Recent advances in brain MRI have provided large amount of data with an increasingly high level of quality but analysis of large and complex MRI datasets is onerous for clinicians, who still extract information manually. In brain MRI analysis, image segmentation is used for measuring and visualizing the brain’s anatomical structures, analyzing changes and identification of pathological regions, as well as for surgical planning and image-guided interventions. Image segmentation is crucial in medical image analysis and is perhaps the most critical step in many clinical applications ( Despotović et al., 2015). For understanding the interaction between brain areas and regions, subcortical nuclei, gyri and sulci, the resolution appears to be sufficient ( Toga et al., 2006). The spatial resolution is about 1 mm for structural imaging and is below the cellular scale ( Roland & Zilles, 1994). Computerized brain atlases are also used for topographically defined data from the literature ( Roland & Zilles, 1994). To make assumptions about localization of function and structure at both the macroscopic and microscopic levels, computerized brain atlases are needed. To construct brain atlases, collections of micrographs or schematic drawings of brain sections from one or a few brains are used in which anatomical structures such as nuclei, cortical ribbon or tracts, are identified ( Roland & Zilles, 1994). This enables such atlases to become plastic or deformable to fit the size/shape of individual brains. With rapid strides made in computer-based technologies, brain atlases are ‘constructed’ by computers. Our study could potentially be useful in building a platform for patient-specific treatment options based on 3D analysis of brain disease, particularly in acute settings such as stroke, mass effect of tumors, midline shift in patients with acute intracerebral hemorrhage, among others. As an alternative, patient-specific modeling (PSM) can be used as an analytical tool to optimize an individual's therapy. However, most results might not apply directly to individual patients yet because they are based on averages ( Kent & Hayward, 2007). For instance, the accurate prediction of rupture of abdominal aortic aneurysm is possible through patient-based diagnostic tools coupled to medical imaging ( Ricotta et al., 2008). For starters, the potential to improve diagnosis and optimize clinical treatment by predicting outcomes of therapies is attainable. The use of MRI in tracking disease of the human brain and spinal cord in patients with MS is central to the diagnosis and treatment of the disease.ĭevelopment of computational models for patient-specific requirements based on human pathophysiology individualized to patient-specific data is needed as we move forward with advanced techniques such as 3D printing in medicine. Gray matter disease in MS is poorly visualized in conventional MRI but has been increasingly studied in recent years using high strength magnets ( de Graaf et al., 2013). Multiple sclerosis (MS) is a chronic, white and gray matter disease of the central nervous system. Medical teaching is moored in 2D graphics and it is time to evolve into 3D models that can be life-like and deliver instant impact. The purpose of our study was to demonstrate that 3D depiction of chronic neurological diseases is possible in a printable model while serving a fundamental need for patient education. Rendering brain tumor(s) in 3D has been attempted with the specific intent of extending the options available to a surgeon but no study to our knowledge has attempted to quantify brain disease in MS that has, for all practical purposes, no surgical options. The imaging data were then segmented into regions and surface rendering was done to achieve 3D virtual printable files of the desired structures of interest. The imported axial images were automatically formatted to display coronal and sagittal slices within the software. The patient’s images in Digital Imaging and Communications in Medicine (DICOM) format were imported into Mimics inPrint 2.0 (Materialise NV, Leuven, Belgium) a dedicated medical image processing software designed for the purposes of image segmentation and 3D modeling. It is a first of its kind model that depicts the total white matter lesion (WML) load using T2 FLAIR images in an MS patient. Using 3D reconstruction algorithms, we built a 3D printed patient-specific brain model to scale. Conventional magnetic resonance imaging (MRI) studies depict disease of the human brain in 2D but the reconstruction of a patient’s brain stricken with multiple sclerosis (MS) in 3D using 2D images has not been attempted.
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