Structural-functional lung imaging using a combined CT-EIT and a Discrete Cosine Transformation reconstruction method

Abstract

Lung EIT is a functional imaging method that utilizes electrical currents to reconstruct images of conductivity changes inside the thorax. This technique is radiation free and applicable at the bedside, but lacks of spatial resolution compared to morphological imaging methods such as X-ray computed tomography (CT). In this article we describe an approach for EIT image reconstruction using morphologic information obtained from other structural imaging modalities. This leads to recon- structed images of lung ventilation that can easily be superimposed with structural CT or MRI images, which facilitates image interpretation. The approach is based on a Discrete Cosine Transformation (DCT) of an image of the considered transversal thorax slice. The use of DCT enables reduction of the dimensionality of the reconstruction and ensures that only conductivity changes of the lungs are reconstructed and displayed. The DCT based approach is well suited to fuse morphological image information with functional lung imaging at low computational costs. Results on simulated data indicate that this approach preserves the morphological structures of the lungs and avoids blurring of the solution. Images from patient measurements reveal the capabilities of the method and demonstrate benefits in possible applications.

A Fidelity-Embedded Regularization Method for Robust Electrical Impedance Tomography

“…This is the reason why the formula (25) directly computes the local ensemble average of conductivity changes at each point. The algebraic formula (25) can be seen as a regularized least-squares datafitting method (19) when the regularization operator is R FE . Then, the formula (25) can be expressed using R FE in (21) aṡ…”

“…When the regularization parameter λ is small (λ ≈ 0), the FER method is equivalent to the regularized least-squares data-fitting method (19). When λ is large (λ ≈ ∞), it converges to the algebraic formula (25) and directly recovers the weighted average conductivityσ FE . The regularization operation R FE fully exploits the somewhat orthogonal structure of the sensitivity matrix, thereby embedding data fidelity in the regularization process.…”

“…The direct algebraic formula (25) or (20) for λ = ∞ can be expressed as a transpose of a scaled sensitivity matrix. This type of direct approach was suggested in the late 1980s by Kotre [36], but was soon abandoned owing to poor performance and lack of theoretical grounding [37].…”

Lobe based image reconstruction in Electrical Impedance Tomography

“…This reduces the dimension of the Jacobian to J lobe 2 R 208Â5 , which means that the reconstruction problem is now overdetermined and no regularization scheme is needed. The estimated conductivity change in the lobesx lobe can be calculated as shown in (8).…”

Comparison of Image Reconstruction Algorithms in EIT Imaging

“…Recently, we have developed an approach for image reconstruction including patient specific structural information (obtained e.g. from CT or MRI data) into the reconstruction process [3]. This approach facilitates the superposition of reconstructed images of conductivity change and structural images and thus provides a broader insight into the pathophysiology of the lungs.…”