Using a spatiotemporal neural network on dynamic gadolinium-enhanced MR images for diagnosing recurrent nasal papilloma

Abstract

Gadolinium (Gd)-enhanced magnetic resonance imaging (MRI) is widely used in the detection of recurrent nasal tumors. We have developed a spatiotemporal neural network (STNN) for identifying the tumor and fibrosis in the nasal regions. A more accurate signal-time curve called relative intensity change (RIC) for dynamic MR images is proposed as representation of gadolinium-enhanced MRI temporal information. The RIC curves of different diseases are embedded into the STNN and stored in the synaptic weights of the input layer through learning. In addition, to enhance the capability of the STNN in discriminating temporal information between tumors and fibrosis, the synaptic weights of its tap delays were obtained through a creative learning scheme, which reinforces the most distinguishable features, between tumor and fibrosis while inhibiting the indistinguishable features. The outputs of proposed STNN were indexed on a colormap in which red represents tumor and green represents fibrosis. The color-coded tumor/fibrosis areas are fused to the original MR image to facilitate visual interpretation. The experimental results show that the proposed method is able to detect abnormal tissues precisely.