Magnetic Resonance (MR) imaging allows the acquisition of images with
different contrast properties depending on the acquisition protocol and the
magnetic properties of tissues. Many MR brain image processing techniques, such
as tissue segmentation, require multiple MR contrasts as inputs, and each
contrast is treated differently. Thus it is advantageous to automate the
identification of image contrasts for various purposes, such as facilitating
image processing pipelines, and managing and maintaining large databases via
content-based image retrieval (CBIR). Most automated CBIR techniques focus on a
two-step process: extracting features from data and classifying the image based
on these features. We present a novel 3D deep convolutional neural network
(CNN)-based method for MR image contrast classification. The proposed CNN
automatically identifies the MR contrast of an input brain image volume.
Specifically, we explored three classification problems: (1) identify
T1-weighted (T1-w), T2-weighted (T2-w), and fluid-attenuated inversion recovery
(FLAIR) contrasts, (2) identify pre vs post-contrast T1, (3) identify pre vs
post-contrast FLAIR. A total of 3418 image volumes acquired from multiple sites
and multiple scanners were used. To evaluate each task, the proposed model was
trained on 2137 images and tested on the remaining 1281 images. Results showed
that image volumes were correctly classified with 97.57% accuracy.

提出一种基于三维卷积神经网络的磁共振图像对比度分类方法，在多个任务中验证其准确性可达 97.57%。