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Detecting focal cortical dysplasia lesions from FLAIR-negative images based on cortical thickness
by
Tian, Maoyu
, Wen, Junhai
, Feng, Cuixia
, Zhao, Hulin
, Lu, Miaomiao
in
Abnormalities
/ Adult
/ Algorithms
/ Automation
/ Biomaterials
/ Biomedical Engineering and Bioengineering
/ Biomedical Engineering/Biotechnology
/ Biotechnology
/ Brain - diagnostic imaging
/ Brain - pathology
/ Brain diseases
/ Brain research
/ Care and treatment
/ Cerebral cortex
/ Cortical thickness
/ Diagnosis
/ Drug resistance
/ Dysplasia
/ Engineering
/ Epilepsy
/ FCD
/ Female
/ FLAIR-negative image
/ Humans
/ Identification methods
/ Image detection
/ Image Processing, Computer-Assisted
/ Inspection
/ Lesions
/ Magnetic Resonance Imaging
/ Male
/ Malformations of Cortical Development - diagnostic imaging
/ Malformations of Cortical Development - pathology
/ Medical imaging
/ Methods
/ Patients
/ Physicians
/ Physiological aspects
/ Prefrontal cortex
/ Thickness
2020
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Detecting focal cortical dysplasia lesions from FLAIR-negative images based on cortical thickness
by
Tian, Maoyu
, Wen, Junhai
, Feng, Cuixia
, Zhao, Hulin
, Lu, Miaomiao
in
Abnormalities
/ Adult
/ Algorithms
/ Automation
/ Biomaterials
/ Biomedical Engineering and Bioengineering
/ Biomedical Engineering/Biotechnology
/ Biotechnology
/ Brain - diagnostic imaging
/ Brain - pathology
/ Brain diseases
/ Brain research
/ Care and treatment
/ Cerebral cortex
/ Cortical thickness
/ Diagnosis
/ Drug resistance
/ Dysplasia
/ Engineering
/ Epilepsy
/ FCD
/ Female
/ FLAIR-negative image
/ Humans
/ Identification methods
/ Image detection
/ Image Processing, Computer-Assisted
/ Inspection
/ Lesions
/ Magnetic Resonance Imaging
/ Male
/ Malformations of Cortical Development - diagnostic imaging
/ Malformations of Cortical Development - pathology
/ Medical imaging
/ Methods
/ Patients
/ Physicians
/ Physiological aspects
/ Prefrontal cortex
/ Thickness
2020
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Detecting focal cortical dysplasia lesions from FLAIR-negative images based on cortical thickness
by
Tian, Maoyu
, Wen, Junhai
, Feng, Cuixia
, Zhao, Hulin
, Lu, Miaomiao
in
Abnormalities
/ Adult
/ Algorithms
/ Automation
/ Biomaterials
/ Biomedical Engineering and Bioengineering
/ Biomedical Engineering/Biotechnology
/ Biotechnology
/ Brain - diagnostic imaging
/ Brain - pathology
/ Brain diseases
/ Brain research
/ Care and treatment
/ Cerebral cortex
/ Cortical thickness
/ Diagnosis
/ Drug resistance
/ Dysplasia
/ Engineering
/ Epilepsy
/ FCD
/ Female
/ FLAIR-negative image
/ Humans
/ Identification methods
/ Image detection
/ Image Processing, Computer-Assisted
/ Inspection
/ Lesions
/ Magnetic Resonance Imaging
/ Male
/ Malformations of Cortical Development - diagnostic imaging
/ Malformations of Cortical Development - pathology
/ Medical imaging
/ Methods
/ Patients
/ Physicians
/ Physiological aspects
/ Prefrontal cortex
/ Thickness
2020
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Detecting focal cortical dysplasia lesions from FLAIR-negative images based on cortical thickness
Journal Article
Detecting focal cortical dysplasia lesions from FLAIR-negative images based on cortical thickness
2020
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Overview
Background
Focal cortical dysplasia (FCD) is a neuronal migration disorder and is a major cause of drug-resistant epilepsy. However, many focal abnormalities remain undetected during routine visual inspection, and many patients with histologically confirmed FCD have normal fluid-attenuated inversion recovery (FLAIR-negative) images. The aim of this study was to quantitatively evaluate the changes in cortical thickness with magnetic resonance (MR) imaging of patients to identify FCD lesions from FLAIR-negative images.
Methods
We first used the three-dimensional (3D) Laplace method to calculate the cortical thickness for individuals and obtained the cortical thickness mean image and cortical thickness standard deviation (SD) image based on all 32 healthy controls. Then, a cortical thickness extension map was computed by subtracting the cortical thickness mean image from the cortical thickness image of each patient and dividing the result by the cortical thickness SD image. Finally, clusters of voxels larger than three were defined as the FCD lesion area from the cortical thickness extension map.
Results
The results showed that three of the four lesions that occurred in non-temporal areas were detected in three patients, but the detection failed in three patients with lesions that occurred in the temporal area. The quantitative analysis of the detected lesions in voxel-wise on images revealed the following: specificity (99.78%), accuracy (99.76%), recall (67.45%), precision (20.42%), Dice coefficient (30.01%), Youden index (67.23%) and area under the curve (AUC) (83.62%).
Conclusion
Our studies demonstrate an effective method to localize lesions in non-temporal lobe regions. This novel method automatically detected FCD lesions using only FLAIR-negative images from patients and was based only on cortical thickness feature. The method is noninvasive and more effective than a visual analysis for helping doctors make a diagnosis.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
Subject
/ Adult
/ Biomedical Engineering and Bioengineering
/ Biomedical Engineering/Biotechnology
/ Epilepsy
/ FCD
/ Female
/ Humans
/ Image Processing, Computer-Assisted
/ Lesions
/ Male
/ Malformations of Cortical Development - diagnostic imaging
/ Malformations of Cortical Development - pathology
/ Methods
/ Patients
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