A Review of Electrical Impedance Tomography in Lung Applications: Theory and Algorithms for Absolute Images.

Electrical Impedance Tomography (EIT) is under fast development, the present paper is a review of some procedures that are contributing to improve spatial resolution and material properties accuracy, admitivitty or impeditivity accuracy. A review of EIT medical applications is presented and they were classified into three broad categories: ARDS patients, obstructive lung diseases and perioperative patients. The use of absolute EIT image may enable the assessment of absolute lung volume, which may significantly improve the clinical acceptance of EIT. The Control Theory, the State Observers more specifically, have a developed theory that can be used for the design and operation of EIT devices. Electrode placement, current injection strategy and electrode electric potential measurements strategy should maximize the number of observable and controllable directions of the state vector space. A non-linear stochastic state observer, the Unscented Kalman Filter, is used directly for the reconstruction of absolute EIT images. Historically, difference images were explored first since they are more stable in the presence of modelling errors. Absolute images require more detailed models of contact impedance, stray capacitance and properly refined finite element mesh where the electric potential gradient is high. Parallelization of the forward program computation is necessary since the solution of the inverse problem often requires frequent solutions of the forward problem. Several reconstruction algorithms benefit by the Bayesian inverse problem approach and the concept of prior information. Anatomic and physiologic information are used to form the prior information. An already tested methodology is presented to build the prior probability density function using an ensemble of CT scans and in vivo impedance measurements. Eight absolute EIT image algorithms are presented.

Electrical impedance tomography reconstruction through Simulated Annealing with incomplete evaluation of the objective function.

The EIT reconstruction problem is approached as an optimization problem where the difference between a simulated impedance domain and the observed one is minimized. This optimization problem is often solved by Simulated Annealing (SA), but at a large computational cost due to the expensive evaluation process of the objective function. We propose here, a variation of SA applied to EIT where the objective function is evaluated only partially, while ensuring upper boundaries on the deviation on the behavior of the modified SA. The reconstruction method is evaluated with simulated and experimental data.

Temporal segmentation of lung region from MRI sequences using multiple active contours.

Segmentation of the lung is particularly difficult because of the large variation in image quality. A modified Hough transform in combination with a mask creation algorithm can robustly determine synchronous respiratory patterns. The synchronicity restriction is relaxed by applying a greedy active contour algorithm. The respiratory patterns define a point cloud near the lung region boundary representing a subjective contour. The gravitation vector field (GVF) active contour algorithm is used to create an initial segmentation exclusively based on the point cloud. A final active contours algorithm is executed to adjust the boundary to the images. The algorithm was tested with healthy subjects and COPD patients, and the result was checked through temporal registration of coronal and sagittal images.