Intelligence-Aware Batch Processing for TMA with Bearings-Only Measurements

This paper develops a framework to track the trajectory of a target in 2D by considering a moving ownship able to measure bearing measurements. Notably, the framework allows one to incorporate additional information (e.g., obtained via intelligence) such as knowledge on the fact the target’s trajectory is contained in the intersection of some sets or the fact it lies outside the union of other sets. The approach is formally characterized by providing a constrained maximum likelihood estimation (MLE) formulation and by extending the definition of the Cramér–Rao lower bound (CRLB) matrix to the case of MLE problems with inequality constraints, relying on the concept of generalized Jacobian matrix. Moreover, based on the additional information, the ownship motion is chosen by mimicking the Artificial Potential Fields technique that is typically used by mobile robots to aim at a goal (in this case, the region where the target is assumed to be) while avoiding obstacles (i.e., the region that is assumed not to intersect the target’s trajectory). In order to show the effectiveness of the proposed approach, the paper is complemented by a simulation campaign where the MLE computations are carried out via an evolutionary ant colony optimization software, namely, mixed-integer distributed ant colony optimization solver (MIDACO-SOLVER). As a result, the proposed framework exhibits remarkably better performance, and in particular, we observe that the solution is less likely to remain stuck in unsatisfactory local minima during the MLE computation.

Quantitative Molecular Imaging of Breast Microcalcification Composition using Photon-Counting Spectral Computed Tomography

Mammographic screening for breast cancer is unable to distinguish molecular differences between hydroxyapatite (HA) microcalcifications (μcals) that are associated with malignancy and calcium oxalate (CaOx) μcals that are benign. Therefore, the objective of this study was to investigate quantitative material decomposition of model breast μcals of clinically-relevant composition and size using spectral photon-counting computed tomography (PCCT). Model μcals composed of HA, CaOx, and dicalcium phosphate (DCP) were treated as materials containing spatially coincident elemental compositions of calcium (Ca), phosphorus (P), and oxygen (O). Elemental decomposition was performed using constrained maximum-likelihood estimation in the image domain. Images were acquired with a commercial, preclinical PCCT system (MARS Bioimaging) with five energy bins selected to maximize counts at low photon energies and spectral differences between Ca and P. Elemental concentrations of Ca and P within the each μcal composition were accurately identified and quantified with a root-mean-squared error < 12%. HA and CaOx μcals, < 1 mm is size, were accurately discriminated by the measured P content with an area under the receiver operating characteristic curve (AUC) > 0.9. The mole fraction of P, P/(Ca+P), was able to discriminate all three μcal compositions with AUC > 0.8 for μcals < 1 mm is size and AUC = 1 for μcals > 2 mm in size. The overall accuracy for the classification of μcal types and quantification of P was robust against different assumptions in the elemental decomposition calibration, but quantification of Ca was improved with assumptions that most accurately accounted for the molar volume of each element within μcal compositions. Thus, PCCT enabled quantitative molecular imaging of breast μcal composition, which is not possible with current clinical molecular imaging modalities.