Measurement optimization for Near-Infrared optical tomography

The image resolution and contrast in Near-Infrared (NIR) tomographic image reconstruction is in part affected by the number of available boundary measurements. In the presented study, singular-value decomposition (SVD) of the Jacobian has been used to find the benefit of the total number of measurements that can be obtained in a two-dimensional (2D) and three-dimensional (3D) problem. Reconstructed images show an increase in improvement in the reconstructed images when the number of measurements are increased, with a central anomaly showing more improvement as compared to a more superficial one. It is also shown that given a 2D model of the domain, the increase in amount of useful data drops exponentially with an increase in total number of measurements. For 3D NIR tomography use of three fundamentally different data collection strategies are discussed and compared. It is shown that given a 3D NIR problem, using three planes of data gives more independent information compared to the single plane of data. Given a three planes of data collection fibers, it is shown that although more data can be collected in the out-of-plane data collection strategy as compared to the only in-plane case, the addition of new data does not increase image accuracy dramatically where as it increases the data collection and computation time.