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A tomographic time-domain reconstruction algorithm for solving the inverse electromagnetic problem is described. The application we have in mind is dielectric breast cancer detection but the results are of general interest to the field of microwave tomography. Reconstructions have been made from experimental and numerically simulated data for objects of different sizes in order to investigate the relation between the spectral content of the illuminating pulse and the quality of the reconstructed image. We have found that the spectral content is crucial for a successful reconstruction. The work has further shown that when imaging objects with different scale lengths it is an advantage to use a multiple step procedure. Low frequency content in the pulse is used to image the large structures and the reconstruction process then proceed by using higher frequency data to resolve small scale lengths. Good agreement between the results obtained from experimental data and simulated data has been achieved.

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Traumatic brain injury is the leading cause of death and severe disability for young people and a major public health problem for elderly. Many patients with intracranial bleeding are treated too late, because they initially show no symptoms of severe injury and are not transported to a trauma center. There is a need for a method to detect intracranial bleedings in the prehospital setting. In this study, we investigate whether broadband microwave technology (MWT) in conjunction with a diagnostic algorithm can detect subdural hematoma (SDH). A human cranium phantom and numerical simulations of SDH are used. Four phantoms with SDH 0, 40, 70 and 110 mL are measured with a MWT instrument. The simulated dataset consists of 1500 observations. Classification accuracy is assessed using fivefold cross-validation, and a validation dataset never used for training. The total accuracy is 100 and 82–96 % for phantom measurements and simulated data, respectively. Sensitivity and specificity for bleeding detection were 100 and 96 %, respectively, for the simulated data. SDH of different sizes is differentiated. The classifier requires training dataset size in order of 150 observations per class to achieve high accuracy. We conclude that the results indicate that MWT can detect and estimate the size of SDH. This is promising for developing MWT to be used for prehospital diagnosis of intracranial bleedings.

This paper presents a systematic approach to robust preconditioning for gradient-based nonlinear inverse scattering algorithms. In particular, one-and two-dimensional inverse problems are considered where the permittivity and conductivity profiles are unknown and the input data consist of the scattered field over a certain bandwidth. A time-domain least-squares formulation is employed and the inversion algorithm is based on a conjugate gradient or quasi-Newton algorithm together with an FDTD-electromagnetic solver. A Fisher information analysis is used to estimate the Hessian of the error functional. A robust preconditioner is then obtained by incorporating a parameter scaling such that the scaled Fisher information has a unit diagonal. By improving the conditioning of the Hessian, the convergence rate of the conjugate gradient or quasi-Newton methods are improved. The preconditioner is robust in the sense that the scaling, i.e. the diagonal Fisher information, is virtually invariant to the numerical resolution and the discretization model that is employed. Numerical examples of image reconstruction are included to illustrate the efficiency of the proposed technique.

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The measurement accuracy of an ultra-wideband time domain microwave tomography system is investigated. In order to make an assessment of the random variation of the measurements, the measurement repeatability of the system is evaluated by comparison with an ultra-wideband frequency domain system. A phantom is imaged with the time domain microwave tomography system and the reconstructed images are compared to those obtained by using the frequency domain system. The results suggest that with averaging tens of measurements, the time domain system can achieve the same level of measurement repeatability as that of the frequency domain system in the interesting frequency range of microwave tomography. The imaging results, however, indicate that the phantom reconstruction does not require such high measurement accuracy. The permittivity profile of the phantom reconstructed from the non-averaging time domain measurements is very similar to that obtained by means of the frequency domain system.

In this paper, a time-domain system dedicated to medical diagnostics has been designed, a prototype has been built and its performance has been evaluated. Measurements show that the system has a 3-dB bandwith of about 3.5 GHz and a signal to noise ratio over 40 dB in the frequency range about 800 MHz to 3.8 GHz. The system has been used to perform a microwave tomographic image reconstruction test. The same target was reconstructed based on data measured with a network analyzer.A comparison between the images shows very small differences, and proves the functionality of the time domain system. Index Terms-Microwave imaging, time domain measurements, ultrawideband (UWB).