Error Measurement Between Anatomical Porcine Spine, CT Images, and 3D Printing.

RATIONALE AND OBJECTIVES: 3D printers are increasingly used in medical applications such as surgical planning, creation of implants and prostheses, and medical education. For the creation of reliable 3D printed models of the vertebral column, processing must be performed on CT images. This processing must be assessed and validated so that any error of the printed model can be recognized and minimized. MATERIAL AND METHODS: In order to perform this validation, 10 CT scans of porcine lumbar spinal vertebra were used, which were then dissected and scanned again. CT image processing was performed to obtain a mesh and perform 3D printing. RESULTS: There was no statistical difference among the four different levels of vertebrae measurements (first CT images, second CT images, anatomical piece of porcine bone and 3D printing of porcine bone; One Way repeated measure ANOVA, F < F_crit, p value > α = 0.05). The Intraclass Correlation also revealed a mean intraclass correlation coefficient (3,1) = 0.9553, which describes the reliability of all four levels in addition to the reliability of the data between porcine samples subjected to different levels of measurement. This shows that the average error is less than 1 mm. CONCLUSIONS: The measurements of models created with 3D printers using the pipeline described in this paper have an average error of 0.60 mm with CT images and 0.73 mm with anatomical piece. Thus, 3D printed models accurately reflect in vivo bones and provide accurate 3D impressions to assist in surgical planning.

Study of Resting-State Functional Connectivity Networks Using EEG Electrodes Position As Seed.

Electroencephalography (EEG) is the standard diagnosis method for a wide variety of diseases such as epilepsy, sleep disorders, encephalopathies, and coma, among others. Resting-state functional magnetic resonance (rs-fMRI) is currently a technique used in research in both healthy individuals as well as patients. EEG and fMRI are procedures used to obtain direct and indirect measurements of brain neural activity: EEG measures the electrical activity of the brain using electrodes placed on the scalp, and fMRI detects the changes in blood oxygenation that occur in response to neural activity. EEG has a high temporal resolution and low spatial resolution, while fMRI has high spatial resolution and low temporal resolution. Thus, the combination of EEG with rs-fMRI using different methods could be very useful for research and clinical applications. In this article, we describe and show the results of a new methodology for processing rs-fMRI using seeds positioned according to the 10-10 EEG standard. We analyze the functional connectivity and adjacency matrices obtained using 65 seeds based on 10-10 EEG scheme and 21 seeds based on 10-20 EEG. Connectivity networks are created using each 10-20 EEG seeds and are analyzed by comparisons to the seven networks that have been found in recent studies. The proposed method captures high correlation between contralateral seeds, ipsilateral and contralateral occipital seeds, and some in the frontal lobe.