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Angela R. Laird

Publications and source records attributed to Angela R. Laird.

3 recordsLinked to original sources

Twist-Tuned Bilayer Metasurface for 3T MRI

Magnetic resonance imaging (MRI) can see deep inside the body without ionizing radiation, but image quality depends strongly on how well the radio-frequency field is controlled. Passive resonant pads and metasurfaces can help, yet they often lose their tuning when they are placed next to water-rich tissue or tissue-like materials. Here we show a simple way to bring such a device back into tune. We built a bilayer metasurface made of two aluminum wire arrays. One layer can rotate relative to the other, and the gap between the two layers can also be adjusted. Bench measurements show that adding a controlled water load shifts the resonance to lower frequency by about \SIrange{4.2}{11.4}{\mega\hertz}. Rotating the layers shifts it back by about \SIrange{13.2}{14.9}{\mega\hertz}, which is much stronger than changing the gap alone. One loaded setting lands essentially at the proton frequency used in \SI{3}{\tesla} MRI. In a proof-of-concept scan on a clinical \SI{3}{\tesla} system, the metasurface made internal features in a structured pineapple phantom easier to see than in a substrate-only control. These results show that a passive MRI metasurface can be tuned after fabrication and retuned under load using geometry alone, opening a practical route to simple adjustable RF accessories for MRI.

physics.optics

The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.

q-bio.OT

Neuroimaging Meta Regression for Coordinate Based Meta Analysis Data with a Spatial Model

Coordinate-based meta-analysis combines evidence from a collection of Neuroimaging studies to estimate brain activation. In such analyses, a key practical challenge is to find a computationally efficient approach with good statistical interpretability to model the locations of activation foci. In this article, we propose a generative coordinate-based meta-regression (CBMR) framework to approximate smooth activation intensity function and investigate the effect of study-level covariates (e.g., year of publication, sample size). We employ spline parameterization to model spatial structure of brain activation and consider four stochastic models for modelling the random variation in foci. To examine the validity of CBMR, we estimate brain activation on $20$ meta-analytic datasets, conduct spatial homogeneity tests at voxel level, and compare to results generated by existing kernel-based approaches.

stat.ME