Reducing the number of samples in spatiotemporal dMRI acquisition design

Abstract

Purpose Acquisition time is a major limitation in recovering brain white matter microstructure with diffusion magnetic resonance imaging. The aim of this paper is to bridge the gap between growing demands on spatiotemporal resolution of diffusion signal and the real‐world time limitations. The authors introduce an acquisition scheme that reduces the number of samples under adjustable quality loss. Methods Finding a sampling scheme that maximizes signal quality and satisfies given time constraints is NP‐hard. Therefore, a heuristic method based on genetic algorithm is proposed in order to find suboptimal solutions in acceptable time. The analyzed diffusion signal representation is defined in the qτ space, so that it captures both spacial and temporal phenomena. Results The experiments on synthetic data and in vivo diffusion images of the C57Bl6 wild‐type mouse corpus callosum reveal superiority of the proposed approach over random sampling and even distribution in the qτ space. Conclusions The use of genetic algorithm allows to find acquisition parameters that guarantee high signal reconstruction accuracy under given time constraints. In practice, the proposed approach helps to accelerate the acquisition for the use of qτ‐dMRI signal representation.

Machine learning based white matter models with permeability: An experimental study in cuprizone treated in-vivo mouse model of axonal demyelination

“…We use the same DW-PGSE protocol for the synthetic and in-vivo data, optimised to maximise signal reconstruction accuracy under realistic time constraints (Filipiak et al, 2019). Our imaging protocol has 25 shells, each with one b=0 measurement and a different combination of diffusion gradient strength G and diffusion gradient separation Δ as summarised in Table 1 .…”

Systematic review of reconstruction techniques for accelerated quantitative MRI

“…320 However, given the many possible combinations of acquisition settings and undersampling patterns, empirical optimization of in vivo precision is impractical. Hence, efforts for in-silico evaluation, such as predicting time efficiency, 12,266,[320][321][322][323][324][325] or accuracy, 118 are relevant. Recently, automated learning-based methodologies have been proposed to select an optimal sampling strategy independent of the model.…”

“Select and retrieve via direct upsampling” network (SARDU-Net): a data-driven, model-free, deep learning approach for quantitative MRI protocol design

“…a situation quickly reached for M ~ 30). Nonetheless, we acknowledge that different design choices could be equally valid, as for example genetic searches (33) for the selection stage. In future we will improve the performance and stability of SARDU-Net, for instance by replacing the simple grid search used here to design SARDU-Net architecture and learning options.…”

“…Examples include the design of optimal diffusion-weighting protocols (4,5,23–26); number and spacing of temporal sampling in relaxometry (27,28); DRI sampling (18). These previous studies adopt different optimisation strategies, such as Cramér-Rao lower bound (CRLB) minimisation based on Fisher information (29), Monte Carlo (MC) samplings (30), mutual information computation (31), empirical approaches (32) or discrete searches (33). Importantly, these previous optimisation approaches rely on fixed, a priori representation of measured signals, such as biophysical models (e.g.…”