Imaging task-based optimal kV and mA selection for CT radiation dose reduction: from filtered backprojection (FBP) to statistical model based iterative reconstruction (MBIR)

Abstract

Optimal selections of tube potential (kV) and tube current (mA) are essential in maximizing the diagnostic potential of a given CT technology while minimizing radiation dose. The use of a lower tube potential may improve image contrast, but may also require a significantly higher tube current to compensate for the rapid decrease of tube output at lower tube potentials. Therefore, the selection of kV and mA should take those kinds of constraints as well as the specific diagnostic imaging task in to consideration. For conventional quasi-linear CT systems employing linear filtered back-projection (FBP) image reconstruction algorithm, the optimization of kV-mA combinations are relatively straightforward, as neither spatial resolution nor noise texture has significant dependence on kV and mA settings. In these cases, zero-frequency analysis such as contrast-to-noise ratio (CNR) or normalized CNR by dose (CNRD) can be used for optimal kV-mA selection. The recently introduced statistical model-based iterative reconstruction (MBIR) method, however, has introduced new challenges to optimal kV and mA selection, as both spatial resolution and noise texture become closely correlated with kV and mA. In this work, a task-based approach based on modern signal detection theory and the corresponding frequency-dependent analysis has been proposed to perform the kV and mA optimization for both FBP and MBIR. By performing exhaustive measurements of task-based detectability index through the technically accessible kV-mA parameter space, iso-detectability contours were generated and overlaid on top of iso-dose contours, from which the kV-mA pair that minimize dose yet still achieving the desired detectability level can be identified.