Abstract
Purpose
The confusion of MRI sequence names could be solved if MR images were automatically identified after image data acquisition. We revealed the ability of deep learning to classify head MRI sequences.
Materials and methods
Seventy-eight patients with mild cognitive impairment (MCI) having apparently normal head MR images and 78 intracranial hemorrhage (ICH) patients with morphologically deformed head MR images were enrolled. Six imaging protocols were selected to be performed: T2-weighted imaging, fluid attenuated inversion recovery imaging, T2-star-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient mapping, and source images of time-of-flight magnetic resonance angiography. The proximal first image slices and middle image slices having ambiguous and distinctive contrast patterns, respectively, were classified by two deep learning imaging classifiers, AlexNet and GoogLeNet.
Results
AlexNet had accuracies of 73.3%, 73.6%, 73.1%, and 60.7% in the middle slices of MCI group, middle slices of ICH group, first slices of MCI group, and first slices of ICH group, while GoogLeNet had accuracies of 100%, 98.1%, 93.1%, and 94.8%, respectively. AlexNet significantly had lower classification ability than GoogLeNet for all datasets.
Conclusions
GoogLeNet could judge the types of head MRI sequences with a small amount of training data, irrespective of morphological or contrast conditions.
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Acknowledgements
This work was supported in part by Grants-in-Aid for Scientific Research from Japan society for the promotion of science (16K10333).
Funding
This work was supported in part by Grants-in-Aid for Scientific Research from Japan society for the promotion of science (16K10333).
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We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.
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This study was approved by our hospital's institutional review board, which waived the need for written informed consent from the patients in light of the retrospective study design.
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Noguchi, T., Higa, D., Asada, T. et al. Artificial intelligence using neural network architecture for radiology (AINNAR): classification of MR imaging sequences. Jpn J Radiol 36, 691–697 (2018). https://doi.org/10.1007/s11604-018-0779-3
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DOI: https://doi.org/10.1007/s11604-018-0779-3