Three-Dimensional NEQ Transfer Characteristics of Volume CT using Direct and Indirect-Detection Flat-Panel Imagers

Foremost among the promising imaging performance characteristics of cone-beam CT using flat-panel imagers is the ability to form volumetric images with soft-tissue contrast visibility in combination with sub-millimeter 3-D spatial resolution. Each of these two essential characteristics is intimately related to the spatial-frequency-dependent signal and noise transfer characteristics of the imaging system. Therefore a thorough, quantitative analysis of the 3-D noiseequivalent quanta (NEQ) and detective quantum efficiency (DQE) is essential to understanding the volumetric imaging performance of such systems, identifying the factors that limit performance, and revealing their full potential. This paper presents investigation of the 3-D NEQ and DQE for volume CT systems based on direct and indirectdetection flat-panel imagers (FPIs). Classical descriptions of image noise in transaxial CT are extended to the case of non-ideal 2-D detectors and 3-D image reconstruction. Definitions of NEQ and DQE are extended to provide figures of merit for 3-D imaging performance. A complex interplay between the system transfer functions, 3-D noise aliasing, and the 3-D DQE is uncovered, revealing several important phenomena: 1.) 3-D NPS aliasing is a significant factor in the reconstruction process affecting DQE; 2.) the degree of 3-D NPS aliasing is different for direct and indirect-detection FPIs and is related in non-trivially to the detector MTF and reconstruction filter; 3.) the 3-D NEQ depends significantly on the choice of reconstruction filter – in contrast to the classical notion that NEQ is independent of such – and the effect is wholly attributable to 3-D NPS aliasing; and 4.) the 3-D DQE for volume reconstructions is asymmetric between transverse and sagittal/coronal planes. Results for 3-D NEQ and DQE are integrated with 3-D spatial-frequencydependent descriptions of imaging task (e.g., ideal observer detection and/or discrimination tasks) to yield the 3-D detectability index, helping to bridge the gap between NEQ and the performance of model observers.