Measurement Uncertainty in Dynamic Contrast Enhanced MRI of Brain Tumors
In this video I present the highlight reel of research during my dissertation at the Magnetic Resonance Engineering Laboratory (MREL) at the University of Southern California (USC), where I had the honor of being supervised by Prof. Krishna S. Nayak.
Efficient DCE-MRI parameter and uncertainty estimation using a Neural Network
Quantitative DCE-MRI provides voxel-wise estimates of tracer-kinetic parameters that are valuable in the assessment of health and disease, yet suffers from many known sources of variability.
This work demonstrates a novel approach for simultaneous estimation of tracer-kinetic parameters and their uncertainty due to intrinsic characteristics of the tracer-kinetic model, with very low computation time.
This work demonstrates a novel approach for simultaneous estimation of tracer-kinetic parameters and their uncertainty due to intrinsic characteristics of the tracer-kinetic model, with very low computation time.
Impact of (k,t) sampling on DCE MRI tracer kinetic parameter estimation in digital reference objects
Sparse DCE-MRI measurement methods rely on sampling far below the Nyquist sampling rate to achieve the desired whole-brain coverage with simultaneous high spatial and temporal resolution.
This work investigates the influence of choice of (k,t) space sampling strategies on the precision in the estimated tracer-kinetic parameters.
This work investigates the influence of choice of (k,t) space sampling strategies on the precision in the estimated tracer-kinetic parameters.