Inverse regression in MR Fingerprinting: reducing dictionary size while increasing parameters accuracy

Abstract : Purpose: To reduce dictionary size and increase parameter estimate accuracy in MR Fingerprinting (MRF). Methods: A dictionary-based learning (DBL) method is investigated to bypass inherent MRF limitations in high dimension: reconstruction time and memory requirement. The DBL method is a 3-step procedure: (1) a quasi-random sampling strategy to produce the dictionary, (2) a statistical inverse regression model to learn from the dictionary a probabilistic mapping between MR fingerprints and parameters, and (3) this mapping to provide both parameter estimates and their confidence levels. Results: On synthetic data, experiments show that the quasi-random sampling outperforms the grid when designing the dictionary for inverse regression. Dictionaries up to 100 times smaller than usually employed in MRF yield more accurate parameter estimates with a 500 time gain. Estimates are supplied with a confidence index, well correlated with the estimation bias (r~$\ge$~0.89). On microvascular MRI data, results show that dictionary-based methods (MRF and DBL) yield more accurate estimates than the conventional, closed-form equation, method.On MRI signals from tumor bearing rats, the DBL method shows very little sensitivity to the dictionary size in contrast to the MRF method. Conclusion: The proposed method efficiently reduces the number of required simulations to produce the dictionary, speeds up parameter estimation, and improve estimates accuracy. The DBL method also introduces a confidence index for each parameter estimate.
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Contributor : Florence Forbes <>
Submitted on : Friday, October 11, 2019 - 4:38:18 PM
Last modification on : Thursday, October 17, 2019 - 11:38:43 AM


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  • HAL Id : hal-02314026, version 1



Fabien Boux, Florence Forbes, Julyan Arbel, Emmanuel Barbier. Inverse regression in MR Fingerprinting: reducing dictionary size while increasing parameters accuracy. 2019. ⟨hal-02314026⟩



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