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.
Highlights
The upper row depicts the conductivity change used in the simulation of boundary voltages
The feasibility of reduced-order models using orthogonal decomposition of conductivity and potential distributions has been demonstrated in simulations and tank measurements[47], but no individual structural information is embedded in the reconstruction
Anomalies in ventilation distribution can be correlated with underlying morphological information, and vice versa the impact of pathological changes in lung tissue on ventilation distribution can be examined if the described approach is used for image reconstruction
Summary
The Discrete Cosine Transformation (DCT) is an established method in image processing. A popular application is the JPEG image compression, where images are converted to a frequency-domain representation. The idea of DCT is to represent a signal as a weighted sum of basis functions, whereas the basis functions are cosine functions with different frequencies p and q in x-direction and y-direction, respectively. For a two dimensional image A with M rows and N columns the DCT is defined as follows: M−1 N−1 ∑ ∑ Vp,q = α pαq Am,n ⋅. Cos (2n + 1)qπ 2N (6) where α p =. The resulting matrix V contains the DCT coefficients. A lossy compression of the image A can be reconstructed using only a subset of the DCT coefficients
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