Comparison of CT image quality between the AIDR 3D and FIRST iterative reconstruction algorithms: an assessment based on phantom measurements and clinical images

Abstract

Modern CT iterative reconstruction algorithms are transitioning from a statistical-based to model-based approach. However, increasing complexity does not ensure improved image quality for all indications, and thorough characterization of new algorithms is important to understand their potential clinical impacts. This study performs both quantitative and qualitative analyses of image quality to compare Canon’s statistical-based Adaptive Iterative Dose Reduction 3D (AIDR 3D) algorithm to its model-based algorithm, Forward-projected model-based Iterative Reconstruction SoluTion(FIRST). A phantom was used to measure the task-specific modulation transfer function (MTFTask), the noise power spectrum (NPS), and the low-contrast object-specific CNR (CNRLO) for each algorithm using three dose levels and the convolution algorithm (kernel) appropriate for abdomen, lung, and brain imaging. Additionally, MTFTask was measured at four contrast levels, and CNRLO was measured for two object sizes. Lastly, three radiologists participated in a preference study to compare clinical image quality for three study types: non-contrast abdomen, pulmonary embolism (PE), and lung screening. Nine questions related to the appearance of anatomical features or image quality characteristics were scored for twenty exams of each type. The behavior of both algorithms depended strongly on the kernel selected. Phantom measurements suggest that FIRST should be beneficial over AIDR 3D for abdomen imaging, but do not suggest a clear overall benefit to FIRST for lung or brain imaging; metrics suggest performance may be equivalent to or slightly favor AIDR 3D, depending on the size of the object being imaged and whether spatial resolution or low-contrast resolution is more important for the task at hand. Overall, radiologists strongly preferred AIDR 3D for lung screening, slightly preferred AIDR 3D for non-contrast abdomen, and had no preference for PE. FIRST was superior for the reduction of metal artifacts. Radiologist preference may be influenced by changes to noise texture.

Analysis of the precision of modulation transfer function measurements with a circular edge: influence of object circularity

“…As shown in Eq. (2), the degree of blurring depending on changes in circularity is an unsharpness coefficient independent of the true MTF, but for a high-resolution image 30 and using a high spatial frequency enhancement type algorithm, 18 we believe that the effect of distortion appears more prominently, owing to the noise or structures enhanced by imaging process, even if the imaging conditions are the same. Thus, although there are some research limitations, the knowledge gap regarding the influence of changes in the circularity of circular edges on MTF measurements addressed by this study should contribute to the appropriate interpretation of evaluation results and quantitative understanding of measurement errors when obtaining the image quality characteristics of various imaging modalities using the circular edge technique.…”

“…[14][15][16] Although a study has mentioned the feasibility of novel metrics to assess these complex characteristics, 17 a thorough characterization of new algorithms applying the circular edge method to understand their potential clinical impact remains important. 18 Using the circular edge method, low-contrast objects are applied to evaluate the complex physical properties of images, 19 assuming, for example, the need to detect low-contrast signals in noise-rich images for clinical practice. In such cases, it is necessary to improve the signal-tonoise ratio of the analysis image by various ingenuities to improve the precision of the measured MTF.…”

“…However, some reconstruction algorithms exhibit nonlinear behavior that is induced by degradation of the resolution characteristics and changes in the noise texture, depending on imaging conditions such as the noise level and object contrast 14 – 16 Although a study has mentioned the feasibility of novel metrics to assess these complex characteristics, 17 a thorough characterization of new algorithms applying the circular edge method to understand their potential clinical impact remains important 18 …”