Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.
Title: Accelerating Magnetic Resonance Imaging using Convolutional Recurrent Neural Networks
Abstract: Fast and accurate MRI image reconstruction from undersampled data is critically important in clinical practice. Compressed sensing based methods are widely used in image reconstruction but the speed is slow due to the iterative algorithms. Deep learning based methods have shown promising advances in recent years. However, recovering the fine details from highly undersampled data is still challenging. Moreover, Current protocol of Amide Proton Transfer-weighted (APTw) imaging commonly starts with the acquisition of high-resolution T2-weighted (T2w) images followed by APTw imaging at particular geometry and locations (i.e. slice) determined by the acquired T2w images. Although many advanced MRI reconstruction methods have been proposed to accelerate MRI, existing methods for APTw MRI lack the capability of taking advantage of structural information in the acquired T2w images for reconstruction. In this work, we introduce a novel deep learning-based method with Convolutional Recurrent Neural Networks (CRNN) to reconstruct the image from multiple scales. Finally, we explore the use of the proposed Recurrent Feature Sharing (RFS) reconstruction module to utilize intermediate features extracted from the matched T2w image by CRNN so that the missing structural information can be incorporated into the undersampled APT raw image thus effectively improving the image quality of the reconstructed APTw image.
Committee Members
Vishal M. Patel, Department of Electrical and Computer Engineering
Rama Chellappa, Department of Electrical and Computer Engineering
Shanshan Jiang, Department of Radiology and Radiological Science