The current approaches to imaging the tissue blood flow index (BFI) from diffuse correlation tomography (DCT) data are either an analytical solution or a finite element method, both of which are unable to simultaneously account for the tissue heterogeneity and fully utilize the DCT data. In this study, a new imaging concept for DCT, namely NL-DCT, was created by us in which the medical images are combined with light Monte Carlo simulation to provide geometrical and heterogeneous information in tissue. Moreover, the DCT data at multiple delay time are fully utilized via iterative linear regression. The unique merit of NL-DCT in utilizing the medical images as prior information, when combined with a split Bregman algorithm for total variation minimization (Bregman-TV), was validated on a realistic human head model. Computer simulation outcomes demonstrate the accuracy and robustness of NL-DCT in localizing and separating the flow anomalies as well as the capability to preserve edges of anomalies.

Near-infrared diffuse correlation imaging (DCT) is an important method of tissue blood flow imaging for the prognosis and diagnosis of various diseases. A new solution of DCT that is based on the Nth-order linear (NL) algorithm, termed as NL-DCT, was proposed in our previous study to overcome the limitation of tissue geometry and heterogeneity. The NL-DCT converts the image reconstruction into linear equations, and this solution is an ill-posed problem in mathematics. To improve the accuracy and robustness of the DCT image reconstruction, a combination of algebra reconstruction technique (ART) and total variation (TV), namely ART-TV, is proposed in this study. After each ART iteration, the TV model is used as an a priori constraint to reduce noise. The validations from computer simulation and phantom experiments with different anomalies demonstrate that the proposed ART-TV algorithm is efficient in DCT blood flow image reconstruction.

Diffuse correlation tomography (DCT) is an emerging tissue blood flow index (BFI) imaging technique that typically requires a large number of source‐detector pairs, resulting in high instrumentation costs. We developed a low‐cost paradigm for upgrading the DCT system with time‐sharing hardware sensors via optical switches, wherein the S‐D configuration was spatially optimized and combined with a novelty Nth‐order linear algorithm for image reconstruction. We verified this system through the phantom experiment and the lower limb skeletal muscle cuff occlusion test. The reconstructed BFI images exhibit the excellent performance of the upgraded DCT system in retrieval of the target location, outline, and anomaly contrast. For the deepest quasi‐solid anomaly, location accuracy was 90% with the αD_B‐contrast of 0.75. While, for the deepest liquid tube anomaly, a good linear relationship was achieved between contrast and pump speed, with the αD_B‐contrast of 3.29 at 600 mL/h. For human experiments, it was found that the BFI in the relaxed state was ten times higher than in the cuff occlusion state. This study demonstrates the feasibility of the proposed upgraded low‐cost DCT system for diagnosing various diseases associated with local perfusion abnormalities.