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

Dedicated application-specific CT systems are popular solutions to high-resolution clinical needs. Some applications, such as mammography and extremities 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. As resolution requirements increase, more accurate models of system properties are needed. Using a high-fidelity measurement model, we analyze the effects of shift-variant focal spot blur due to depth-dependence and anode angulation on image quality throughout the three-dimensional field of view of a simulated extremities scanner. A model of the shift-variant blur associated with this device is then incorporated into a Model-Based Iterative Reconstruction (MBIR) algorithm, which is then compared to FDK and MBIR with simpler blur models (i.e., no projection blur and shift-invariant projection blur) at select locations throughout the field of view. We show that shift-variant focal spot blur leads to location-dependent imaging performance. Furthermore, changing the orientation of the X-ray tube alters this spatial dependence. The analysis suggests methods to improve imaging performance based on specific image quality needs. For example, for small region of interest imaging, a transaxial X-ray tube orientation provides the best local image quality at a specific location, while for large objects an axial X-ray tube orientation provides better image quality uniformity. The results also demonstrate that image quality can be improved by combining accurate blur modeling with MBIR. Specifically, across the entire field of view, MBIR with shift-variant blur modeling yielded the best image quality, followed by MBIR with a shift-invariant blur model, MBIR with an identity blur model, and FDK, respectively. These results suggest a number of opportunities for the optimization of imaging system performance in the hardware setup, the imaging protocol, and the reconstruction approach. While the high-fidelity models used here are applied using the specifications of a dedicated extremities imaging system, the methods are general and may be applied to optimize imaging performance in any CT system.