TY - JOUR

T1 - Enforcing necessary non-negativity constraints for common diffusion MRI models using sum of squares programming

AU - Dela Haije, Tom

AU - Ozarslan, Evren

AU - Feragen, Aasa

PY - 2020

Y1 - 2020

N2 - In this work we investigate the use of sum of squares constraints for various diffusion-weighted MRI models, with a goal of enforcing strict, global non-negativity of the diffusion propagator. We formulate such constraints for the mean apparent propagator model and for spherical deconvolution, guaranteeing strict non-negativity of the corresponding diffusion propagators. For the cumulant expansion similar constraints cannot exist, and we instead derive a set of auxiliary constraints that are necessary but not sufficient to guarantee non-negativity. These constraints can all be verified and enforced at reasonable computational costs using semidefinite programming. By verifying our constraints on standard reconstructions of the different models, we show that currently used weak constraints are largely ineffective at ensuring non-negativity. We further show that if strict non-negativity is not enforced then estimated model parameters may suffer from significant errors, leading to serious inaccuracies in important derived quantities such as the main fiber orientations, mean kurtosis, etc. Finally, our experiments confirm that the observed constraint violations are mostly due to measurement noise, which is difficult to mitigate and suggests that properly constrained optimization should currently be considered the norm in many cases.

AB - In this work we investigate the use of sum of squares constraints for various diffusion-weighted MRI models, with a goal of enforcing strict, global non-negativity of the diffusion propagator. We formulate such constraints for the mean apparent propagator model and for spherical deconvolution, guaranteeing strict non-negativity of the corresponding diffusion propagators. For the cumulant expansion similar constraints cannot exist, and we instead derive a set of auxiliary constraints that are necessary but not sufficient to guarantee non-negativity. These constraints can all be verified and enforced at reasonable computational costs using semidefinite programming. By verifying our constraints on standard reconstructions of the different models, we show that currently used weak constraints are largely ineffective at ensuring non-negativity. We further show that if strict non-negativity is not enforced then estimated model parameters may suffer from significant errors, leading to serious inaccuracies in important derived quantities such as the main fiber orientations, mean kurtosis, etc. Finally, our experiments confirm that the observed constraint violations are mostly due to measurement noise, which is difficult to mitigate and suggests that properly constrained optimization should currently be considered the norm in many cases.

KW - Constrained optimization

KW - Cumulant expansion

KW - Diffusion MRI

KW - Diffusional kurtosis imaging

KW - Diffusion tensor imaging

KW - Mean apparent propagator

KW - Sampling scheme design

KW - Semidefinite programming

KW - Spherical deconvolution

KW - Sum of squares optimization

KW - Sum of squares polynomials

U2 - 10.1016/j.neuroimage.2019.116405

DO - 10.1016/j.neuroimage.2019.116405

M3 - Journal article

C2 - 31846758

VL - 209

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

M1 - 116405

ER -