Mutual information as a measure of image quality for 3D dynamic lung imaging with EIT

Abstract

We report on a pilot study of dynamic lung electrical impedance tomography (EIT) at the University of Manchester. Low-noise EIT data at 100 frames per second were obtained from healthy male subjects during controlled breathing, followed by magnetic resonance imaging (MRI) subsequently used for spatial validation of the EIT reconstruction. The torso surface in the MR image and electrode positions obtained using MRI fiducial markers informed the construction of a 3D finite element model extruded along the caudal-distal axis of the subject. Small changes in the boundary that occur during respiration were accounted for by incorporating the sensitivity with respect to boundary shape into a robust temporal difference reconstruction algorithm. EIT and MRI images were co-registered using the open source medical imaging software, 3D Slicer. A quantitative comparison of quality of different EIT reconstructions was achieved through calculation of the mutual information with a lung-segmented MR image. EIT reconstructions using a linear shape correction algorithm reduced boundary image artefacts, yielding better contrast of the lungs, and had 10% greater mutual information compared with a standard linear EIT reconstruction.

Figure 1

Electrode arrays; (a) electrodes attached during EIT data acquisition and (b) MRI fiducial markers to inform electrode positions.

Figure 2

FE model generation; (a) segmented MR slice with thorax and lung shapes highlighted and (b) extruded ‘2.5D’ FE model generated in EIDORS.

Figure 3

Voltage measurement sites for a seated subject; (a) two diametrically opposed sites with large breathing signal and (b) higher temporal resolution of large breathing signal.

Figure 4

Reconstructions in 3D rendered in MayaVi software (Ramachandran (2001)); (a) and (b) are conductivity changes at max inhalation for a standard and shape corrected reconstruction respectively. The front of the chest is in the background, and transverse and coronal scalar-cut planes are shown within the 3D volume.

Figure 5

Image reconstructions viewed down the caudal-distal axis. Left and right: Mid inhalation (t = 1.50s) and max inhalation (t = 3.00s). Top and bottom: Standard algorithm and shape correction algorithm.

Figure 6

MRI and EIT image co-registration. Left to right columns: Superior and inferior transverse planes. Top to bottom rows: MRI image, EIT image and co-registered EIT and MRI images at the transverse slice.

Figure 7

MRI and EIT image co-registration. Left to right columns: Sitting standard reconstruction, sitting shape correction reconstruction, supine standard reconstruction and supine shape correction reconstruction. Top to bottom rows: Transverse slices from superior to inferior.

Figure 8

Mutual information parameter study as a function of α; (a) and (b) are for the subject in the sitting and supine position respectively with a shape corrected (β = 4 × 10⁻²) and standard (β = 0) reconstruction.