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Blood oxygenation level dependent (BOLD) contrast in functional magnetic resonance imaging (fMRI) can be enhanced using multi-echo imaging and postprocessing techniques that combine the echoes in weighted summation. Here, existing echo-weighting methods are reassessed in the context of an explicit physiological noise model, and a new method is introduced: weights that scale linearly with echo time. Additionally, a method using data-driven weights defined using principal component analysis (PCA) is included for comparison. Differences in BOLD contrast enhancement between methods were compared analytically where possible, and using Monte Carlo simulations for different noise conditions and different combinations of acquisition parameters. The comparisons were also validated through densely sampled (256-echo) multi-echo fMRI experimental data acquired at 1.5T and 3.0T. Results indicated that the contrast-to-noise ratio (CNR) of the studied weighting methods have a strong dependence on the physiological noise, echo spacing and the width of the sampling window. With low noise correlations between echoes, contrast gain for all weighting methods was shown to have a square root dependence on the echo sampling density, and in typical experimental noise conditions, increasing the sampling window beyond 3·T2* produced marginal additional benefit. Simulations and experiments also emphasized that noise correlations between echoes are likely the main factor limiting the potential CNR gains achievable by densely sampled multi-echo fMRI.

Original publication




Journal article


IEEE Trans Med Imaging

Publication Date





1691 - 1703


Brain, Echo-Planar Imaging, Female, Humans, Image Enhancement, Image Processing, Computer-Assisted, Male, Monte Carlo Method, Oxygen, Principal Component Analysis, Respiratory Transport, Sensitivity and Specificity, Young Adult