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()
- 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
- 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
- Increasing the scan-efficiency of pulmonary imaging at 0.55 T using iterative concomitant field and motion-corrected reconstruction
- Inline automatic quality control of 2D phase-contrast flow MRI for subject-specific scan time adaptation
- A 3D dual-echo spiral sequence for simultaneous dynamic susceptibility contrast and dynamic contrast-enhanced MRI with single bolus injection
- Submillimeter balanced SSFP BOLD-functional MRI accelerated with 3D stack-of-spirals at 9.4 T
- Analytical corrections for B(1)-inhomogeneity and signal decay in multi-slice 2D spiral hyperpolarized (129)Xe MRI using keyhole reconstruction