Evaluation of Image Noise from Computed Tomography in an Organ-Specific Anthropomorphic Phantom

Abstract

Computed tomography (CT) is used ubiquitously for diagnostic imaging, with novel iterative reconstructions such as hybrid iterative reconstruction with partial model-based component (ASiR-V) and deep learning image reconstruction (DLIR) increasingly utilized. In conjunction, CT vendors have developed a noise index (NI) to reduce radiation dose during protocol development. However, it is unclear how these reconstruction algorithms reduce noise as NI changes, particularly across multiple organs. The goal of this study was to utilize an anthropomorphic phantom to evaluate image noise against the prescribed NI across different organs, determine if DLIR reduces image noise compared to ASiR-V, and use this relationship to determine the maximum volume CT dose index (CTDIvol) reduction at equivalent noise levels. Repeat images were acquired on a GE HealthCare Revolution Apex CT scanner across NI values in an anthropomorphic adult-sized phantom from Kyoto Kagaku with ASiR-V and DLIR. Organs-of-interest were contoured using TotalSegmentator in 3D Slicer. The correlation between the CTDIvol and NI was determined. The standard deviation of the difference in the repeat images was used as a noise metric (NM), correlated with NI, used to determine the NI at equivalent NM between ASiR-V and DLIR, then applied to determine the dose reduction and difference in NM. The relationship between NI and the resulting CTDIvol was found to be proportional to a power law of exponent -2.23. The maximum potential absolute dose reduction from using DLIR was found in the liver at 1.3 mGy with an NI of 7.9. Across all organs, the relative dose reduction increased, the NM-to-NI ratio decreased, and the noise reduction increased across NI. The relationship between image noise and the prescribed NI differed between ASiR-V and DLIR and varied by organ, with model-based dose reductions of up to 1.3 mGy in the liver.