ABSTRACT
Purpose@#To understand the effects of datasets with various parameters on pretrained network performance, the generalization capacity of the artificial neural network for myelin water imaging (ANN-MWI) is explored by testing datasets with various scan protocols (i.e., resolution and refocusing RF pulse shape) and types of disorders (i.e., neuromyelitis optica and edema). @*Materials and Methods@#ANN-MWI was trained to generate a T2 distribution, from which the myelin water fraction value was measured. The training and test datasets were acquired from healthy controls and multiple sclerosis patients using a multiecho gradient and spin-echo sequence with the same scan protocols. To test the generalization capacity of ANN-MWI, datasets with different settings were utilized.The datasets were acquired or generated with different resolutions, refocusing pulse shape, and types of disorders. For all datasets, the evaluation was performed in a white matter mask by calculating the normalized root-mean-squared error (NRMSE) between the results from the conventional method and ANN-MWI. Additionally, for the patient datasets, the NRMSE was calculated in each lesion mask. @*Results@#The results of ANN-MWI showed high reliability in generating myelin water fraction maps from the datasets with different resolutions. However, the increased errors were reported for the datasets with different refocusing pulse shapes and disorder types. Specifically, the region of lesions in edema patients reported high NRMSEs. These increased errors indicate the dependency of ANN-MWI on refocusing pulse flip angles and T 2 characteristics. @*Conclusion@#This study proposes information about the generalization accuracy of a trained network when applying deep learning to processing myelin water imaging.
ABSTRACT
Purpose@#To understand the effects of datasets with various parameters on pretrained network performance, the generalization capacity of the artificial neural network for myelin water imaging (ANN-MWI) is explored by testing datasets with various scan protocols (i.e., resolution and refocusing RF pulse shape) and types of disorders (i.e., neuromyelitis optica and edema). @*Materials and Methods@#ANN-MWI was trained to generate a T2 distribution, from which the myelin water fraction value was measured. The training and test datasets were acquired from healthy controls and multiple sclerosis patients using a multiecho gradient and spin-echo sequence with the same scan protocols. To test the generalization capacity of ANN-MWI, datasets with different settings were utilized.The datasets were acquired or generated with different resolutions, refocusing pulse shape, and types of disorders. For all datasets, the evaluation was performed in a white matter mask by calculating the normalized root-mean-squared error (NRMSE) between the results from the conventional method and ANN-MWI. Additionally, for the patient datasets, the NRMSE was calculated in each lesion mask. @*Results@#The results of ANN-MWI showed high reliability in generating myelin water fraction maps from the datasets with different resolutions. However, the increased errors were reported for the datasets with different refocusing pulse shapes and disorder types. Specifically, the region of lesions in edema patients reported high NRMSEs. These increased errors indicate the dependency of ANN-MWI on refocusing pulse flip angles and T 2 characteristics. @*Conclusion@#This study proposes information about the generalization accuracy of a trained network when applying deep learning to processing myelin water imaging.
ABSTRACT
OBJECTIVE: The aim of this study is to investigate abnormal findings of social brain network in Korean children with autism spectrum disorder (ASD) compared with typically developing children (TDC). METHODS: Functional magnetic resonance imaging (fMRI) was performed to examine brain activations during the processing of emotional faces (happy, fearful, and neutral) in 17 children with ASD, 24 TDC. RESULTS: When emotional face stimuli were given to children with ASD, various areas of the social brain relevant to social cognition showed reduced activation. Specifically, ASD children exhibited less activation in the right amygdala (AMY), right superior temporal sulcus (STS) and right inferior frontal gyrus (IFG) than TDC group when fearful faces were shown. Activation of left insular cortex and right IFG in response to happy faces was less in the ASD group. Similar findings were also found in left superior insular gyrus and right insula in case of neutral stimulation. CONCLUSION: These findings suggest that children with ASD have different processing of social and emotional experience at the neural level. In other words, the deficit of social cognition in ASD could be explained by the deterioration of the capacity for visual analysis of emotional faces, the subsequent inner imitation through mirror neuron system (MNS), and the ability to transmit it to the limbic system and to process the transmitted emotion.