Method and system for identifying epileptic focus of temporal lobe epilepsy caused by hippocampal sclerosis and/or predicting pathological typing of epilepsy

Abstract

The invention provides a method and system for identifying epilepsy focus of temporal lobe epilepsy caused by hippocampus sclerosis and / or predicting pathological typing of the epilepsy focus of temporal lobe epilepsy caused by hippocampus sclerosis, and the method for locating the epilepsy focus of temporal lobe epilepsy caused by hippocampus sclerosis comprises the following steps: acquiring analysis data, including acquiring PET / MR dynamic continuous brain imaging of < 11 > C-choline, < 18 > F-FDG and < 11 > C-FMZ in the attack interval of a patient to be identified; carrying out reconstruction of analysis data, including performing data reconstruction on PET / MR dynamic continuous brain imaging of 11C-choline, 18F-FDG and 11C-FMZ in the attack interval of the to-be-identified patient, and obtaining reconstruction data synchronized with the analysis data; inputting the analysis data and / or the reconstruction data to an epilepsy focus positioning model, and processing and analyzing the analysis data and / or the reconstruction data by the epilepsy focus positioning model to obtain an output image for indicating an epilepsy focus area; and outputting an output image for indicating the epilepsy focus area.

Description

technical field [0001] The invention relates to the technical field of epileptic focus identification, in particular to a method and system for identifying the epileptogenic focus of temporal lobe epilepsy caused by hippocampal sclerosis and / or predicting its pathological type. Background technique [0002] The information disclosed in this background section is only intended to increase the understanding of the general background of the present invention, and is not necessarily taken as an acknowledgment or any form of suggestion that the information constitutes the prior art already known to those skilled in the art. [0003] More than 70 million people worldwide suffer from epilepsy. The World Health Organization proposed in 2015 that epilepsy is the most common severe neurological disorder syndrome. According to the data obtained in clinical practice by the German Neuropathology Reference Center for Epilepsy Surgery, the histopathological types of refractory focal epile...

AI Technical Summary

Problems solved by technology

[0009] Aiming at the current problems of difficult localization of refractory temporal lobe epilepsy, non-invasive localization and the inability to predict the pathological type of hippocampal sclerosis, the present invention is based on the slow proliferation of glial cells, abnormal energy metabolism, and loss of inhibitory neurons in the epileptogenic focus. Theory of pathological changes, using multinuclides ( 11 C-choline, 18 F-FDG, 11 C-FMZ) The multimodal radiomics method of PET dynamic brain imaging forms the time-radiation curves of different radiopharmaceuticals, and compares and analyzes the distribution differences between the curves, based on the difference between the epileptogenic focus and normal brain tissue time-radiation curves To form a "metabolic characteristic fitting curve" model of the corresponding pixel pairs in the brain tissue to improve the accuracy of the diagnosis of epileptogenic focus; at the same time, further use the amount of signal change as a feature, and the results of pathological classification of epileptogenic focus as a label, Using machine learning algorithms, a classifier model that can distinguish the pathological subtypes of epileptogenic foci is trained, revealing the potential regularity of such pathological changes, and improving the accuracy of predicting pathological subtypes; Provide a theoretical basis for the precise positioning of the lesion before surgery, and provide a new idea for the use of radiomics data for neuropathological research

Images