Analysis of the Effect of Varying Slice Thickness on Image Quality in CT Scans of the Brain With Clinical Infarction at Siti Rahmah Islamic Hospital in Padang
DOI:
https://doi.org/10.65739/archipel.v1i10.65Keywords:
CT-Scan, Slice Thickness, Image Quality, Cerebral Infarction, CNR, SNRAbstract
An Analysis Of The Effect Of Slice Thickness Variation On Image Quality In Ct-Scan Examination Of The Brain With Clinical Infarction At Siti Rahmah Islamic Hospital In Padang. Infarction stroke occurs when blood flow in the brain is disrupted, causing damage to brain tissue. The appropriate slice thickness increases picture quality and allows for more accurate detection of infarction stroke, reducing the possibility of error. The purpose of this study is to investigate the influence of slice thickness variation on image quality in a brain CT-scan examination with clinical infarction performed at RSI Siti Rahmah Padang. This study was carried out at the RSI Siti Rahmah Padang radiology installation in May-June 2024 utilizing an experimental quantitative research design and a purposive sample technique. Five CT-Scan infarction pictures were taken with a slice thickness of 5 mm and reconstructed to 2 mm and 3 mm. The reconstruction result is then in the ROI on the radiant dicom, then the CNR and SNR calculations are carried out and analysed using SPSS with the Manova test. The result of the slice thickness variation value towards CNR and SNR that has been processed using the Manova test obtained R square CNR 0.839 and SNR 0.797 with a significance level of p of 0.000<0.05, then it was concluded that the slice thickness variation had an effect on the CNR and SNR values. The highest mean value that is optimally meaningful is obtained at a slice thickness of 5 mm, so it can be concluded that the size of the slice thickness is directly proportional to the increase in CNR and SNR values.
Downloads
References
Abdulkareem, N., Hajee, S., Hassan, F., Ibrahim, I. K., Al-Khalidi, R. E. H., & Abdulqader, N. A. (2023). Investigating the slice thickness effect on noise and diagnostic content of single-source multi-slice computerized axial tomography. Journal of Medicine and Life, 16, 862–867. https://doi.org/10.25122/jml-2022-0188
Anggarani, N. K. N., Suryatika, I. B. M., & Sutapa, G. (2023). Effect of slice thickness variation on spatial resolution of CT-scan device images at the Bali Mandara Hospital Radiology Installation. Asian Journal of Medicine and Health. https://doi.org/10.9734/ajmah/2023/v21i10901
Baert, A. (2018). Multislice CT (4th ed.). Springer Heidelberg Dordrecht.
Choopani, M., Abedi, I., & Dalvand, F. (2022). Pragmatic approaches to reducing radiation dose in brain computed tomography scan using scan parameter modification. Journal of Medical Signals and Sensors, 12, 219–226. https://doi.org/10.4103/jmss.jmss_83_20
Dewilza, N., Yudha, S., Hanifah, W., Nansih, L. A., & Septiani, R. (2024). Analysis of differences in image quality with variations in slice thickness in CT-scan brain examinations with trauma cases at the Radiology Installation of RSI Siti Rahmah Padang. Jurnal Ilmu dan Teknologi Kesehatan. https://doi.org/10.33666/jitk.v15i2.643
Funama, Y., Shirasaka, T., Goto, T., Aoki, Y., Tanaka, K., & Yoshida, R. (2022). Iterative reconstruction with multifrequency signal recognition technology to improve low-contrast detectability: A phantom study. Acta Radiologica Open, 11. https://doi.org/10.1177/20584601221109919
Harkey, T., Baker, D., Hagen, J., Scott, H., & Palys, V. (2021). Practical methods for segmentation and calculation of brain volume and intracranial volume: A guide and comparison. Quantitative Imaging in Medicine and Surgery, 12(7), 3748–3761. https://doi.org/10.21037/qims-21-958
Hirairi, T., Ichikawa, K., Urikura, A., Kawashima, H., Tabata, T., & Matsunami, T. (2023). Improvement of diagnostic performance of hyperacute ischemic stroke in head CT using an image-based noise reduction technique with non-black-boxed process. Physica Medica, 112, 102646. https://doi.org/10.1016/j.ejmp.2023.102646
Hutami, I. A. P. A., Sutapa, G., & Paramarta, I. (2021). Analisis pengaruh slice thickness terhadap kualitas citra pesawat CT scan di RSUD Bali Mandara. Buletin Fisika, 22(2), 77–83.
Inoue, Y., Itoh, H., Hata, H., Miyatake, H., Mitsui, K., Uehara, S., & Masuda, C. (2024). Noise reduction in brain CT: A comparative study of deep learning and hybrid iterative reconstruction using multiple parameters. Tomography, 10, 2073–2086. https://doi.org/10.3390/tomography10120147
Karina. (2017). Analisis pengaruh variasi slice thickness terhadap kualitas citra CT scan. [Thesis, Indonesia].
