Rapid convergence to the inverse solution regularized with Lorentzian distributed function for near-infrared continuous wave diffuse optical tomography.
A promising method to achieve rapid convergence for image reconstruction is introduced for the continuous-wave near-infrared (NIR) diffuse optical tomography (DOT). Tomographic techniques are usually implemented off line and are time consuming to realize image reconstruction, especially for NIR DOT. Therefore, it is essential to both speed up reconstruction and achieve stable and convergent solutions. We propose an approach using a constraint based on a Lorentzian distributed function incorporated into Tikhonov regularization, thereby rapidly converging a stable solution. It is found in the study that using the proposed method with around five or six iterations leads to a stable solution. The result is compared to the primary method usually converging in approximately 25 iterations. Our algorithm rapidly converges to stable solution in the case of noisy (>20 dB) detected intensities.
Pan
Min-Cheng
MC
Tungnan University, Department of Electronic Engineering, Shenkeng Taipei, Taiwan.
Pan
Min-Chun
MC
D000465:Algorithms; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007259:Infrared Rays; D008962:Models, Theoretical; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D016008:Statistical Distributions; D041622:Tomography, Optical / Q000379:methods*