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 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.

Usefulness of the apparent diffusion coefficient (ADC) for predicting the consistency of intracranial meningiomas.

Meningioma consistency is an important factor for surgical treatment. Tumor cellularity and fibrous tissue contribute to the consistency of tumors, and it is proposed that the minimum apparent diffusion coefficient (ADC) value is significantly correlated with meningioma consistency. Twenty-seven consecutive patients with 28 meningiomas were retrospectively enrolled. Minimum ADC values in meningiomas with a hard consistency were significantly lower than those with a soft consistency. The minimum ADC value might have clinical use as a predictor of meningioma consistency.