Authors: Ashkan Javaherian Manuchehr Soleimani Knut Moeller
Publish Date: 2016/01/06
Volume: 54, Issue: 8, Pages: 1243-1255
Abstract
A class of sparse optimization techniques that require solely matrix–vector products rather than an explicit access to the forward matrix and its transpose has been paid much attention in the recent decade for dealing with largescale inverse problems This study tailors application of the socalled Gradient Projection for Sparse Reconstruction GPSR to largescale timedifference threedimensional electrical impedance tomography 3D EIT 3D EIT typically suffers from the need for a large number of voxels to cover the whole domain so its application to realtime imaging for example monitoring of lung function remains scarce since the large number of degrees of freedom of the problem extremely increases storage space and reconstruction time This study shows the great potential of the GPSR for largesize timedifference 3D EIT Further studies are needed to improve its accuracy for imaging smallsize anomaliesThe rationale behind employing the smoothed Sobolev gradient is that heuristically the direct application of nabla psi to this algorithm exhibits unappealing oscillations in the reconstruction which often gives rise to numerical instability This is another superiority of the GPSR over this algorithm as the GPSR suitably converges through the direct application of the gradient
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