Harmonization of technical image quality in computed tomography: comparison between different reconstruction algorithms and kernels from six scanners

Abstract

Purpose: The radiology department faces a large number of reconstruction algorithms and kernels during their computed tomography (CT) optimization process. These reconstruction methods are proprietary and ensuring consistent image quality between scanners is becoming increasingly difficult. This study contributes to solving this challenge in CT image quality harmonization by modifying and evaluating a reconstruction algorithm and kernel matching scheme. Methods: The Catphan 600 phantom was scanned with six different CT scanners from four vendors. The phantom was scanned with volumetric CT dose indices (CTDIvols) of 10 mGy and 40 mGy, and the data were reconstructed using 1 mm and 5 mm slices with each combination of reconstruction algorithm, body region kernel, and iterative and deep learning reconstruction strength. A matching scheme developed in previous research, which utilizes the noise power spectrum (NPS) and modulation transfer function (MTF), was modified based on our organization’s needs and used to identify the matching reconstruction algorithms and kernels between different scanners. Results: The matching paradigm produced good matching results, and the mean ± standard deviation (median) matching function values for the different acquisition settings were (a value of 1 indicates a perfect match): CTDIvol 10 mGy, 1 mm slice: 0.78 ± 0.31 (0.94); CTDIvol 10 mGy, 5 mm slice: 0.75 ± 0.33 (0.93); CTDIvol 40 mGy, 1 mm slice: 0.81 ± 0.28 (0.95); CTDIvol 40 mGy, 5 mm slice: 0.75 ± 0.33 (0.93). In general, soft reconstruction kernels, i.e., noise-reducing kernels that reduce sharpness, of one vendor were matched with the soft kernels of another vendor, and vice versa for sharper kernels. Conclusions: Combined quantitative assessment of NPS and MTF allows effective strategy for harmonization of technical image quality between different CT scanners. A software was also shared to support CT image quality harmonization in other institutions.

Inter-vendor harmonization of CT reconstruction kernels using unpaired image translation

“…Another physics-based approach implemented a generative deep learning model for harmonization and measured its performance on image similarity metrics and emphysema-based imaging biomarkers 7 . Juntunen et al 8 investigated harmonization of image quality in computed tomography using reconstruction kernels and algorithms obtained from six different scanners and determined the noise power spectrum and modulation transfer function for the purpose of image harmonization. Deep learning approaches perform kernel conversion on paired data by learning the differences between high and low-resolution images using convolutional neural networks (CNN) 9 .…”

Optimization of the Reconstruction Settings for Low-Dose Ultra-High-Resolution Photon-Counting Detector CT of the Lungs

“…Image quality depends not only on the established detectors but also on the reconstruction parameters, i.e., the reconstruction kernel, use of iterative reconstruction, slice thickness, and in-plane resolution [17]. The first clinically approved PCD-CT scanner was introduced with a novel iterative reconstruction algorithm known as quantum iterative reconstruction (QIR, Siemens Healthineers, Forchheim, Germany), which has four strength levels (QIR-1 to QIR-4) and is specifically designed to match the hardware and software needs of the PCD-CT system.…”