Quantitative T1 estimation from T1-weighted images affected by partial volume effects

Abstract

Event Abstract Back to Event Quantitative T1 estimation from T1-weighted images affected by partial volume effects Piet Bladt1*, Gabriel Ramos Llordén1, Gwendolyn Van Steenkiste1, Jan Sijbers1 and Arjan Den Dekker1 1 Vision Lab, University of Antwerp, Department of Physics, Belgium Fundamental MRI parameters, such as the spin-lattice relaxation time T1, reflect the local tissue structure and their quantification provides valuable clinical information. Specifically, quantification of T1 has significant value in many brain disorders, such as multiple sclerosis, tumors and stroke. T1 values can be retrieved by fitting a voxel-wise model to a set of T1-weighted MR images, referred to as T1 mapping. Conventionally, a mono-exponential model is chosen to describe the T1 relaxation. However, many voxels consist of two tissue types, in particular at a tissue border, a phenomenon called partial volume effect (PVE). For voxels suffering from PVEs, the mono-exponential estimator will yield an inaccurate T1 value. For such voxels, a bi-exponential model is needed to account for PVEs. Unfortunately, single-voxel bi-exponential estimators (SBEs) are inaccurate and imprecise in clinically realistic circumstances. We propose a joint multi-voxel bi-exponential estimator (JMBE) assuming that a cluster of four neighbouring voxels contains two tissue types with two global T1 values. The JMBE is constructed based on a bias-corrected maximum likelihood (ML) estimation framework. To assess its accuracy and efficiency at certain fixed imaging settings, the JMBE is compared to an ML SBE using numerical brain phantom data and a Cramér-Rao lower bound analysis. We show that the JMBE is accurate and efficient for clinically realistic single-voxel signal-to-noise ratios (SNRs), in contrast to the SBE. This demonstrates the need to incorporate information of neighbouring voxels for accurate bi-exponential T1 estimation.