Paper accepted at MICCAI 2016
Tran Minh Quan (5th year UNIST CSE PhD student) and Professor Won-Ki Jeong’s paper entitled with “Compressed Sensing Dynamic MRI Reconstruction using GPU-accelerated 3D Convolutional Sparse Coding” was accepted for publication at MICCAI 2016.
This paper introduced a fast alternating method for reconstructing highly undersampled dynamic MRI data using 3D convolutional sparse coding. The proposed solution leverages Fourier Convolution Theorem to accelerate the process of learning a set of 3D filters and iteratively refine the MRI reconstruction based on the sparse codes found subsequently. In contrast to conventional CS methods which exploit the sparsity by applying universal transforms such as wavelet and total variation, this approach extracts and adapts the temporal information directly from the MRI data using compact shift-invariant 3D filters. The reconstruction outperforms CPU implementation of the state-of-the-art dictionary learning-based approaches by up to two orders of magnitude.
MICCAI, stands for Medical Image Computing and Computer Assisted Intervention, is among the top premier international conferences for medical image analysis with an overall acceptance rate of 25%. This year, MICCAI 2016 will be held in Athens, Greece in October.