Publication
FID-guided retrospective motion correction based on autofocusing
Résumé
This work investigates the possibility of using FID navigator signals to improve the performance of a recently proposed autofocusing-based retrospective motion correction technique. FID navigators were incorporated into an MP-RAGE sequence and 3 subjects were scanned at 3T while performing head movements. The acquired data was retrospectively corrected for motion by exploiting the FID signals to constrain the unknown motion parameters. The results were compared against the reconstructions obtained from a non-FID-guided version of the algorithm, demonstrating that the use of FID navigators for retrospective motion correction leads to improvement in both image quality and computation time.
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Concepts associés (18)
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A computation is any type of arithmetic or non-arithmetic calculation that is well-defined. Common examples of computations are mathematical equations and computer algorithms. Mechanical or electronic devices (or, historically, people) that perform computations are known as computers. The study of computation is the field of computability, itself a sub-field of computer science. The notion that mathematical statements should be ‘well-defined’ had been argued by mathematicians since at least the 1600s, but agreement on a suitable definition proved elusive.
Computational complexity
In computer science, the computational complexity or simply complexity of an algorithm is the amount of resources required to run it. Particular focus is given to computation time (generally measured by the number of needed elementary operations) and memory storage requirements. The complexity of a problem is the complexity of the best algorithms that allow solving the problem. The study of the complexity of explicitly given algorithms is called analysis of algorithms, while the study of the complexity of problems is called computational complexity theory.
Theory of computation
In theoretical computer science and mathematics, the theory of computation is the branch that deals with what problems can be solved on a model of computation, using an algorithm, how efficiently they can be solved or to what degree (e.g., approximate solutions versus precise ones). The field is divided into three major branches: automata theory and formal languages, computability theory, and computational complexity theory, which are linked by the question: "What are the fundamental capabilities and limitations of computers?".
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