class BMEer:
def __init__(self):
self.name = "Kang Yan"
self.role = "Ph.D. candidate"
self.major = "MRI"
self.university = "University of Virginia"
self.interested_topics = ["spiral", "deep learning", "dMRI", "MRgFUS"]
def welcome(self):
print("Thanks for stopping by, have fun!")
kangyans = BMEer()
kangyans.welcome()- Variable-flip-angle 3D spiral-in-out turbo spin-echo imaging using concomitant gradient compensation and echo reordering at 0.55 T
- Time-resolved MR fingerprinting for T2* signal extraction: MR fingerprinting meets echo planar time-resolved imaging
- Simultaneous multi-slice cardiac real-time MRI at 0.55T
- Giving the prostate the boost it needs: Spiral diffusion MRI using a high-performance whole-body gradient system for high b-values at short echo times
- Parallel-transmission spatial spectral pulse design with local specific absorption rate control: Demonstration for robust uniform water-selective excitation in the human brain at 7 T
- Fast 3D (31)P B1+ mapping with a weighted stack of spiral trajectory at 7 T
- 3D quantitative myocardial perfusion imaging with hyperpolarized HP001(bis-1,1-(hydroxymethyl)-[1-13C]cyclopropane-d8): Application of gradient echo and balanced SSFP sequences
- Motion and temporal B(0)-shift corrections for QSM and R2* mapping using dual-echo spiral navigators and conjugate-phase reconstruction
- High-quality FLORET UTE imaging for clinical translation
- Spiral 3DREAM sequence for fast whole-brain B1 mapping