A fast time-difference inverse solver for 3D EIT with application to lung imaging

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 large-scale inverse problems. This study tailors application of the so-called Gradient Projection for Sparse Reconstruction (GPSR) to large-scale time-difference three-dimensional 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 real-time 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 large-size time-difference 3D EIT. Further studies are needed to improve its accuracy for imaging small-size anomalies.

An accelerated version of alternating direction method of multipliers for TV minimization in EIT

“…The smoothness penalties, which were employed since the inaugural studies in EIT, typically impose some unappealing blurriness on the reconstructed image [15]. Recently, the sparsity penalties have received much attention in EIT [18][19][20][21] thanks to their efficiency in recovering sparse conductivity fields, i.e., simple inclusions plus an unknown but uninteresting background [11]. Unfortunately, the sparse penalties cannot suitably deal with the sparsity of the gradient of the conductivity rather than the conductivity itself.…”

“…In many cases, a sequence of expansion coefficients of the signal over an orthonormal basis includes only a small number of nonzero entries, and is thus assumed sparse [11,17]. In many applications in EIT, the object under study involves an uninteresting background plus a number of interesting inclusions, which represents sparsity [11,[18][19][20][21].…”

“…In contrast with a great deal of sparsity regularization schemes that have been applied to EIT [16][17][18][19][20][21], the recent advances on TV minimization have not been paid due attention. As far as we know, TVAL3 is the most powerful TV algorithm in the context of image restoration [51][52].…”

Real-time monitoring radiofrequency ablation using tree-based ensemble learning models

“…Electrical impedance tomography (EIT), one of the methods that utilize the measured electrical impedance for monitoring RFA, uses electrodes surrounding the targeted tissue to measure impedance paths [16]. The data collected in EIT are then reconstructed into tissue electrical conductivity and temperature to provide lesion depth images [17][18][19]. The principle that allows for electrical impedance to be utilized is the temperature dependence of the electrical conductivity of biological tissue [20].…”

“…Thus, computing a single EIT-based lesion depth map requires time on the order of tens of seconds and minutes (100þ seconds at 90%þ accuracy on an Â86 processor-based workstation) [21]. Space-wise, computing an EIT lesion depth map can potentially occupy 1þ gigabyte of memory due to the reconstruction mesh size [12,18,19]. An optoacoustic imaging step requires 400þ seconds to reach 95% accuracy with a similar tomographic reconstruction algorithm [18].…”