Maghfirah, A., Rahim, A., & Fadhlin, A. (2024). Linearity test of CT number and spatial resolution using ACR 464 phantom for image quality assessment on a 16-slice CT scanner. Journal of Technomaterial Physics. https://doi.org/10.32734/jotp.v6i2.12410
Mitani, H., Tatsugami, F., Higaki, T., Kaichi, Y., Nakamura, Y., Smit, E., Prokop, M., Ono, C., Ono, K., Korogi, Y., & Awai, K. (2021). Accuracy of thin-slice model-based iterative reconstruction designed for brain CT to diagnose acute ischemic stroke in the middle cerebral artery territory: A multicenter study. Neuroradiology, 63, 2013–2021. https://doi.org/10.1007/s00234-021-02745-4
Nelson, B., Gomez-Cardona, D., Thorne, J., Huber, N., Yu, L., Leng, S., McCollough, C., & Missert, A. (2024). Multiple kernel synthesis of head CT using a task-based loss function. Journal of Imaging Informatics in Medicine, 37, 864–872. https://doi.org/10.1007/s10278-023-00959-x
Notoatmojo, S. (2018). Metodologi penelitian kesehatan. Rineka Cipta.
Peng, Y., Luo, C., Wang, H., Sun, K., Lin, F., Wang, J., Rao, Y., Fan, R., Gong, L., & Sun, X. (2024). Feasibility of CT attenuation values in distinguishing acute ischemic stroke, old cerebral infarction and leukoaraiosis. BMC Medical Imaging. https://doi.org/10.1186/s12880-024-01340-2
Putu, L., Kusumaningsih, R., Suryatika, I. B. M., Trisnawati, N. L. P., & Irhas, R. (2023). Pengaruh slice thickness terhadap signal to noise ratio (SNR) dari hasil penyinaran CT Scan di RSUP Prof. Dr. I.G.N.G Ngoerah. Kappa Journal. https://doi.org/10.29408/kpj.v7i2.7675
Rajendran, K., Petersilka, M., Henning, A., Shanblatt, E., Marsh, J., Thorne, J., Schmidt, B., Flohr, T., Fletcher, J., McCollough, C., & Leng, S. (2021). Full field-of-view, high-resolution, photon-counting detector CT: Technical assessment and initial patient experience. Physics in Medicine and Biology, 66. https://doi.org/10.1088/1361-6560/ac155e
Rinaldi, S. F., & Mujianto, B. (2017). Metodologi penelitian dan statistik. Trans Info Media.
Sato, T., & Takagi, T. (2021). Validation of the influence of CT slice thickness on the quantitative accuracy and image quality of single photon emission computed tomography. Asia Oceania Journal of Nuclear Medicine and Biology, 9(2), 148–157. https://doi.org/10.22038/aojnmb.2021.54670.1376
Seeram, E. (2016). Computed tomography: Physical principles, clinical applications, and quality control (4th ed.). Elsevier.
Siregar, E. S. (2020). Analisis dosis radiasi pasien pada pemeriksaan CT scan menggunakan aplikasi Si-INTAN. Buletin Fisika, 21(2), 53–59.
Solong, D. I., Putu, N., Jeniyanthi, R., & Sukadana, I. (2023). Analisis pengaruh variasi slice thickness terhadap kualitas citra pada pemeriksaan CT scan kepala dengan kasus stroke hemorragic di RS TK II Pelamonia Makassar. Journal of Educational Innovation and Public Health. https://doi.org/10.55606/innovation.v1i4.1992
Sun, J., Li, H., Wang, B., Li, J., Li, M., Zhou, Z., & Peng, Y. (2021). Application of a deep learning image reconstruction (DLIR) algorithm in head CT imaging for children to improve image quality and lesion detection. BMC Medical Imaging, 21. https://doi.org/10.1186/s12880-021-00637-w
Sun, J., Yao, H., Han, T., Wang, Y., Yang, L., Hao, X., & Wu, S. (2025). Thin-slice brain CT image quality and lesion detection evaluation in deep learning reconstruction algorithm. Clinical Neuroradiology, 35, 817–825. https://doi.org/10.1007/s00062-025-01542-3
Vromans, J., Bennink, E., Dankbaar, J., Velthuis, B., & De Jong, H. W. A. M. (2026). Robust filtering of thin-slice reconstructions improves lacunar stroke detection in CT perfusion imaging. Medical Physics, 53. https://doi.org/10.1002/mp.70271.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Wahdini Hanifah, Nerifa Dewilza, Sagita Yudha , Livia Ade Nansih, Santa Mareta, Rina Astuti
This work is licensed under a Creative Commons Attribution 4.0 International License.
