Monte Carlo modeling of angiographic optical coherence tomography.

Optical coherence tomography (OCT) provides both structural and angiographic imaging modes. Because of its unique capabilities, OCT-based angiography has been increasingly adopted into small animal and human subject imaging. To support the development of the signal and image processing algorithms on which OCT-based angiography depends, we describe here a Monte Carlo-based model of the imaging approach. The model supports arbitrary three-dimensional vascular network geometries and incorporates methods to simulate OCT signal temporal decorrelation. With this model, it will be easier to compare the performance of existing and new angiographic signal processing algorithms, and to quantify the accuracy of vascular segmentation algorithms. The quantitative analysis of key algorithms within OCT-based angiography may, in turn, simplify the selection of algorithms in instrument design and accelerate the pace of new algorithm development.

A resistive mesh phantom for assessing the performance of EIT systems.

Assessing the performance of electrical impedance tomography (EIT) systems usually requires a phantom for validation, calibration, or comparison purposes. This paper describes a resistive mesh phantom to assess the performance of EIT systems while taking into account cabling stray effects similar to in vivo conditions. This phantom is built with 340 precision resistors on a printed circuit board representing a 2-D circular homogeneous medium. It also integrates equivalent electrical models of the Ag/AgCl electrode impedances. The parameters of the electrode models were fitted from impedance curves measured with an impedance analyzer. The technique used to build the phantom is general and applicable to phantoms of arbitrary shape and conductivity distribution. We describe three performance indicators that can be measured with our phantom for every measurement of an EIT data frame: SNR, accuracy, and modeling accuracy. These performance indicators were evaluated on our EIT system under different frame rates and applied current intensities. The performance indicators are dependent on frame rate, operating frequency, applied current intensity, measurement strategy, and intermodulation distortion when performing simultaneous measurements at several frequencies. These parameter values should, therefore, always be specified when reporting performance indicators to better appreciate their significance.

A 3-D hybrid finite element model to characterize the electrical behavior of cutaneous tissues.

Finite element modeling of the skin is useful to study the electrical properties of cutaneous tissues and gain a better understanding of the current distribution within the skin. Such an epithelial finite element model comprises extremely thin structures like cellular membranes, nuclear membranes, and the extracellular fluid. Meshing such narrow spaces considerably increases the number of elements leading to longer computing time. This also greatly reduces the number of epithelial cells that can be assembled before reaching computing limitations. To avoid the problem of meshing extremely narrow spaces while unnecessarily increasing the number of elements, we present a new hybrid modeling approach to develop a 3-D finite element model of the skin. This skin model comprises all skin layers, different lesion types, and a complete electrode model. It is used to analyze the complex electrical behavior of normal and malignant skin tissues. The current distribution within this model is also simulated to assess the depth of field achievable by an electrical impedance tomography system at different operating frequencies.

A multi-frequency EIT system design based on telecommunication signal processors.

A multi-frequency electrical impedance tomography system for cardiopulmonary monitoring has been designed with specialized digital signal processors developed primarily for the telecommunications sector. The system consists of two modules: a scan-head and a base-station. The scan-head, located close to the patient's torso, contains front-end circuits for measuring transfer impedance with a 16-electrode array. The base-station, placed at the bedside, comprises 16 direct digital synthesizers, 32 digital down-converters, digital circuits to control the data acquisition sequence and a USB-2.0 microcontroller. At every step of the scan sequence, the system simultaneously measures four complex variables at eight frequencies. These variables are the potential difference between the selected pair of sense electrodes, the currents applied by the source and sink electrodes, and the current flowing through the ground electrode. Frequencies are programmable from 10 kHz to 2 MHz with a resolution of 2 mHz. Characterization tests were performed with a precision mesh phantom connected to the scan-head. For a 5 Hz frame rate, the mean signal-to-noise ratio and accuracy are, respectively, 43 dB and 95.4% for eight frequencies logarithmically spaced from 70 to 950 kHz. In vitro and in vivo time-difference images have been reconstructed.

Real-time management of faulty electrodes in electrical impedance tomography.

Completely or partially disconnected electrodes are a fairly common occurrence in many electrical impedance tomography (EIT) clinical applications. Several factors can contribute to electrode disconnection: patient movement, perspiration, manipulations by clinical staff, and defective electrode leads or electronics. By corrupting several measurements, faulty electrodes introduce significant image artifacts. In order to properly manage faulty electrodes, it is necessary to: 1) account for invalid data in image reconstruction algorithms and 2) automatically detect faulty electrodes. This paper presents a two-part approach for real-time management of faulty electrodes based on the principle of voltage-current reciprocity. The first part allows accounting for faulty electrodes in EIT image reconstruction without a priori knowledge of which electrodes are at fault. The method properly weights each measurement according to its compliance with the principle of voltage-current reciprocity. Results show that the algorithm is able to automatically determine the valid portion of the data and use it to calculate high-quality images. The second part of the approach allows automatic real-time detection of at least one faulty electrode with 100% sensitivity and two faulty electrodes with 80% sensitivity enabling the clinical staff to fix the problem as soon as possible to minimize data loss.

Accounting for hardware imperfections in EIT image reconstruction algorithms.

Electrical impedance tomography (EIT) is a non-invasive technique for imaging the conductivity distribution of a body section. Different types of EIT images can be reconstructed: absolute, time difference and frequency difference. Reconstruction algorithms are sensitive to many errors which translate into image artefacts. These errors generally result from incorrect modelling or inaccurate measurements. Every reconstruction algorithm incorporates a model of the physical set-up which must be as accurate as possible since any discrepancy with the actual set-up will cause image artefacts. Several methods have been proposed in the literature to improve the model realism, such as creating anatomical-shaped meshes, adding a complete electrode model and tracking changes in electrode contact impedances and positions. Absolute and frequency difference reconstruction algorithms are particularly sensitive to measurement errors and generally assume that measurements are made with an ideal EIT system. Real EIT systems have hardware imperfections that cause measurement errors. These errors translate into image artefacts since the reconstruction algorithm cannot properly discriminate genuine measurement variations produced by the medium under study from those caused by hardware imperfections. We therefore propose a method for eliminating these artefacts by integrating a model of the system hardware imperfections into the reconstruction algorithms. The effectiveness of the method has been evaluated by reconstructing absolute, time difference and frequency difference images with and without the hardware model from data acquired on a resistor mesh phantom. Results have shown that artefacts are smaller for images reconstructed with the model, especially for frequency difference imaging.