Modified Compressive Sensing For Real-time Dynamic Mr Imaging

In this work, we propose algorithms to recursively and causally reconstruct a sequence of natural images from a reduced number of linear projection measurements taken in a domain that is "incoherent" with respect to the image's sparsity basis (typically wavelet) and demonstrate their application in real-time MR image reconstruction. For a static version of the above problem, Compressed Sensing (CS) provides a provably exact and computationally efficient solution. But most existing solutions for the actual problem are either offline and non-causal or cannot compute an exact reconstruction (for truly sparse signal sequences), except using as many measurements as those needed for CS. The key idea of our proposed solution (modified-CS) is to design a modification of CS when a part of the support set is known (available from reconstructing the previous image). We demonstrate the exact reconstruction property of modified-CS on full-size image sequences using much fewer measur...

• Type: Conference
• Year: 2009
• Where: ICIP
• Keywords: Exact Reconstruction Property, ICIP 2009, Image Processing, Linear Projection Measurements, MR Image Reconstruction