IEEE Transactions on Medical Imaging

A framework to analyze the propagation of measurement noise through backprojection reconstruction algorithms in electrical impedance tomography (EIT) is presented. Two measurement noise sources were considered: noise in the current drivers and in the voltage detectors. The influence of the acquisition system architecture (serial/semi-parallel) is also discussed. Three variants of backprojection reconstruction are studied: basic (unweighted), weighted and exponential backprojection. The results of error propagation theory have been compared with those obtained from simulated and experimental data. This comparison shows that the approach provides a good estimate of the reconstruction error variance. It is argued that the reconstruction error in EIT images obtained via backprojection can be approximately modeled as a spatially nonstationary Gaussian distribution. This methodology allows us to develop a spatial characterization of the reconstruction error in EIT images.

For pt. I see ibid., vol. 21, no. 7, p. 755-63 (2002). Image error analysis of a diffuse near-infrared tomography (NIR) system has been carried out on simulated data using a statistical approach described in pt. I of this paper (Pogue et al., 2002). The methodology is used here with experimental data acquired on phantoms with a prototype imaging system intended for characterizing breast tissue. Results show that imaging performance is not limited by random measurement error, but rather by calibration issues. The image error over the entire field of view is generally not minimized when an accurate homogeneous estimate of the phantom properties is available; however, local image error over a target region of interest (ROI) is reduced. The image reconstruction process which includes a Levenberg-Marquardt style regularization provides good minimization of the objective function, yet its reduction is not always correlated with an overall image error decrease. Minimization of the bias in an ROI which contains localized changes in the optical properties can be achieved through five to nine iterations of the algorithm. Precalibration of the algorithm through statistical evaluation of phantom studies may provide a better measure of the image accuracy than that implied by minimization of the standard objective function.

The author reviews the history of medical image computing at his institute, summarizes the achievements, sketches some of the difficulties encountered, and draws conclusions that might be of interest especially to people new to the field. The origin and history section provides a chronology of this work, emphasizing the milestones reached during the past three decades. In accordance with the author's group's focus on imaging, the paper is accompanied by many pictures, some of which, he thinks, are of historical value.

Electrical impedance (EI) measurements conducted on the thorax contain useful information about the changes in blood volume that occur in the thorax during the heart cycle. The aim of this paper is to present a new (tomographic-like) method to obtain this relevant information with electrical impedance measurements, using a linear electrode array. This method is tested on three subjects and the results are compared with results, obtained from magnetic resonance cine-images showing the cross-sectional surface area changes of the aorta, the vena cava, the carotid arteries, and the heart. This paper shows that the different sources of the thoracic EI waveform may be separated in time and location on the thoracic surface and that aortic volume changes may be estimated accurately.

For pt. I see ibid., vol. 21, no. 7, p. 823-8 (2002). Microwave-induced thermoacoustic tomography (TAT) in a cylindrical configuration is developed to image biological tissue. Thermoacoustic signals are acquired by scanning a flat ultrasonic transducer. Using a new expansion of a spherical wave in cylindrical coordinates, we apply the Fourier and Hankel transforms to TAT and obtain an exact frequency-domain reconstruction method. The effect of discrete spatial sampling on image quality is analyzed. An aliasing-proof reconstruction method is proposed. Numerical and experimental results are included.