Because the inverse problem in diffuse optical tomography (DOT) is highly ill-posed in general, appropriate regularization based on prior knowledge of the target is necessary for the reconstruction of the image. The total variation L1 norm regularization method (TV-L1) that preserves the boundaries of a target is known to have excellent result in image reconstruction. However, large computational cost of the TV-L1 prevents its use in portable applications. In this study, we propose a dimension reduction method in DOT for fast and hardware-efficient image reconstruction. The proposed method is based on the fact that the optical flux from a light source in a highly scattering medium is localized spatially. As such, the dimension of a sensitivity matrix used in the forward model of the DOT can be reduced by eliminating uncorrelated subspaces. The simulation results indicate up to 96.1% reduction in dimensions and up to 79.3% reduction in runtime while suppressing the reconstruction error below 2.26%.
Lung cancer is the most common cause of cancer-related death. To diagnose lung cancers in early stages, numerous studies and approaches have been developed for cancer screening with computed tomography (CT) imaging. In recent years, convolutional neural networks (CNN) have become one of the most common and reliable techniques in computer aided detection (CADe) and diagnosis (CADx) by achieving state-of-the-art-level performances for various tasks. In this study, we propose a CNN classification system for false positive reduction of initially detected lung nodule candidates. First, image patches of lung nodule candidates are extracted from CT scans to train a CNN classifier. To reflect the volumetric contextual information of lung nodules to 2D image patch, we propose a weighted average image patch (WAIP) generation by averaging multiple slice images of lung nodule candidates. Moreover, to emphasize central slices of lung nodules, slice images are locally weighted according to Gaussian distribution and averaged to generate the 2D WAIP. With these extracted patches, 2D CNN is trained to achieve the classification of WAIPs of lung nodule candidates into positive and negative labels. We used LUNA 2016 public challenge database to validate the performance of our approach for false positive reduction in lung CT nodule classification. Experiments show our approach improves the classification accuracy of lung nodules compared to the baseline 2D CNN with patches from single slice image.