TH-C-18A-01: Is Automatic Tube Current Modulation Still Necessary with Statistical Iterative Reconstruction?

Abstract

Purpose: Automatic tube current modulation (TCM) has been widely used in modern multi-detector CT to reduce noise spatial nonuniformity and streaks to improve dose efficiency. With the advent of statistical iterative reconstruction (SIR), it is expected that the importance of TCM may diminish, since SIR incorporates statistical weighting factors to reduce the negative influence of photon-starved rays. The purpose of this work is to address the following questions: Does SIR offer the same benefits as TCM? If yes, are there still any clinical benefits to using TCM? Methods: An anthropomorphic CIRS chest phantom was scanned using a state-of-the-art clinical CT system equipped with an SIR engine (Veo™, GE Healthcare). The phantom was first scanned with TCM using a routine protocol and a low-dose (LD) protocol. It was then scanned without TCM using the same protocols. For each acquisition, both FBP and Veo reconstructions were performed. All scans were repeated 50 times to generate an image ensemble from which noise spatial nonuniformity (NSN) and streak artifact levels were quantified. Monte-Carlo experiments were performed to estimate skin dose. Results: For FBP, noise streaks were reduced by 4% using TCM for both routine and LD scans. NSN values were actually slightly higher withmore » TCM (0.25) than without TCM (0.24) for both routine and LD scans. In contrast, for Veo, noise streaks became negligible (<1%) with or without TCM for both routine and LD scans, and the NSN was reduced to 0.10 (low dose) or 0.08 (routine). The overall skin dose was 2% lower at the shoulders and more uniformly distributed across the skin without TCM. Conclusion: SIR without TCM offers superior reduction in noise nonuniformity and streaks relative to FBP with TCM. For some clinical applications in which skin dose may be a concern, SIR without TCM may be a better option. K. Li, W. Zhao, D. Gomez-Cardona: Nothing to disclose; G.-H. Chen: Research funded, General Electric Company Research funded, Siemens AG Research funded, Varian Medical Systems, Research funded, Hologic, Inc.« less