| Quantifying imaging performance is an important aspect in the development, optimization, and assessment of medical imaging systems. This thesis addresses new challenges in the characterization of imaging performance for advanced x-ray tomographic imaging technologies. Central to the work is a task-based cascaded systems analysis framework that encompasses aspects of system geometry, x-ray beam characteristics, dose, detector design, background anatomy, model observers, and the imaging task. The metrology throughout includes Fourier domain descriptors of spatial resolution (modulation transfer function, MTF), noise (noise-power spectrum, NPS), noise-equivalent quanta (NEQ), and task-based detectability. Central elements and advances of the work include: a task-based model for 3D imaging performance in tomosynthesis and cone-beam CT (CBCT); generalization of imaging performance metrics to include the influence of anatomical background clutter; validation of the model in comparison to human observer performance; extension to dual-energy (DE) tomographic imaging; analysis of non-stationary (i.e., spatially varying) signal and noise characteristics; and extension to model-based statistical image reconstruction. In each case, the analytical framework demonstrates the importance of task-based assessment and the capability for system optimization in a fairly broad scope of clinical applications ranging from breast to abdominal and musculoskeletal imaging. The validity of the framework in describing "local" signal and noise characteristics is demonstrated under conditions of strong nonstationarity, ranging from simple phantoms to complex anthropomorphic scenes. In addition to providing a framework for system design and optimization, the analysis opens potential new opportunities in task-based imaging and statistical reconstruction, with examples demonstrated in the design of optimal regularization in iterative reconstruction. |
