Tomoyuki Noguchi1, Daichi Higa2, Takashi Asada3, Yusuke Kawata2, Akihiro Machitori2, Yoshitaka Shida2, Takashi Okafuji2, Kota Yokoyama2, Fumiya Uchiyama2, Tsuyoshi Tajima2. 1. Department of Radiology, National Center for Global Health and Medicine, 1-21-1 Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan. tnogucci@radiol.med.kyushu-u.ac.jp. 2. Department of Radiology, National Center for Global Health and Medicine, 1-21-1 Toyama, Shinjuku-ku, Tokyo, 162-8655, Japan. 3. Memory Clinics Ochanomizu, 4th floor, Ochanomizu Igaku Kaikan, 1-5-34, Yushima, Bunkyo-ku, Tokyo, 113-0034, Japan.
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.
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.
Authors: Nima Tajbakhsh; Jae Y Shin; Suryakanth R Gurudu; R Todd Hurst; Christopher B Kendall; Michael B Gotway Journal: IEEE Trans Med Imaging Date: 2016-03-07 Impact factor: 10.048
Authors: Bibo Shi; Lars J Grimm; Maciej A Mazurowski; Jay A Baker; Jeffrey R Marks; Lorraine M King; Carlo C Maley; E Shelley Hwang; Joseph Y Lo Journal: J Am Coll Radiol Date: 2018-02-02 Impact factor: 5.532
Authors: Hoo-Chang Shin; Holger R Roth; Mingchen Gao; Le Lu; Ziyue Xu; Isabella Nogues; Jianhua Yao; Daniel Mollura; Ronald M Summers Journal: IEEE Trans Med Imaging Date: 2016-02-11 Impact factor: 10.048
Authors: Norbert Braeker; Cornelia Schmitz; Natalie Wagner; Badrudin J Stanicki; Christina Schröder; Felix Ehret; Christoph Fürweger; Daniel R Zwahlen; Robert Förster; Alexander Muacevic; Paul Windisch Journal: Cureus Date: 2022-02-21