Impact of noise reduction schemes on quantitative accuracy of CT numbers

Abstract

Purpose: In previous works, it has been demonstrated that for filtered backprojection (FBP) reconstruction-based computed tomography (CT) images, the measured CT numbers are biased and the bias level decreases with increasing radiation dose. Low-dose scans typically include noise reduction schemes to reduce noise level. The purpose of this work was to investigate the potential impact of different noise reduction schemes on the CT number bias. Methods: Three different filtration methods: Gaussian, adaptive trimmed mean (ATM), and anisotropic diffusion (AD) were implemented to reduce noise. All filters were independently applied in three different domains: raw counts, log-processed sinogram, or reconstructed image domain. A quality assurance phantom was scanned on a benchtop CT cone beam CT system, at dose levels ranging from 0.6 to 4.0 mGy. The conventional FBP reconstructions were performed to reconstruct CT images for the study of CT number biases. The CT number bias of different material inserts in the phantom was then measured. To further study the overall impact of CT number bias together with the potential consequences of noise reduction schemes on both the spatial resolution and noise characteristics, the task-based detectability of a high-contrast and high spatial resolution imaging task was used as an example to assess the performance of each noise reduction scheme. To qualitatively assess the impact of these noise reduction schemes on image, an anthropomorphic head phantom was also scanned on the benchtop CT system and processed with the above noise reduction schemes to generate images for demonstration. Results: Our results demonstrated the following major findings: (a) CT number bias can be significantly reduced when the noise reduction schemes are implemented in the raw counts domain; CT number bias cannot be reduced when these noise reduction schemes are implemented either in the reconstructed image domain or in the log-processed sinogram domain. (b) The extent of CT number bias reduction is dependent on both the material composition and noise reduction parameters. (c) The overall impact of the noise reduction schemes can be studied using the task-based detectability analysis framework and this framework can be used to select the appropriate parameters in each noise reduction scheme to optimize the performance for a given imaging task. Conclusions: Noise reduction schemes can be used to considerably reduce CT number bias when they are implemented in the raw counts domain; however, their application cannot be arbitrarily extended to either the log-processed sinogram data domain or image domain. Trade-offs between bias reduction and overall image quality must be studied for an optimal performance of a given imaging task.

Correcting statistical CT number biases without access to raw detector counts: Applications to high spatial resolution photon counting CT imaging

“…Due to radiation safety and other practical considerations, the estimation of sinogram data for CT reconstruction has been using N instead of N. As reviewed in Section 2.1, the use of stochastic N leads to statistical bias and noise. The statistical bias becomes more severe with lower radiation exposure and smaller detector pixels, such as those used in low-dose highresolution PCD-CT. Statistical bias has been observed in both EID-CT and PCD-CT, and previous works have proposed methods to mitigate it using data filtration 29 or iterative reconstruction techniques. 30 The effectiveness of these methods is dependent on the parameter selection and often has a tradeoff with spatial resolution.…”

“…Statistical bias has been observed in both EID‐CT and PCD‐CT, and previous works have proposed methods to mitigate it using data filtration 29 or iterative reconstruction techniques 30 . The effectiveness of these methods is dependent on the parameter selection and often has a tradeoff with spatial resolution.…”

Algorithm development for automatic laser alignment assessment on an ACR CT phantom and its evaluation on sixteen CT scanners

“…A recent phantom study by [30] also reported that a deviation of 4.3 HU was obtained from a misalignment of 80 mm with a low-tube voltage. This can result in inaccurate diagnosis [31]. In some circumstances, the misalignment can increase the patient dose, causing an unnecessary dose received by patient.…”

“…However, misalignment up to 10 mm does not cause significant artifacts to appear. Several studies have reported that misalignment can cause changes in image noise and dose at certain position within the phantoms or patients [29][30][31][32][33]. Further studies are needed on the influence of misalignment on the emergence of artifacts.…”

Patch-wise Deep Metric Learning for Unsupervised Low-Dose CT Denoising

“…Hence, the networks often result the distortion in pixel values. Unfortunately, the change of the CT numbers has detrimental effects to the radiological diagnostic performance, since it is used as a diagnostic indicator [6]. Especially in the temporal bone CT scans which we are interested in this paper, the CT number supports the evaluation of a pathologies of the ear, such as an inflammation within the inner ear, and a cholesteatoma in early stage [5,13,1].…”