Direct Sensitivity Based Data-Optimization Strategy for Image-Guided Diffuse Optical Tomography

Implementing image-guidance for diffuse optical tomographic imaging involves reducing the spatial optical parameter space within the discrete tissue types being estimated. This makes the inverse problem overdetermined which indicates that the required number of measurements could be less than all available measurements. In this work, we propose a data-optimization approach to curtail the algorithmic complexity and implicitly reconstruct optical absorption image based on direct sensitivity approach. The performance of the proposed method was validated using numerical and gelatin phantom data indicating that this perturbation-like approach can quantify embedded regions with good accuracy and is free of bias errors associated with regularization approaches. The testing of the algorithm on human data of fast pulsatile NIR imaging in breast tissue showed that fast updates are possible and the required number of measurements is equal to discrete tissue types. The proposed method offers high level of measurement optimization for the dynamic imaging, compared to traditional methods of full iterative regularized tomography or recently proposed data resolution-based methods.