Samuel St-Jean1,2, Filip Szczepankiewicz2, Christian Beaulieu1, and Markus Nilsson2
1University of Alberta, Edmonton, AB, Canada, 2Clinical Sciences Lund, Lund University, Lund, Sweden
1University of Alberta, Edmonton, AB, Canada, 2Clinical Sciences Lund, Lund University, Lund, Sweden
A method is presented to efficiently compute and visualize how well a given diffusion MRI model is represented by a given acquisition scheme. Results with tensor-valued diffusion encoding literally show the dimensions accessible by a given protocol.
Figure 3: The first eigenvectors for the HCP dataset and for the multiple TE dataset. The constrained model is described by 2 components, despite being a four-parameter model. The first 5 eigenvectors of the unconstrained model show little contrast among themselves, suggesting that the acquisition cannot disentangle all model parameters. Bottom row shows the eigenvectors for data with varying b-tensors and TE. The contrast between images shows that additional information is captured by the acquisition.
Figure 1: Parameter maps (diffusivities in mm2/s) obtained with maximizing the inner product for the two HCP datasets (top and middle row) and for the b-tensor dataset (bottom row). The constrained estimation (top row) enforces Dpar > Dperp, leading to coherent-looking parameter maps by forcing the model into a lower dimensional space, which is not supported as well by the unconstrained version. The b-tensor encoded dataset suffers less from this degeneracy by providing an additional sample dimension.