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()- 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
- Optimizing pseudo-spiral sampling for abdominal DCE MRI using a digital anthropomorphic phantom
- Improved abdominal T1 weighted imaging at 0.55T
- A decay-modeled compressed sensing reconstruction approach for non-Cartesian hyperpolarized (129)Xe MRI
- Improving MR axon radius estimation in human white matter using spiral acquisition and field monitoring
- Whole-cerebrum guanidino and amide CEST mapping at 3 T by a 3D stack-of-spirals gradient echo acquisition
- B0-insensitive image navigators for prospective motion-corrected MRS with localized second-order shimming
- Retrospective correction of second-order concomitant fields in 3D axial stack-of-spirals imaging on a high-performance gradient system
- Simultaneous brain and neck time-of-flight MRA using spiral multiband with localized quadratic encoding