Gopal Nataraj

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We have observed, in metamaterial with hyperbolic dispersion (an array of silver nanowires in alumina membrane), a sixfold reduction of the emission lifetime of dye deposited onto the metamaterial's surface. This serves as evidence of an anomalously high density of photonic states in hyperbolic metamaterials, demonstrates the feasibility of an(More)
Rapid, reliable quantification of MR relaxation parameters <inline-formula> <tex-math notation="LaTeX">$\textit{T}_{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\textit{T}_{2}$ </tex-math></inline-formula> is desirable for many clinical applications. Steady-state sequences such as Spoiled Gradient-Recalled Echo (SPGR)(More)
These notes introduce a new kind of classifier called a dyadic decision tree (DDT). We also introduce a discrimination rule for learning a DDT that achieves the optimal rate of convergence, ER(ĥn) − R∗ = O(n−1/d), for the box-counting class, which was defined in the previous set of notes. This improves on the rate of ER(ĥn)−R = O(n−1/(d+2)) for the(More)
Fast and accurate quantification of spin-spin relaxation parameter T<sub>2</sub> is of importance for clinical MRI applications. Classical spin echo (SE) sequences yield straightforward T<sub>2</sub> estimates, but require undesirably long scans. By contrast, steady-state sequences such as the Dual-Echo Steady-State (DESS) sequence are considerably faster,(More)
MRI parameter quantification has diverse applications, but likelihood-based methods typically require nonconvex optimization due to nonlinear signal models. To avoid expensive grid searches used in prior works, we propose to learn a nonlinear estimator from simulated training examples and (approximate) kernel ridge regression. As proof of concept, we apply(More)
Rapid, reliable quantification of MR relaxation parameters T1 and T2 is desirable for many clinical applications. Steady-state sequences such as Spoiled Gradient-Recalled Echo (SPGR) and Dual-Echo Steady-State (DESS) are fast and wellsuited for relaxometry because the signals they produce are quite sensitive to T1 and T2 variation. However, T1, T2(More)
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