In vivo whole-cell recording from morphologically identified mouse superior colliculus neurons [PDF]
STAR Protocols 4 (2023) 101963, 2022 In vivo whole-cell recording when combined with morphological characterization after biocytin labeling is a powerful technique to study subthreshold synaptic processing in cell-type-identified neuronal populations. Here, we provide a step-by-step procedure for performing whole-cell recordings in the superior colliculus of urethane-anesthetized mice, a ...
arxiv +1 more source
Superiorized algorithm for reconstruction of CT images from sparse-view and limited-angle polyenergetic data [PDF]
Physics in Medicine and Biology, 62(16), 6762, 2017, 2017 Recent work in CT imaging has seen increased interest in the use of total variation (TV) and related penalties to regularize problems involving reconstruction from undersampled or incomplete data. Superiorization is a recently proposed heuristic which provides an automatic procedure to "superiorize" an iterative reconstruction algorithm with respect to
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Superiorized Regularization of Inverse Problems [PDF]
arXiv, 2023 Inverse problems are characterized by their inherent non-uniqueness and sensitivity with respect to data perturbations. Their stable solution requires the application of regularization methods including variational and iterative regularization methods. Superiorization is a heuristic approach that can steer basic iterative algorithms to have small value
arxiv
Can Linear Superiorization Be Useful for Linear Optimization Problems? [PDF]
, 2016 Linear superiorization considers linear programming problems but instead of attempting to solve them with linear optimization methods it employs perturbation resilient feasibility-seeking algorithms and steers them toward reduced (not necessarily minimal) target function values.
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Superiorization of Incremental Optimization Algorithms for Statistical Tomographic Image Reconstruction [PDF]
, 2016 We propose the superiorization of incremental algorithms for tomographic image reconstruction. The resulting methods follow a better path in its way to finding the optimal solution for the maximum likelihood problem in the sense that they are closer to the Pareto optimal curve than the non-superiorized techniques.
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A Direct Approach to Simultaneous Tests of Superiority and Noninferiority with Multiple Endpoints [PDF]
arXiv, 2023 Simultaneous tests of superiority and non-inferiority hypotheses on multiple endpoints are often performed in clinical trials to demonstrate that a new treatment is superior over a control on at least one endpoint and non-inferior on the remaining endpoints.
arxiv
Superiorization: An optimization heuristic for medical physics [PDF]
, 2012 Purpose: To describe and mathematically validate the superiorization methodology, which is a recently-developed heuristic approach to optimization, and to discuss its applicability to medical physics problem formulations that specify the desired solution (of physically given or otherwise obtained constraints) by an optimization criterion.
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Superiorization as a novel strategy for linearly constrained inverse radiotherapy treatment planning [PDF]
arXiv, 2022 We apply the superiorization methodology to the intensity-modulated radiation therapy (IMRT) treatment planning problem. In superiorization, linear voxel dose inequality constraints are the fundamental modeling tool within which a feasibility-seeking projection algorithm will seek a feasible point. This algorithm is then perturbed with gradient descent
arxiv
Total variation superiorization schemes in proton computed tomography image reconstruction [PDF]
Medical Physics, Vol. 37, pp. 5887-5895, (2010), 2010 Purpose: Iterative projection reconstruction algorithms are currently the preferred reconstruction method in proton computed tomography (pCT). However, due to inconsistencies in the measured data arising from proton energy straggling and multiple Coulomb scattering, noise in the reconstructed image increases with successive iterations.
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Weak and Strong Superiorization: Between Feasibility-Seeking and Minimization [PDF]
arXiv, 2014 We review the superiorization methodology, which can be thought of, in some cases, as lying between feasibility-seeking and constrained minimization. It is not quite trying to solve the full fledged constrained minimization problem; rather, the task is to find a feasible point which is superior (with respect to an objective function value) to one ...
arxiv