Artificial intelligence using neural network architecture for radiology (AINNAR): classification of MR imaging sequences.
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 appa...
| Publicado en: | Japanese Journal of Radiology Vol. 36; no. 12; pp. 691 - 698 |
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| Autores principales: | , , , , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=133160232&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133160232 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 18671071 AUCM jtl: Japanese Journal of Radiology issn: 18671071 maglogo: N pubinfo: dt: Dec2018 vid: 36 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 133160232 133160232 NLM30232585 133160232 10.1007/s11604-018-0779-3 NLM30232585 133160232 ppf: 691 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Artificial intelligence using neural network architecture for radiology (AINNAR): classification of MR imaging sequences. aug: au: Noguchi, Tomoyuki Higa, Daichi Asada, Takashi Kawata, Yusuke Machitori, Akihiro Shida, Yoshitaka Okafuji, Takashi Yokoyama, Kota Uchiyama, Fumiya Tajima, Tsuyoshi affil: Department of Radiology, National Center for Global Health and Medicine, 1-21-1 Toyama, Shinjuku-ku, 162-8655, Tokyo, Japan sug: subj: Neural Networks (Computer) Magnetic Resonance Imaging Methods Brain Brain Physiopathology Artificial Intelligence Middle Age Aged Aged, 80 and Over Reproducibility of Results Magnetic Resonance Angiography Magnetic Resonance Imaging Female Male Retrospective Design Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Female Male ab: 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. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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