Traveling-wave MRI, which uses relatively small and simple RF antennae, has robust matching performance and capability for large field-of-view (FOV) imaging. However, the power efficiency of traveling-wave MRI is much lower than conventional methods, which limits its application. One simple approach to improve the power efficiency is to place passive resonators around the subject being imaged. The feasibility of this approach has been demonstrated in previous works using a single small resonant loop. In this work, we aim to explore how much the improvements can be maintained in human imaging using an array design, and whether electric dipoles can be used as local elements. First, a series of electromagnetic (EM) simulations were performed on a human model. Then RF coils were constructed and the simulation results using the best setup for head imaging were validated in MR experiments. By using the passive local loop and transverse dipole arrays, respectively, the transmit efficiency (B) of traveling-wave MRI can be improved by 3-fold in the brain and 2-fold in the knee. The types of passive elements (loops or dipoles) should be carefully chosen for brain or knee imaging to maximize the improvement, and the enhancement depends on the local body configuration.
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http://dx.doi.org/10.1016/j.mri.2017.02.003 | DOI Listing |
BMC Neurosci
October 2024
Department of Cognitive Neuroscience, Maastricht University, Oxfordlaan 55, 6228 EV, Maastricht, The Netherlands.
Hum Brain Mapp
February 2024
Centre for Cognitive and Brain Sciences, University of Macau, Taipa, China.
Natural language processing unfolds information overtime as spatially separated, multimodal, and interconnected neural processes. Existing noninvasive subtraction-based neuroimaging techniques cannot simultaneously achieve the spatial and temporal resolutions required to visualize ongoing information flows across the whole brain. Here we have developed rapid phase-encoded designs to fully exploit the temporal information latent in functional magnetic resonance imaging data, as well as overcoming scanner noise and head-motion challenges during overt language tasks.
View Article and Find Full Text PDFChaos
November 2023
Research Domain IV-Transdisciplinary Concepts & Methods, Potsdam Institute for Climate Impact Research, Potsdam D-14415, Germany.
This study presents a general framework, namely, Sparse Spatiotemporal System Discovery (S3d), for discovering dynamical models given by Partial Differential Equations (PDEs) from spatiotemporal data. S3d is built on the recent development of sparse Bayesian learning, which enforces sparsity in the estimated PDEs. This approach enables a balance between model complexity and fitting error with theoretical guarantees.
View Article and Find Full Text PDFIEEE Trans Med Imaging
September 2023
Magnetic Resonance Elastography (MRE) can characterize biomechanical properties of soft tissue for disease diagnosis and treatment planning. However, complicated wavefields acquired from MRE coupled with noise pose challenges for accurate displacement extraction and modulus estimation. Using optimization-based displacement extraction and Traveling Wave Expansion-based Neural Network (TWENN) modulus estimation, we propose a new pipeline for processing MRE images.
View Article and Find Full Text PDFResting-state functional magnetic resonance imaging (MRI) has yielded seemingly disparate insights into large-scale organization of the human brain. The brain's large-scale organization can be divided into two broad categories: zero-lag representations of functional connectivity structure and time-lag representations of traveling wave or propagation structure. In this study, we sought to unify observed phenomena across these two categories in the form of three low-frequency spatiotemporal patterns composed of a mixture of standing and traveling wave dynamics.
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