2021
DOI: 10.1016/j.media.2021.102149
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Model-based multi-parameter mapping

Abstract: Highlights Model-based estimation of quantitative multi-parameter maps. Maximum-likelihood or Maximum a posteriori solutions. Embedded denoising using a joint total-variation prior. Stable second-order solver using a novel approximate Hessian.

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Cited by 4 publications
(3 citation statements)
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References 75 publications
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“…We used a mono‐exponential fit method to calculate the R2*. Future advanced models incorporating the non‐Gaussian noise models could help to get more accurate R2* mapping (Balbastre et al, 2021 ). Third, our current study focused on healthy older participants 60–80 years of age and younger controls 21–30 years of age.…”
Section: Discussionmentioning
confidence: 99%
“…We used a mono‐exponential fit method to calculate the R2*. Future advanced models incorporating the non‐Gaussian noise models could help to get more accurate R2* mapping (Balbastre et al, 2021 ). Third, our current study focused on healthy older participants 60–80 years of age and younger controls 21–30 years of age.…”
Section: Discussionmentioning
confidence: 99%
“…Furthermore, rigid registration could be interleaved with model fitting 12 to reach a better global optimum. Finally, the generative model could naturally be integrated with any fitting approach that defines a joint probability over all acquired data, such as Balbastre et al 13 in the context of MPM.…”
Section: Discussionmentioning
confidence: 99%
“…We therefore replace the Gauss-Newton Hessian with the more robust preconditioner: ) which has been shown to yield monotonic convergence. 13 The inversion in equation (A9) is performed with a full multi-grid solver that leverages the sparsity and structure of the preconditioner. 20 Finally, a global scaling field s = exp z, applied to both the sensitivities (s kn ← s kn ∕s n ) and mean image (r n ← s n r n ), ensures that the product s kn r n is unchanged.…”
Section: F I G U R Ementioning
confidence: 99%