Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting

Jan 25, 2020 Blind Submission readers: everyone Show Bibtex
  • Abstract: Magnetic Resonance Fingerprinting (MRF) enables the simultaneous quantification of multiple properties of biological tissues. It relies on a pseudo-random acquisition and the matching of acquired signal evolutions to a precomputed dictionary. However, the dictionary is not scalable to higher-parametric spaces, limiting MRF to the simultaneous mapping of only a small number of parameters (proton density, T1 and T2 in general). Inspired by diffusion-weighted SSFP imaging, we present a proof-of-concept of a novel MRF sequence with embedded diffusion-encoding gradients along all three axes to efficiently encode orientational diffusion and T1 and T2 relaxation. We take advantage of a convolutional neural network (CNN) to reconstruct multiple quantitative maps from this single, highly undersampled acquisition. We bypass expensive dictionary matching by learning the implicit physical relationships between the spatiotemporal MRF data and the T1, T2 and diffusion tensor parameters. The predicted parameter maps and the derived scalar diffusion metrics agree well with state-of-the-art reference protocols. Orientational diffusion information is captured as seen from the estimated primary diffusion directions. In addition to this, the joint acquisition and reconstruction framework proves capable of preserving tissue abnormalities in multiple sclerosis lesions.
  • Paper Type: methodological development
  • TL;DR: We present a novel MR Fingerprinting scheme that is simultaneously sensitive to T1 and T2 relaxation and orientational diffusion and combine it with a CNN to jointly map relaxation parameters together with the diffusion tensor.
  • Track: full conference paper
  • Keywords: Magnetic Resonance Fingerprinting, Convolutional Neural Network, Image Reconstruction, Diffusion Tensor, Multiple Sclerosis
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