Deep learning in neuroimaging of epilepsy.

In recent years, artificial intelligence, particularly deep learning (DL), has demonstrated utility in diverse areas of medicine. DL uses neural networks to automatically learn features from the raw data while this is not possible with conventional machine learning. It is helpful for the assessment of patients with epilepsy and whilst most published studies have been aimed at the automatic detection and prediction of seizures from electroencephalographic records, there is a growing number of investigations that use neuroimaging modalities (structural and functional magnetic resonance imaging, diffusion-weighted imaging and positron emission tomography) as input data. We review the application of DL to neuroimaging (sMRI, fMRI, DWI and PET) of focal epilepsy, specifically presurgical evaluation of drug-refractory epilepsy. First, a brief theoretical overview of artificial neural networks and deep learning is presented. Next, we review applications of deep learning to neuroimaging of epilepsy: diagnosis and lateralization, automated detection of lesion, presurgical evaluation and prediction of postsurgical outcome. Finally, the limitations, challenges and possible future directions in the application of these methods in the study of epilepsies are discussed. This approach could become an essential tool in clinical practice, particularly in the evaluation of images considered negative by visual inspection, in individualized treatments, and in the approach to epilepsy as a network disorder. However, greater multicenter collaboration is required to achieve the collection of sufficient data with the required quality together with the open access availability of the developed codes and tools.

Relation of Brain Perfusion Patterns to Sudden Unexpected Death Risk Stratification: A Study in Drug Resistant Focal Epilepsy.

To explore the role of the interictal and ictal SPECT to identity functional neuroimaging biomarkers for SUDEP risk stratification in patients with drug-resistant focal epilepsy (DRFE). Twenty-nine interictal-ictal Single photon emission computed tomography (SPECT) scans were obtained from nine DRFE patients. A methodology for the relative quantification of cerebral blood flow of 74 cortical and sub-cortical structures was employed. The optimal number of clusters (K) was estimated using a modified v-fold cross-validation for the use of K means algorithm. The two regions of interest (ROIs) that represent the hypoperfused and hyperperfused areas were identified. To select the structures related to the SUDEP-7 inventory score, a data mining method that computes an automatic feature selection was used. During the interictal and ictal state, the hyperperfused ROIs in the largest part of patients were the bilateral rectus gyrus, putamen as well as globus pallidus ipsilateral to the seizure onset zone. The hypoperfused ROIs included the red nucleus, substantia nigra, medulla, and entorhinal area. The findings indicated that the nearly invariability in the perfusion pattern during the interictal to ictal transition observed in the ipsi-lateral putamen F = 12.60, p = 0.03, entorhinal area F = 25.80, p = 0.01, and temporal middle gyrus F = 12.60, p = 0.03 is a potential biomarker of SUDEP risk. The results presented in this paper allowed identifying hypo- and hyperperfused brain regions during the ictal and interictal state potentially related to SUDEP risk stratification.

Herramienta basada en código abierto para el cálculo de blindaje en las instalaciones de Medicina Nuclear / Open Source Based Tool for Shielding Calculation in Nuclear Medicine Installations

Uno de los requerimientos indispensables en el diseño de las instalaciones donde se trabaja con radiación ionizante es la determinación del espesor adecuado de las paredes, pisos, techo y puertas de los locales, que garanticen dosis por debajo de las restricciones establecidas por la autoridad regulatoria. El objetivo del presente trabajo es desarrollar una herramienta interactiva, libre y de código abierto para calcular los blindajes requeridos en una instalación de Medicina Nuclear. En el código, desarrollado en Phyton utilizando el entorno interactivo Jupiter Notebook, se incluyó el análisis tanto para Tomografía por Emisión de Fotón Único como para Tomografía por Emisión de Positrones. La herramienta fue implementada para el cálculo de los blindajes de un departamento de Medicina Nuclear del Centro Internacional de Restauración Neurológica (CIREN). Esta herramienta libre y de código abierto facilita los cálculos de blindaje aumentando la velocidad, lo que contribuye a lograr una optimización de la protección radiológica, pero también puede usarse como herramienta pedagógica(AU)

One of the indispensable requirements in the design of the facilities where ionizing radiation is used is the determination of the adequate thickness of the walls, floors, ceiling and doors of the premises, which guarantee doses below the restrictions established by the regulatory authority. The goal of this work is to develop an interactive, free and open source tool to calculate the shields required in a Nuclear Medicine installation. Analysis for both Single Photon Emission Tomography and Positron Emission Tomography was included in the code, developed in Phyton using the interactive Jupiter Notebook environment. The tool was implemented to calculate the shields of a Nuclear Medicine department of the International Center for Neurological Restoration (CIREN). This free and open source tool facilitates shielding calculations by increasing speed, which contributes to the optimization of radiation protection, but can also be used as a pedagogical tool(AU)