Juntunen, M. A. K., Rautiainen, J., Hänninen, N. E., & Kotiaho, A. O. (2022). Harmonization of technical image quality in computed tomography: Comparison between different reconstruction algorithms and kernels from six scanners. Biomedical Physics & Engineering Express, 8(3), 037002. https://doi.org/10.1088/2057-1976/ac605b
Harmonization of technical image quality in computed tomography : comparison between different reconstruction algorithms and kernels from six scanners
|Author:||Juntunen, Mikael A.K.1,2; Rautiainen, Jari1,3; Hänninen, Nina E.4;|
1Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
2Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
3Department of Radiology, Lapland Central Hospital, Rovaniemi, Finland
4Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
5Suomen Terveystalo Oy, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 9.1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022090257115
|Publish Date:|| 2023-04-05
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.
Biomedical physics & engineering express
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
217 Medical engineering
This is a peer-reviewed, un-copyedited version of an article accepted for publication/published in Biomedical Physics & Engineering Express. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at 10.1088/2057-1976/ac605b.