Volume Reconstruction for MRI

Abstract

One of the challenges in medical imaging is to increase the resolution of 3D MRI (Magnetic Resonance Imaging) signals. This is the problem of 3D signal reconstruction under the condition of very low sampling rate. Based on compressive sensing theory, the Direct Volume Reconstruction (DVR) method is proposed to reconstruct the 3D signal volume-by-volume based on a learned dictionary. DVR is a general method and applicable to any 3D signal as long as it can be sparsely represented. To exploit the nature of the 3D MRI system, the Progressive Volume Reconstruction (PVR) method is further proposed to improve the DVR reconstruction. In PVR, local reconstruction is used to reconstruct in-plane slices, and the output is then forwarded to global reconstruction, in which both the initially sampled and locally reconstructed signals are used together to reconstruct the whole 3D signal. Two separate dictionaries, rather than one, are trained in PVR. In this way, more prior knowledge from the training data is exploited. Experiments on a head MRI dataset demonstrate that DVR achieves much better performance than conventional tricubic interpolation and that PVR considerably improves DVR performance with regard to both PSNR and visibility quality.