CT reconstruction algorithms affect histogram and texture analysis: evidence for liver parenchyma, focal solid liver lesions, and renal cysts
- 313 Downloads
To determine the effects of different reconstruction algorithms on histogram and texture features in different targets.
Materials and methods
Among 3620 patients, 480 had normal liver parenchyma, 494 had focal solid liver lesions (metastases = 259; hepatocellular carcinoma = 99; hemangioma = 78; abscess = 32; and cholangiocarcinoma = 26), and 488 had renal cysts. CT images were reconstructed with filtered back-projection (FBP), hybrid iterative reconstruction (HIR), and iterative model reconstruction (IMR) algorithms. Computerized histogram and texture analyses were performed by extracting 11 features.
Different reconstruction algorithms had distinct, significant effects. IMR had a greater effect than HIR. For instance, IMR had a significant effect on five features of liver parenchyma, nine features of focal liver lesions, and four features of renal cysts on portal-phase scans and four, eight, and four features, respectively, on precontrast scans (p < 0.05). Meanwhile, different algorithms had a greater effect on focal liver lesions (six in HIR and nine in IMR on portal-phase, three in HIR, and eight in IMR on precontrast scans) than on liver parenchyma or cysts. The mean attenuation and standard deviation were not affected by the reconstruction algorithm (p > .05). Most parameters showed good or excellent intra- and interobserver agreement, with intraclass correlation coefficients ranging from 0.634 to 0.972.
Different reconstruction algorithms affect histogram and texture features. Reconstruction algorithms showed stronger effects in focal liver lesions than in liver parenchyma or renal cysts.
• Imaging heterogeneities influenced the quantification of image features.
• Different reconstruction algorithms had a significant effect on histogram and texture features.
• Solid liver lesions were more affected than liver parenchyma or cysts.
KeywordsLiver Kidney Cyst Neoplasms Tomography
Angular second moment
Gray level co-occurrence matrix
Hybrid iterative reconstruction
Intraclass correlation coefficient
Inverse difference moment
Iterative model reconstruction
Picture archiving and communications system
Region of interest
We thank Bonnie Hami, M.A. (USA), for her editorial assistance in the preparation of this manuscript.
The authors state that this work has not received any funding.
Compliance with ethical standards
The scientific guarantor of this publication is Joon Koo Han, M.D.
Conflict of interest
The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
Statistics and biometry
Su Joa Ahn, MD, has significant statistical expertise and no complex statistical methods were necessary for this paper.
Written informed consent was waived by the Institutional Review Board.
Institutional Review Board approval was obtained (IRB No. 1706–128-861).
• Diagnostic or prognostic study
• Performed at one institution
- 21.Brown KM, Zabic S, Koehler T (2012) Acceleration of ML iterative algorithms for CT by the use of fast start images. Physics of Medical Imaging. https://doi.org/10.1117/12.911412