class BMEer:
def __init__(self):
self.name = "Kang Yan"
self.role = "Ph.D. candidate"
self.major = "MRI"
self.univ = "University of Virginia"
def welcome(self):
print("Hi. Thanks for stopping by, have fun!")
if __name__ == '__main__':
kangyans = BMEer()
kangyans.welcome()- MR Elastography-Based Slip Interface Imaging for Assessment of Myofascial Interface Mobility in Chronic Low Back Pain: A Pilot Study
- Ticept: Wideband Electrical Properties Tomography by Tissue Composition Assessment With Quantitative <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> mml:semantics mml:mrowmml:msupmml:mrow</mml:mrow> mml:mrowmml:mn1</mml:mn></mml:mrow> </mml:msup> </mml:mrow> mml:annotation$$ {}^1 $$</mml:annotation></mml:semantics> </mml:math> H <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> mml:semantics mml:mrowmml:msupmml:mrow</mml:mrow> mml:mrowmml:mn23</mml:mn></mml:mrow> </mml:msup> </mml:mrow> mml:annotation$$ {}^{23} $$</mml:annotation></mml:semantics> </mml:math> Na <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> mml:semantics mml:mrowmml:msupmml:mrow</mml:mrow> mml:mrowmml:mn39</mml:mn></mml:mrow> </mml:msup> </mml:mrow> mml:annotation$$ {}^{39} $$</mml:annotation></mml:semantics> </mml:math> K Multinuclear MRI
- Temperature Mapping During MR-Guided Cryoablation Using a FLORET UTE Sequence
- Rapid 3D whole-brain high-resolution T1 quantification: Accelerating standard inversion recovery with stack-of-spirals turbo FLASH acquisition
- Subject grounding to reduce electromagnetic interference for MRI scanners operating in unshielded environments
- Intraoral Coil Arrays for Single-Tooth Dental MRI
- Saturation Power Dispersion Analysis for the Imaging of Amide Proton Transfer (APT) and Nuclear Overhauser Effect (NOE)
- High-Accelerated Parallel Imaging With the Inherent Local Feature in PE-xSPEN MRI
- A Novel CEST-Based Approach for Reliably Assessing Skeletal Muscle Oxidative Phosphorylation: OXCEST
- Balancing Bias and Variance in Deep Learning-Based Tumor Microstructural Parameter Mapping
- Rapid 3D whole-brain high-resolution T(1) quantification: Accelerating standard inversion recovery with stack-of-spirals turbo FLASH acquisition
- MIMOSA: Multi-Parametric Imaging Using Multiple-Echoes With Optimized Simultaneous Acquisition for Highly-Efficient Quantitative MRI
- Prospective compensation of second-order concomitant fields in a high-performance gradient system using a second-order harmonic shim coil
- Design and performance of a toroidal radiofrequency volume coil with intrinsic electromagnetic interference rejection for low-field portable Halbach-based MRI systems
- Free-breathing 3D pulmonary ventilation mapping at 0.55 T using stack-of-spiral out-in bSSFP
- Exact, time-dependent analytical equations for spiral trajectories and matching gradient and density-correction waveforms
- Abdominal simultaneous 3D water T(1) and T(2) mapping using a free-breathing Cartesian acquisition with spiral profile ordering
- Feasibility of tagged MRI at 0.55โT
- Alternating-contrast single-shot spiral MR-ARFI with model-based displacement map reconstruction
- Navigator motion-resolved MR fingerprinting using implicit neural representation: Feasibility for free-breathing three-dimensional whole-liver multiparametric mapping
- MR Elastography-Based Slip Interface Imaging for Assessment of Myofascial Interface Mobility in Chronic Low Back Pain: A Pilot Study
- Liver fat quantification at 0.55 T enabled by locally low-rank enforced deep learning reconstruction
- Motion corrected 3D whole-heart SAVA T1 mapping at 0.55โT
- Dynamic contrast enhanced-magnetic resonance fingerprinting (DCE-MRF): A new quantitative MRI method to reliably assess tumor vascular perfusion
- Fast and High-Resolution luminal water imaging for prostate cancer diagnosis
- Compressed sensing acceleration of radial 3-D alternating Look-Locker <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> mml:mrow mml:msubmml:mrowmml:miT</mml:mi></mml:mrow> mml:mrowmml:mn1</mml:mn></mml:mrow> </mml:msub> </mml:mrow> </mml:math> mapping
- Simultaneous T1, T2, and T1ฯ mapping of the myocardium using cardiac MR fingerprinting with a deep image prior reconstruction
- Navigator-free multi-shot diffusion MRI via non-local low-rank reconstruction
- Referenceless 4D flow MRI using radial balanced SSFP at 0.6 T
- Denoising complex-valued diffusion MR images using a two-step, nonlocal principal component analysis approach