Biomimetic phantom with anatomical accuracy for evaluating brain volumetric measurements with magnetic resonance imaging

Abstract

Purpose: Brain image volumetric measurements (BVM) methods have been used to quantify brain tissue volumes using magnetic resonance imaging (MRI) when investigating abnormalities. Although BVM methods are widely used, they need to be evaluated to quantify their reliability. Currently, the gold-standard reference to evaluate a BVM is usually manual labeling measurement. Manual volume labeling is a time-consuming and expensive task, but the confidence level ascribed to this method is not absolute. We describe and evaluate a biomimetic brain phantom as an alternative for the manual validation of BVM. Methods: We printed a three-dimensional (3D) brain mold using an MRI of a three-year-old boy diagnosed with Sturge-Weber syndrome. Then we prepared three different mixtures of styrene-ethylene/butylene-styrene gel and paraffin to mimic white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The mold was filled by these three mixtures with known volumes. We scanned the brain phantom using two MRI scanners, 1.5 and 3.0 Tesla. Our suggestion is a new challenging model to evaluate the BVM which includes the measured volumes of the phantom compartments and its MRI. We investigated the performance of an automatic BVM, i.e., the expectation-maximization (EM) method, to estimate its accuracy in BVM. Results: The automatic BVM results using the EM method showed a relative error (regarding the phantom volume) of 0.08, 0.03, and 0.13 ( $\pm 0.03$ uncertainty) percentages of the GM, CSF, and WM volume, respectively, which was in good agreement with the results reported using manual segmentation. Conclusions: The phantom can be a potential quantifier for a wide range of segmentation methods.

Keywords: brain volume measurements; evaluation method; magnetic resonance imaging; physical phantom.

Fig. 1

A pipeline for volume estimation using the image segmentation method. In this schematic, the volumes of three main brain compartments (WM, GM, and CSF) are estimated using a label map.

Fig. 2

Evaluation of a brain segmentation method using DICE (a similarity coefficient). The calculated coefficient shows the similarity between label maps and the gold standard.

Fig. 3

A part of the axial view of a brain image. Yellow rectangular shows a part of the MRI including two tissues. Red rectangular shows 12 pixels with different intensities. It is difficult for human eyes to choose a very similar pixel in the border for labeling because eyes may not be able to distinguish the difference between them.

Fig. 4

(a) Simulated brain; (b) healthy brain; and (c) patient brain with Sturge-Weber disease. Atrophy can be seen on the left side of the patient’s brain.

Fig. 5

Hypothalamus phantom made by one sample (90% SEBS gel mixed to 10% paraffin wax) covered by 30% and 10% paraffin, respectively. Top left axial, right 3D, down left the sagittal and downright coronal view of the acquired image in 3DSlicer software. Nine different combinations of SEBS gel and paraffin wax are surrounding a hypothalamus. The yellow arrows show the bubbles.

Fig. 6

(a) Complete phantom of the infant’s brain with three materials mimics the real brain. N, noise and B, bubbles. (b) The bias effect took place in the brain phantom.

Fig. 7

(a) Original acquired image of brain phantom; (b) label map of phantom by EM-algorithm in 3DSlicer [▪ CSF (blue), ▪ GM (Gray), and ▪ WM (yellow)]; and (c) red arrows show the bubbles, which were detected as GM by EM.

Fig. 8

(a) Hypothalamus phantom covered by two samples surrounded by nine different combinations of SEBS gel ($G$) and paraffin ($P$). (b) Brain phantom of the infant, diagnosed by Sturge-Weber syndrome, made by three samples ($G=100\%$, 70%, and 20% with $P=0\%$, 30%, and 80%, respectively).