On Density Compensation in Bayesian K-Space Trajectory Optimization

Keywords

achieving adjusting advances allows angular approximate approximated approximation arbitrarily arises assumed augmented authors behind better bibliography bounding brain calculation candidate care choosing classical clear compensation compressed compressive computer condition conditional conjugate construct correctly delta density derived design diagonal distribution domain easily efficient efficiently entropy equivalent estimation estimator evaluation even exactly experiment experimental experimentally fact familiar fast field formulation framework function gain give gradient implicitly important interestingly interpretation inventive iterations iteratively just khan lead least likelihood looked many matrix maximized noise observations operator optimal optimally optimization optimize optimized optimizes optimizing overlooked oversampled patterns peak performance posterior prior process proper property propose proposed proven quadrant quadratic radial random randomized ration rationale recently reconstructed reconstruction regularly requirement respect rise sampled sampling selection selections sense sensing several simulated space special specifically spectrum spiral squares stressed subsequently succeeds suitable though tractable trajectories trajectory transformation true unconstrained uniform uniformly variable variances variational vastly viewpoint white wired wireless