Bayesian inverse regression for vascular magnetic resonance fingerprinting

Fabien Boux (1) , Florence Forbes (2) , Julyan Arbel (2) , Benjamin Lemasson (3) , Emmanuel L. Barbier (4)

1 GIN - [GIN] Grenoble Institut des Neurosciences
2 STATIFY - Modèles statistiques bayésiens et des valeurs extrêmes pour données structurées et de grande dimension
3 NeuroImagerie Fonctionnelle et Perfusion Cérébrale
4 GIN - Grenoble Institut des Neurosciences

Standard parameter estimation from vascular magnetic resonance fingerprinting (MRF) data is based on matching the MRF signals to their best counterparts in a grid of coupled simulated signals and parameters, referred to as a dictionary. To reach a good accuracy, the matching requires an informative dictionary whose cost, in terms of design, storage and exploration, is rapidly prohibitive for even moderate numbers of parameters. In this work, we propose an alternative dictionary-based learning (DBL) approach made of three steps: 1) a quasi-random sampling strategy to produce efficiently an informative dictionary, 2) an inverse statistical regression model to learn from the dictionary a correspondence between fingerprints and parameters, and 3) the use of this mapping to provide both parameter estimates and their confidence indices. Our DBL method is first compared to MRF matching on two types of synthetic signals: scalable and vascular MRF signals. On scalable signals, quasi-random sampling outperforms the grid when using DBL. Dictionaries up to 100 times smaller than in MRF matching, yield a 12 % decreased error on parameter estimates. The confidence indices match the parameter estimation errors (R$^2$= 0.95). Then, on vascular signals, dictionary-based methods yield more accurate estimates than the conventional, closed-form expression fitting method with significantly smaller errors on vessel size estimates.On real vascular MRF signals acquired from tumor bearing rats, the DBL method shows less noisy maps than MRF matching. Our DBL proposal effectively reduces the number of simulations required and speeds up parameter estimation, while providing more accurate estimates with their confidence indices.

Domaines: Statistiques [math.ST]

Fabien Boux, Florence Forbes, Julyan Arbel, Benjamin Lemasson, Emmanuel L. Barbier. Bayesian inverse regression for vascular magnetic resonance fingerprinting. 2020. ⟨hal-02314026v2⟩