Efficient total variation algorithm for fetal MRI reconstruction

Fetal MRI reconstruction aims at finding a high-resolution image given a small set of low-resolution images. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has considered several regularization terms s.a. Dirichlet/Laplacian energy [1], Total Variation (TV)based energies [2,3] and more recently non-local means [4]. Although TV energies are quite attractive because of their ability in edge preservation, standard explicit steepest gradient techniques have been applied to optimize fetal-based TV energies. The main contribution of this work lies in the introduction of a well-posed TV algorithm from the point of view of convex optimization. Specifically, our proposed TV optimization algorithm for fetal reconstruction is optimal w.r.t. the asymptotic and iterative convergence speeds O(1/n(2)) and O(1/root epsilon), while existing techniques are in O(1/n) and O(1/epsilon). We apply our algorithm to (1) clinical newborn data, considered as ground truth, and (2) clinical fetal acquisitions. Our algorithm compares favorably with the literature in terms of speed and accuracy.

Simple Heuristics in a Social Game

Simple Heuristics in a Social World invites readers to discover the simple heuristics that people use to navigate the complexities and surprises of environments populated with others. The social world is a terrain where humans and other animals compete with conspecifics for myriad resources, including food, mates, and status, and where rivals grant the decision maker little time for deep thought, protracted information search, or complex calculations. Yet, the social world also encompasses domains where social animals such as humans can learn from one another and can forge alliances with one another to boost their chances of success. According to the book's thesis, the undeniable complexity of the social world does not dictate cognitive complexity as many scholars of rationality argue. Rather, it entails circumstances that render optimization impossible or computationally arduous: intractability, the existence of incommensurable considerations, and competing goals. With optimization beyond reach, less can be more. That is, heuristics--simple strategies for making decisions when time is pressing and careful deliberation an unaffordable luxury--become indispensible mental tools. As accurate as or even more accurate than complex methods when used in the appropriate social environments, these heuristics are good descriptive models of how people make many decisions and inferences, but their impressive performance also poses a normative challenge for optimization models. In short, the Homo socialis may prove to be a Homo heuristicus whose intelligence reflects ecological rather than logical rationality.