Abstract. The proposed project concentrates on technology developments to enable high sensitivity, bias-tolerant spectral CT for accurate quantitation of iodine concentration. Spectral CT has the potential of providing true quantitative information of tissue composition and provides an avenue for combined functional and structural imaging. High- sensitivity spectral CT accommodates anatomical sites that are traditionally hard to image, and reliable meas- urements of iodine perfusion allow additional quantitative measures such as tissue texture to aid diagnosis and clinical decision making. In the case of pancreatic cancer, the complex tumor microenvironment and the conse- quential poor perfusion characteristics lead to difficulty in diagnosis, staging, and treatment assessment. The need for visualizing low-enhancing lesions and the benefit of extracting quantitative information directly from image data strongly motivate a high-sensitivity imaging modality for reproducible iodine measurements. The need for visualizing low-enhancing lesions and the benefit of extracting quantitative information directly from image data strongly motivate a high-sensitivity imaging modality for reproducible iodine measurements. How- ever, state-of-the-art spectral CT presents large quantitation bias, i.e., inaccuracies in measured iodine concen- tration compared to the truth. We identify three major sources that contribute to quantitation bias: imaging system (e.g., spectrum mismatch), post-processing (e.g., biased estimator), and patient (scatter, beam hardening). The bias effect in current spectral CT cannot be fully eliminated by increasing radiation exposure, and has complex dependencies on the imaging system, imaging techniques, patient habitus, and processing algorithms. This in- accuracy is a major impediment to pancreatic cancer management and quantitative applications in general. The overall goal of this proposal is to develop robust, high-sensitivity spectral CT solutions that will enhance sensi- tivity and reduce variability in iodine quantitation, which in turn enables accurate, high-performance spectral biomarkers for disease management. The following specific aims will be pursued: (1) to develop an end-to-end, modular theoretical model for robust spectral CT design and optimization, (2) to develop bias-tolerant processing pipeline, and (3) to implement and evaluate high performance, hybrid spectral CT solutions on an experimental CT bench. Completion of the proposed efforts enables robust, high sensitivity spectral CT for improved tumor detection and characterization through accurate, high performance spectral biomarkers. Vendor- and spectral technology-independent outcomes of the proposal include: optimized, patient-specific protocols; post-processing pipelines that are robust against quantitative bias and variability; and the next generation spectral CT system designs for enhanced iodine quantitation. Achievements from the proposed project will improv...