Weighting function effects in a direct regularization method for image-guided near-infrared spectral tomography of breast cancer

Abstract

Structural image-guided near-infrared spectral tomography (NIRST) has been developed as a way to use diffuse NIR spectroscopy within the context of image-guided quantification of tissue spectral features. A direct regularization imaging (DRI) method for NIRST has the value of not requiring any image segmentation. Here, we present a comprehensive investigational study to analyze the impact of the weighting function implied when weighting the recovery of optical coefficients in DRI based NIRST. This was done using simulations, phantom and clinical patient exam data. Simulations where the true object is known indicate that changes to this weighting function can vary the contrast by 10%, the contrast to noise ratio by 20% and the full width half maximum (FWHM) by 30%. The results from phantoms and human images show that a linear inverse distance weighting function appears optimal, and that incorporation of this function can generally improve the recovered total hemoglobin contrast of the tumor to the normal surrounding tissue by more than 15% in human cases.

Back-propagation neural network-based reconstruction algorithm for diffuse optical tomography

“…The PSNR (unit: dB) is used to compare the restoration of the images, without depending strongly on the image intensity scaling. 15 SSIM is used to measure the similarity between the true and the reconstructed images, and an SSIM value of 1.0 refers to identical images. We expect lower ABE and MSE, while higher PSNR and SSIM, which show better performance.…”

“…Although both linear and nonlinear reconstruction algorithms for DOT are available, 14 considerable efforts have been made to develop various reconstruction algorithms to improve quantitative accuracy and image quality. [14][15][16][17][18][19][20][21][22] To date, the illposedness of the inverse problem in DOT can be alleviated by employing a regularization technique, which utilizes a data fitting term together with a regularizer (L 2 or L 1 norm, etc.) to suppress the effect of measurement noise and modeling errors.…”