Comparing GA with MART to tomographic reconstruction of ultrasound images with and without noisy input data

Different approaches are in use to solve the problem of tomographic reconstruction, which is an inverse problem. Four different approaches; three variations of multiplicative algebraic reconstruction technique (MART) and a new approach based on genetic algorithms (GA), are evaluated and compared in the paper. The approaches are applied to the reconstruction of specimens from time-of-flight data collected by ultrasound transmission tomography. The time-of-flight data is simulated without taking into consideration the diffraction effects of ultrasound which is reasonably valid, only when the impedance mismatch in the specimen under consideration is small. Also it is assumed that the specimen under consideration consists of a maximum of three different materials with the goal being to identify the number, shape, and location of the inclusions in the specimen. The sensitivity of the various algorithms to the parameters involved, performance of various algorithms in terms of errors in reconstruction and time taken for the reconstruction are studied and presented here. Further the performance of the algorithms when the input data are contaminated with noise is presented. It is observed that although GA takes more time than MART, GA is reliable and accurate and scores much better than MART in dealing with problems where only limited data is available for the reconstruction.