Hierarchical Clustering Method to Improve Transrectal Ultrasound-guided Diffuse Optical Tomography for Prostate Cancer Imaging

The inclusion of anatomical prior information in reconstruction algorithms can improve the quality of reconstructed images in near-infrared diffuse optical tomography (DOT). Prior literature on possible locations of human prostate cancer from transrectal ultrasound (TRUS), however, is limited and has led to biased reconstructed DOT images. In this work, we propose a hierarchical clustering method (HCM) to improve the accuracy of image reconstruction with limited prior information. HCM reconstructs DOT images in three steps: 1) to reconstruct the human prostate, 2) to divide the prostate region into geometric clusters to search for anomalies in finer clusters, 3) to continue the geometric clustering within anomalies for improved reconstruction. We demonstrated this hierarchical clustering method using computer simulations and laboratory phantom experiments. Computer simulations were performed using combined TRUS/DOT probe geometry with a multilayered model; experimental demonstration was performed with a single-layer tissue simulating phantom. In computer simulations, two hidden absorbers without prior location information were reconstructed with a recovery rate of 100% in their locations and 95% in their optical properties. In experiments, a hidden absorber without prior location information was reconstructed with a recovery rate of 100% in its location and 83% in its optical property.