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Focal cortical dysplasia (FCD) is a malformation of cortical development and a common cause of pharmacoresistant epilepsy. Resective surgery of clear-cut lesions may be curative. However, the localization of the seizure focus and the evaluation of its spatial extent can be challenging in many situations. For concordance assessment, medical studies show the relevance of accurate correlation of multisource imaging sequences. to improve the sensitivity and specificity of the evaluation. In this paper, we share the process we went through to reach our simple, but effective, solution for integrating multi-volume rendering into an exploratory visualization environment for the diagnosis of FCD. We focus on fetching of multiple data assigned to a sample when they are rendered. Knowing that the major diagnostic role of multiple volumes is to complement information, we demonstrate that appropriate geometric transformations in the texture space are sufficient for accomplishing this task. This allows us to fully implement our proposal in the OpenGL rendering pipeline and to easily integrate it into the existing visual diagnostic application. Both time performance and the visual quality of our proposal were evaluated with a set of clinical data volumes for assessing the potential practical impact of our solution in routine diagnostic use.

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As the imaging technology and the understanding of neurological disease improve, a solid understanding of neu-roanatomy has become increasingly relevant. Neuroanatomy teaching includes the practice of cadaveric dissectionand neuroanatomy atlases consisting of images of a brain with its labeled structures. However, the natural inter-individual neuroanatomical variability cannot be taken into account. This work addresses the individual grossneuroanatomy atlas that could enrich medical students’ experiences with various individual variations in anatomi-cal landmarks and their spatial relationships. We propose to deform the CerebrA cortical atlas into the individualanatomical magnetic resonance imaging data to increase students’ opportunity to contact normal neuroanatomicalvariations in the early stages of studies. Besides, we include interactive queries on the labels/names of neu-roanatomical structures from an individual neuroanatomical atlas in a 3D space. An implementation on top ofSimpleITK library and VMTK-Neuro software is presented. We generated a series of surface and internal neu-roanatomy maps from 16 test volumes to attest to the potential of the proposed technique in brain labeling. Forthe age group between 10 to 75, there is evidence that the superficial cortical labeling is accurate with the visualassessment of the degree of concordance between the neuroanatomical and label boundaries.