Analyzing Parallel Programming Models for Magnetic Resonance Imaging

Danford, Forest; Welch, Eric; Cárdenas-Rodríguez, Julio; Strout, Michelle Mills

doi:10.1007/978-3-319-52709-3_15

Cited by 2 publications

(2 citation statements)

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Section: Discussionmentioning

confidence: 99%

“…Julia, on the other hand, has been built as open-source from the ground up, with a specific focus on optimizing for scientific computation (33,(40)(41)(42)(43)(44). Even when fully parallelized Julia code offers order-of-magnitude speed benefits over similar Matlab implementations (24,43). Although the translation of code into Julia is already sufficient to see a significant speed increase, we further optimize the approach by employing automatic differentiation (32).…”

Section: Discussionmentioning

confidence: 99%

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Leveraging Julia’s automated differentiation and symbolic computation to increase spectral DCM flexibility and speed

Hofmann,

Chesebro,

Rackauckas

et al. 2023

Preprint

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Using neuroimaging and electrophysiological data to infer neural parameter estimations from theoretical circuits requires solving the inverse problem. Here, we provide a new Julia language package designed to i) compose complex dynamical models in a simple and modular way with ModelingToolkit.jl, ii) implement parameter fitting based on spectral dynamic causal modeling (sDCM) using the Laplace approximation, analogous to MATLAB implementation in SPM12, and iii) leverage Julia’s unique strengths to increase accuracy and speed by employing Automatic Differentiation during the fitting procedure. To illustrate the utility of our flexible modular approach, we provide a method to improve correction for fMRI scanner field strengths (1.5T, 3T, 7T) when fitting models to real data.

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