demands for increased spatial resolution, sensitivity and speed as well as solving problems of high magnetic field, by developing novel MR hardware. We propose to continue this focus on technology development, particularly focusing our work on solving neuroimaging problems at high field (3T) and ultra-high-field (7T and above). With regard to the latter, it is known that there are still substantial technical innovations needed to make ultrahigh- field MRI routine, stable and consistently superior to the best available clinical MRI systems, in all parts of the body. The technical challenges related to gradient, shim and RF performance, decreased B0 and B1 homogeneity, and increased RF power deposition are the most critical. These challenges are the basis for much of the present research activity in the UHF MRI world, and many creative solutions are being found. But one underlying principle is clear: solving these problems will demand innovation in the design, implementation and application of high-performance hardware sub-subsystems. It is also clear that even at field strengths lower than 7T, many improvements in image quality would be enabled through novel hardware development. From the Human Connectome Project comes a clear demand for increased gradient performance, which is needed both for more efficient diffusion encoding and for faster and higher resolution spatial encoding. Yet body-size gradients have now reached hard amplitude and slew rate limits set by human peripheral nerve stimulation thresholds, and therefore any further increases in gradient performance will require innovation in smaller size gradient coils, most obviously head-size gradients. Along with the demands for better gradients come new requirements for better B0 shimming and B1 / radio frequency performance. In this TRD project, we will pursue projects involving major hardware design, construction and analysis in all three of these principal hardware subsystems of the MR scanner.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Biotechnology Resource Grants (P41)
Project #
5P41EB015891-24
Application #
9546682
Study Section
Special Emphasis Panel (ZEB1)
Project Start
Project End
Budget Start
2018-04-01
Budget End
2019-03-31
Support Year
24
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Stanford University
Department
Type
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94304
Han, Amy Kyungwon; Bae, Jung Hwa; Gregoriou, Katerina C et al. (2018) MR-Compatible Haptic Display of Membrane Puncture in Robot-Assisted Needle Procedures. IEEE Trans Haptics :
Levine, Evan; Stevens, Kathryn; Beaulieu, Christopher et al. (2018) Accelerated three-dimensional multispectral MRI with robust principal component analysis for separation of on- and off-resonance signals. Magn Reson Med 79:1495-1505
Winkler, Simone A; Schmitt, Franz; Landes, Hermann et al. (2018) Gradient and shim technologies for ultra high field MRI. Neuroimage 168:59-70
Gu, Meng; Hurd, Ralph; Noeske, Ralph et al. (2018) GABA editing with macromolecule suppression using an improved MEGA-SPECIAL sequence. Magn Reson Med 79:41-47
Perkins, Stephanie L; Daniel, Bruce L; Hargreaves, Brian A (2018) MR imaging of magnetic ink patterns via off-resonance sensitivity. Magn Reson Med 80:2017-2023
Lee, Brian J; Grant, Alexander M; Chang, Chen-Ming et al. (2018) MR Performance in the Presence of a Radio Frequency-Penetrable Positron Emission Tomography (PET) Insert for Simultaneous PET/MRI. IEEE Trans Med Imaging 37:2060-2069
Kogan, Feliks; Levine, Evan; Chaudhari, Akshay S et al. (2018) Simultaneous bilateral-knee MR imaging. Magn Reson Med 80:529-537
Gibbons, Eric K; Le Roux, Patrick; Pauly, John M et al. (2018) Slice profile effects on nCPMG SS-FSE. Magn Reson Med 79:430-438
Chen, Feiyu; Taviani, Valentina; Malkiel, Itzik et al. (2018) Variable-Density Single-Shot Fast Spin-Echo MRI with Deep Learning Reconstruction by Using Variational Networks. Radiology 289:366-373
Chaudhari, Akshay S; Fang, Zhongnan; Kogan, Feliks et al. (2018) Super-resolution musculoskeletal MRI using deep learning. Magn Reson Med 80:2139-2154

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