Aurélien Delphin1, Fabien Boux1,2, Clément Brossard1, Jan M Warnking1, Benjamin Lemasson1, Emmanuel Luc Barbier1, and Thomas Christen1
1Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, GIN, 38000, Grenoble, France, 2Univ. Grenoble Alpes, Inria, CNRS, G-INP, 38000, Grenoble, France
1Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, GIN, 38000, Grenoble, France, 2Univ. Grenoble Alpes, Inria, CNRS, G-INP, 38000, Grenoble, France
We successfully tested a Monte-Carlo framework in the context of MR vascular fingerprinting to assess the encoding capacity of MRF sequences. We showed the clear influence of the vascular geometry in the simulations.
Figure 1: Reconstruction errors obtained for each sequence and each pattern, as well as an example of noised fingerprint. The signal annulations in the qRF-MRF sequence made the ratio impracticable. Direct match on 2D-based dictionaries, 10 000 signals each.
Figure 4: Results obtained with the GESFIDSE (TE=60ms) concatenation pattern. Vf estimates on animal data with the different dictionaries generated, as well as examples of the geometries. Regression-based reconstruction on 3D-based dictionary, 15 000 signals each.
