TU‐EF‐204‐03: Task‐Based KV and MAs Optimization for Radiation Dose Reduction in CT: From FBP to Statistical Model‐Based Iterative Reconstruction (MBIR)

Abstract

Purpose:The introduction of the highly nonlinear MBIR algorithm to clinical CT systems has made CNR an invalid metric for kV optimization. The purpose of this work was to develop a task‐based framework to unify kV and mAs optimization for both FBP‐ and MBIR‐based CT systems.Methods:The kV‐mAs optimization was formulated as a constrained minimization problem: to select kV and mAs to minimize dose under the constraint of maintaining the detection performance as clinically prescribed. To experimentally solve this optimization problem, exhaustive measurements of detectability index (d’) for a hepatic lesion detection task were performed at 15 different mA levels and 4 kV levels using an anthropomorphic phantom. The measured d’ values were used to generate an iso‐detectability map; similarly, dose levels recorded at different kV‐mAs combinations were used to generate an iso‐dose map. The iso‐detectability map was overlaid on top of the iso‐dose map so that for a prescribed detectability level d’, the optimal kV‐mA can be determined from the crossing between the d’ contour and the dose contour that corresponds to the minimum dose.Results:Taking d’=16 as an example: the kV‐mAs combinations on the measured iso‐d’ line of MBIR are 80–150 (3.8), 100–140 (6.6), 120–150 (11.3), and 140–160 (17.2), where values in the parentheses are measured dose values. As a Result, the optimal kV was 80 and optimal mA was 150. In comparison, the optimal kV and mA for FBP were 100 and 500, which corresponded to a dose level of 24 mGy. Results of in vivo animal experiments were consistent with the phantom results.Conclusion:A new method to optimize kV and mAs selection has been developed. This method is applicable to both linear and nonlinear CT systems such as those using MBIR. Additional dose savings can be achieved by combining MBIR with this method.This work was partially supported by an NIH grant R01CA169331 and GE Healthcare. K. Li, D. Gomez‐Cardona, M. G. Lubner: Nothing to disclose. P. J. Pickhardt: Co‐founder, VirtuoCTC, LLC Stockholder, Cellectar Biosciences, Inc. G.‐H. Chen: Research funded, GE Healthcare; Research funded, Siemens AX.