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Dedicated application-specific CT systems are popular solutions tohigh-resolution clinical needs. Some applications, such as mammography andextremities imaging, require spatial resolution beyond current capabilities.Thorough understanding of system properties may help tailor system design,acquisition protocols, and reconstruction algorithms to improve image quality.Using a high-fidelity measurement model, we analyze the effects ofshift-variant focal spot blur due to depth-dependence and anode angulation onimage quality throughout the three-dimensional field of view of a simulatedextremities scanner. A model of the shift-variant blur associated with thisdevice is then incorporated into a Model-Based Iterative Reconstruction (MBIR)algorithm, which is then compared to FDK and MBIR with simpler blur models atselect locations throughout the field of view. We show that shift-variant focalspot blur leads to location-dependent imaging performance. Furthermore,changing the orientation of the X-ray tube alters this spatial dependence. Theanalysis suggests methods to improve imaging performance based on specificimage quality needs. The results also demonstrate that image quality can beimproved by combining accurate blur modeling with MBIR. Specifically, acrossthe entire field of view, MBIR with shift-variant blur modeling yielded thebest image quality, followed by MBIR with a shift-invariant blur model, MBIRwith an identity blur model, and FDK, respectively. These results suggest anumber of opportunities for the optimization of imaging system performance inthe hardware setup, the imaging protocol, and the reconstruction approach.While the high-fidelity models used here are applied using the specificationsof a dedicated extremities imaging system, the methods are general and may beapplied to optimize imaging performance in any CT system.

High-Fidelity Modeling of Shift-Variant Focal-Spot Blur for High-Resolution CT