Differences in Image Information on Abdominal CT Scan with Clinical Intra-Abdominal Tumors Between Variations in ASiR-V 60%, 70%, and 80%
DOI:
https://doi.org/10.26630/jk.v17i1.5633Keywords:
Anatomy, Image quality, Signal-to-Noise RatioAbstract
Previous studies have shown that an ASiR-V rate of 60% is optimal for contrast-enhanced abdominal CT scans in patients with kidney stones. In this study, however, the ASiR-V variations tested were 60%, 70%, and 80%, with a focus on clinical intra-abdominal tumors. The objective was to measure the optimal signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) values for CT images of these tumors. The research employed a quantitative retrospective experimental design with a sample of 10 patients who met the inclusion criteria. The findings reveal notable differences in SNR, CNR, and Hounsfield Unit (HU) values across the ASiR-V variations, with the 70% ASiR-V showing the highest values for both SNR and CNR.
References
Bushberg, J. T., Seibert, J. A., & Boone, J. M. (2012). The Essential Physics of Medical Imaging. Philadelphia, PA, USA: Lippincott Williams & Wilkins.
Caruso, D., Zerunian, M., Pucciarelli, F., Bracci, B., Polici, M., D’Arrigo, B., … Laghi, A. (2021). Influence of Adaptive Statistical Iterative Reconstructions on CT Radiomic Features in Oncologic Patients. Diagnostics (Basel, Switzerland), 11(6). https://doi.org/10.3390/diagnostics11061000
Cau Shayne, C. (2023). Computed Tomography: A Primer for Radiographers. Boca Raton: CRC Press. https://doi.org/10.1201/9781003132554
Choopani, M. R., Abedi, I., & Dalvand, F. (2023). Quality assessment of computed tomography images using a channelized hoteling observer: Optimization of protocols in clinical practice. Advanced Biomedical Research, 12(1), 1–9. https://doi.org/10.4103/abr.abr_353_21
Flower, M. A. (2012). Webb’s Physics Of Medical Imaging. Boca Raton: CRC Press.
Goodenberger, M. H. (2018). CT Image Quality Evaluation of a New Iterative Reconstruction Algorithm in the Abdomen (ASIR-V) with comparison to MBIR, ASIR, and FBP Reconstructions. Journal of Computer Assisted Tomography, 42(2), 184–190. https://doi.org/10.1097/RCT.0000000000000666
Guangyi, C. H. E. N., Lixiang, X. I. E., Chunfeng, H. U., Hao, W. A. N. G., Kun, D. O. N. G., & Jing, Z. H. A. N. G. (2024). Research of ASiR-V on image quality influence of CT with different adult abdominal parenchyma organs. Journal of Xuzhou Medical University, 43(12), 926-930. https://dx.doi.org/10.3969/j.issn.2096-3882.2023.12.012
Haaga, J., & Forsting, M. (2017). CT and MRI of the whole body. Philadelphia, PA, USA: Elsevier.
Hara, A. K., Paden, R. G., Silva, A. C., Kujak, J. L., Lawder, H. J., & Pavlicek, W. (2009). Iterative reconstruction technique for reducing body radiation dose at CT: feasibility study. AJR. American Journal of Roentgenology, 193(3), 764–771. https://doi.org/10.2214/AJR.09.2397
Kalender, W. A. (2022). Computed Tomography: Fundamentals, System Technology, Image Quality, Applications. Weinheim, Germany: Wiley-VCH.
Kwon, H. M. (2015). The adaptive statistical iterative reconstruction-V technique for radiation dose reduction in abdominal CT : comparison with the adaptive statistical iterative reconstruction technique. British Journal of Radiology, 88(1054). https://doi.org/10.1259/bjr.20150463
Lee, E., & Kim, D. S. (2018). Precision analysis of the noise power spectrum estimate in radiography imaging. In J. Y. Lo, T. G. Schmidt, & G.-H. Chen (Eds.), Medical Imaging 2018: Physics of Medical Imaging (Vol. 10573, p. 1057361). SPIE. https://doi.org/10.1117/12.2293614
Noveranty, A., Purwaningsih, S., & Fendriani, Y. (2024). Analisis Pengaruh Variasi Faktor Eksposi Pada CT-Scan. Journal Online of Physics, 9(3), 53–59. https://doi.org/10.22437/jop.v9i3.35155
Pratama Putra, R. A., Rahardjo, P., & Pramono, P. (2020). Analysis of Asir Variation Effect on SNR on Unenhanced Abdominal CT Scan in Urolithiasis. Journal of Vocational Health Studies, 4(2), 78. https://doi.org/10.20473/jvhs.v4.i2.2020.78-82
Prezzi, D., Goh, V., Virdi, S., Mallett, S., Grierson, C., & Breen, D. J. (2017). European Journal of Radiology Open Adaptive statistical iterative reconstruction improves image quality without affecting perfusion CT quantitation in primary colorectal cancer. European Journal of Radiology Open, 4(June), 69–74. https://doi.org/10.1016/j.ejro.2017.05.003
Saifudin, E. A. (2017). Application of ASIR and Windowing to Image Anatomical Information of CT Scan Stonography. Int J of Allied Med Sci and Clin Res 2017; 5(4. International Journal of Allied Medical Sciences and Clinical Research, 5(4). https://ijamscr.com/ijamscr/article/view/467
Samudra, A., Fitriana, L., Hidayat, F. R., Mukti, K., Giovany, A. G., & Caesarendra, W. (2025). Analysis of Differences in Image Quality and Anatomical Information of Head CT Scan Examination in Non-Hemorrhagic Stroke Cases Using Sinogram Affirmed Iterative Reconstruction ( SAFIRE ). Journal of Electronics, Electromedical Engineering, and Medical Informatics, 7(2), 270–282. https://doi.org/10.35882/jeeemi.v7i2.629
Seeram, E. (2019). Computed Tomography: Physical Principles, Patient Care, and Clinical Applications. Philadelphia, PA, USA: Elsevier.
Ungania, S., Solivetti, F. M., D’Arienzo, M., Quagliani, F., Sperduti, I., Morrone, A., … Guerrisi, A. (2023). New-Generation ASiR-V for Dose Reduction While Maintaining Image Quality in CT: A Phantom Study. Applied Sciences, 13(9). https://doi.org/10.3390/app13095639
Xue, G., Liu, H., Cai, X., & Zhang, Z. (2023). Impact of deep learning image reconstruction algorithms on CT radiomic features in patients with liver tumors. Frontiers in Oncology, 13(April), 1–9. https://doi.org/10.3389/fonc.2023.1167745
Zhong, J., Wu, Z., Wang, L., Chen, Y., Xia, Y., Wang, L., … Yao, W. (2024). Impacts of Adaptive Statistical Iterative Reconstruction-V and Deep Learning Image Reconstruction Algorithms on Robustness of CT Radiomics Features: Opportunity for Minimizing Radiomics Variability Among Scans of Different Dose Levels. Journal of Imaging Informatics in Medicine, 37(1), 123–133. https://doi.org/10.1007/s10278-023-00901-1
Zhu, Z., Zhao, Y., Zhao, X., Wang, X., Yu, W., Hu, M., … Zhou, C. (2021). Impact of preset and postset adaptive statistical iterative reconstruction-V on image quality in nonenhanced abdominal-pelvic CT on wide-detector revolution CT. Quantitative Imaging in Medicine and Surgery, 11(1), 264–275. https://doi.org/10.21037/QIMS-19-945
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