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.
| Weber, Hans; Hargreaves, Brian A; Daniel, Bruce L (2018) Artifact-reduced imaging of biopsy needles with 2D multispectral imaging. Magn Reson Med 80:655-661 |
| Gibbons, Eric K; Vasanawala, Shreyas S; Pauly, John M et al. (2018) Body diffusion-weighted imaging using magnetization prepared single-shot fast spin echo and extended parallel imaging signal averaging. Magn Reson Med 79:3032-3044 |
| Tian, Qiyuan; Wintermark, Max; Jeffrey Elias, W et al. (2018) Diffusion MRI tractography for improved transcranial MRI-guided focused ultrasound thalamotomy targeting for essential tremor. Neuroimage Clin 19:572-580 |
| Weber, Hans; Ghanouni, Pejman; Pascal-Tenorio, Aurea et al. (2018) MRI monitoring of focused ultrasound sonications near metallic hardware. Magn Reson Med 80:259-271 |
| Hegarty 2nd, John P; Gu, Meng; Spielman, Daniel M et al. (2018) A proton MR spectroscopy study of the thalamus in twins with autism spectrum disorder. Prog Neuropsychopharmacol Biol Psychiatry 81:153-160 |
| Srinivasan, Subashini; Hargreaves, Brian A; Daniel, Bruce L (2018) Fat-based registration of breast dynamic contrast enhanced water images. Magn Reson Med 79:2408-2414 |
| Yoruk, Umit; Hargreaves, Brian A; Vasanawala, Shreyas S (2018) Automatic renal segmentation for MR urography using 3D-GrabCut and random forests. Magn Reson Med 79:1696-1707 |
| Terem, Itamar; Ni, Wendy W; Goubran, Maged et al. (2018) Revealing sub-voxel motions of brain tissue using phase-based amplified MRI (aMRI). Magn Reson Med 80:2549-2559 |
| Levine, Evan; Hargreaves, Brian (2018) On-the-Fly Adaptive ${k}$ -Space Sampling for Linear MRI Reconstruction Using Moment-Based Spectral Analysis. IEEE Trans Med Imaging 37:557-567 |
| Hargreaves, Brian A; Taviani, Valentina; Litwiller, Daniel V et al. (2018) 2D multi-spectral imaging for fast MRI near metal. Magn Reson Med 79:968-973 |
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