In quantitative magnetic resonance imaging, traditional methods suffer from the so-called
partial volume effect (PVE) due to spatial resolution limitations. As a consequence of PVE,
the parameters of the voxels containing more than one tissue are not correctly estimated.
Magnetic resonance fingerprinting (MRF) is not an exception. The existing methods
addressing PVE are neither scalable nor accurate. We propose to formulate the recovery of
multiple tissues per voxel as a non-convex constrained least-squares minimisation problem …

In quantitative Magnetic Resonance Imaging, traditional methods suffer from the so-called
Partial Volume Effect (PVE) due to spatial resolution limitations. As a consequence of PVE,
the parameters of the voxels containing more than one tissue are not correctly estimated.
Magnetic Resonance Fingerprinting (MRF) is not an exception. The existing methods
addressing PVE are neither scalable nor accurate. We propose to formulate the recovery of
multiple tissues per voxel as a non-convex constrained least-squares minimisation problem …