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 ( ± 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.
ABSTRACT
BACKGROUND: Training in medical education depends on the availability of standardized materials that can reliably mimic the human anatomy and physiology. One alternative to using cadavers or animal bodies is to employ phantoms or mimicking devices. Styrene-ethylene/butylene-styrene (SEBS) gels are biologically inert and present tunable properties, including mechanical properties that resemble the soft tissue. Therefore, SEBS is an alternative to develop a patient-specific phantom, that provides real visual and morphological experience during simulation-based neurosurgical training. RESULTS: A 3D model was reconstructed and printed based on patient-specific magnetic resonance images. The fused deposition of polyactic acid (PLA) filament and selective laser sintering of polyamid were used for 3D printing. Silicone and SEBS materials were employed to mimic soft tissues. A neuronavigation protocol was performed on the 3D-printed models scaled to three different sizes, 100%, 50%, and 25% of the original dimensions. A neurosurgery team (17 individuals) evaluated the phantom realism as "very good" and "perfect" in 49% and 31% of the cases, respectively, and rated phantom utility as "very good" and "perfect" in 61% and 32% of the cases, respectively. Models in original size (100%) and scaled to 50% provided a quantitative and realistic visual analysis of the patient's cortical anatomy without distortion. However, reduction to one quarter of the original size (25%) hindered visualization of surface details and identification of anatomical landmarks. CONCLUSIONS: A patient-specific phantom was developed with anatomically and spatially accurate shapes, that can be used as an alternative for surgical planning. Printed models scaled to sizes that avoided quality loss might save time and reduce medical training costs.
ABSTRACT
Abstract Introduction Magneto-motive ultrasound (MMUS) combines magnetism and ultrasound (US) to detect magnetic nanoparticles in soft tissues. One type of MMUS called shear-wave dispersion magneto-motive ultrasound (SDMMUS) analyzes magnetically induced shear waves (SW) to quantify the elasticity and viscosity of the medium. The lack of an established presets or protocols for pre-clinical and clinical studies currently limits the use of MMUS techniques in the clinical setting. Methods This paper proposes a platform to acquire, process, and analyze MMUS and SDMMUS data integrated with a clinical ultrasound equipment. For this purpose, we developed an easy-to-use graphical user interface, written in C++/Qt4, to create an MMUS pulse sequence and collect the ultrasonic data. We designed a graphic interface written in MATLAB to process, display, and analyze the MMUS images. To exemplify how useful the platform is, we conducted two experiments, namely (i) MMUS imaging to detect magnetic particles in the stomach of a rat, and (ii) SDMMUS to estimate the viscoelasticity of a tissue-mimicking phantom containing a spherical target of ferrite. Results The developed software proved to be an easy-to-use platform to automate the acquisition of MMUS/SDMMUS data and image processing. In an in vivo experiment, the MMUS technique detected an area of 6.32 ± 1.32 mm2 where magnetic particles were heterogeneously distributed in the stomach of the rat. The SDMMUS method gave elasticity and viscosity values of 5.05 ± 0.18 kPa and 2.01 ± 0.09 Pa.s, respectively, for a tissue-mimicking phantom. Conclusion Implementation of an MMUS platform with addressed presets and protocols provides a step toward the clinical implementation of MMUS imaging equipment. This platform may help to localize magnetic particles and quantify the elasticity and viscosity of soft tissues, paving a way for its use in pre-clinical and clinical studies.
