An MRI digital brain phantom for validation of segmentation methods

Knowledge of the exact spatial distribution of brain tissues in images acquired by magnetic resonance imaging (MRI) is necessary to measure and compare the performance of segmentation algorithms. Currently available physical phantoms do not satisfy this requirement. State-of-the-art digital brain phantoms also fall short because they do not handle separately anatomical structures (e.g. basal ganglia) and provide relatively rough simulations of tissue fine structure and inhomogeneity. We present a software procedure for the construction of a realistic MRI digital brain phantom. The phantom consists of hydrogen nuclear magnetic resonance spin–lattice relaxation rate (R1), spin–spin relaxation rate (R2), and proton density (PD) values for a 24 × 19 × 15.5 cm volume of a “normal” head. The phantom includes 17 normal tissues, each characterized by both mean value and variations in R1, R2, and PD. In addition, an optional tissue class for multiple sclerosis (MS) lesions is simulated. The phantom was used to create realistic magnetic resonance (MR) images of the brain using simulated conventional spin-echo (CSE) and fast field-echo (FFE) sequences. Results of mono-parametric segmentation of simulations of sequences with different noise and slice thickness are presented as an example of possible applications of the phantom. The phantom data and simulated images are available online at http://lab.ibb.cnr.it/.

An innovative digital phantom, based on the relaxometric features of the brain, allows to simulate realistic MRI studies, which are suitable to assess, on a voxel by voxel basis, the accuracy of segmentation methods.Figure optionsDownload high-quality image (115 K)Download as PowerPoint slideResearch highlights
► Ground truth is necessary to compare the performance of segmentation algorithms.
► A new digital phantom based on relaxometric tissue characteristics is proposed.
► The phantom was used to create realistic magnetic resonance brain images.
► The simulated datasets allow a voxel by voxel verification of tissue classification.