TINJAUAN SISTEMATIS TEKNIK REDUKSI NOISE PADA CITRA CT SCAN THORAKS UNTUK PENINGKATAN KUALITAS DIAGNOSTIK

Penulis

  • Wahyudi Wahyudi Universitas Islam Negeri Alauddin Makassar
  • Jumardin Uinam Fakultas Sains dan Teknologi, Universitas Islam Negeri Alauddin Makassar

DOI:

https://doi.org/10.24252/sainfis.v5i2.63790

Kata Kunci:

Citra, CT Scan, Diagnostik, Rekonstruksi, Thoraks

Abstrak

Penggunaan protokol dosis rendah pada pemeriksaan CT semakin diutamakan untuk mengurangi paparan radiasi pada pasien. Namun, penurunan dosis ini menyebabkan peningkatan derau  yang berakibat pada turunnya kualitas citra dan berpotensi mengganggu ketepatan diagnosis. Untuk mengatasi masalah tersebut, berbagai teknik rekonstruksi citra telah dikembangkan, mulai dari rekonstruksi proyeksi balik terfilter, rekonstruksi iteratif, rekonstruksi iteratif hibrida, hingga rekonstruksi berbasis kecerdasan buatan. Penelitian ini menelaah lima jurnal yang membandingkan kinerja berbagai metode rekonstruksi berdasarkan dua parameter objektif, yaitu rasio sinyal terhadap derau (SNR) dan rasio kontras terhadap derau (CNR). Hasil telaah menunjukkan bahwa rekonstruksi proyeksi balik terfilter menghasilkan kualitas citra paling rendah karena tidak memiliki model pengendalian derau, sedangkan rekonstruksi iteratif mampu menekan derau namun sering menimbulkan citra yang terlalu halus sehingga detail anatomi berkurang. Sebaliknya, rekonstruksi berbasis kecerdasan buatan khususnya tingkat tinggi secara konsisten memberikan nilai SNR dan CNR tertinggi, mampu mempertahankan detail anatomi halus, serta menjaga ketajaman tepi meskipun dosis radiasi sangat diturunkan. Berdasarkan keseluruhan temuan, rekonstruksi berbasis kecerdasan buatan tingkat tinggi merupakan metode yang paling efektif dan paling direkomendasikan untuk meningkatkan kualitas citra CT dosis rendah tanpa mengurangi keandalan penilaian klinis.

Unduhan

Data unduhan belum tersedia.

Referensi

Abuya, T., Rimiru, R., & Okeyo, G. (2025). Adversarial Content–Noise Complementary Learning Model for Image Denoising and Tumor Detection in Low-Quality Medical Images. Signals, 6(2), 17. https://doi.org/10.3390/signals6020017

Beister, M., Kolditz, D., & Kalender, W. A. (2012). Iterative reconstruction methods in X-ray CT. Physica Medica, 28(2), 94–108. https://doi.org/10.1016/j.ejmp.2012.01.003

Cho, H.-H., Lee, S. M., & You, S. K. (2024). Assessment of deep learning image reconstruction (DLIR) on image quality in pediatric cardiac CT datasets type of manuscript: Original research. PLOS ONE, 19(8), e0300090. https://doi.org/10.1371/journal.pone.0300090

Hwang, M.-H., Kang, S., Lee, J. W., & Lee, G. (2024). Deep Learning-Based Reconstruction Algorithm With Lung Enhancement Filter for Chest CT: Effect on Image Quality and Ground Glass Nodule Sharpness. Korean Journal of Radiology, 25(9), 833. https://doi.org/10.3348/kjr.2024.0472

Jiang, J.-M., Miao, L., Liang, X., Liu, Z.-H., Zhang, L., & Li, M. (2022). The Value of Deep Learning Image Reconstruction in Improving the Quality of Low-Dose Chest CT Images. Diagnostics, 12(10), 2560. https://doi.org/10.3390/diagnostics12102560

Jung, Y., Hur, J., Han, K., Imai, Y., Hong, Y. J., Im, D. J., Lee, K. H., Desnoyers, M., Thomsen, B., Shigemasa, R., Um, K., & Jang, K. (2023a). Radiation dose reduction using deep learning-based image reconstruction for a low-dose chest computed tomography protocol: A phantom study. Quantitative Imaging in Medicine and Surgery, 13(3), 1937–1947. https://doi.org/10.21037/qims-22-618

Jung, Y., Hur, J., Han, K., Imai, Y., Hong, Y. J., Im, D. J., Lee, K. H., Desnoyers, M., Thomsen, B., Shigemasa, R., Um, K., & Jang, K. (2023b). Radiation dose reduction using deep learning-based image reconstruction for a low-dose chest computed tomography protocol: A phantom study. Quantitative Imaging in Medicine and Surgery, 13(3), 1937–1947. https://doi.org/10.21037/qims-22-618

Kim, W., Jeon, S.-Y., Byun, G., Yoo, H., & Choi, J.-H. (2024). A systematic review of deep learning-based denoising for low-dose computed tomography from a perceptual quality perspective. Biomedical Engineering Letters, 14(6), 1153–1173. https://doi.org/10.1007/s13534-024-00419-7

Koetzier, L. R., Mastrodicasa, D., Szczykutowicz, T. P., Van Der Werf, N. R., Wang, A. S., Sandfort, V., Van Der Molen, A. J., Fleischmann, D., & Willemink, M. J. (2023). Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects. Radiology, 306(3), e221257. https://doi.org/10.1148/radiol.221257

Lee, K. B., & Goo, H. W. (2018). Quantitative Image Quality and Histogram-Based Evaluations of an Iterative Reconstruction Algorithm at Low-to-Ultralow Radiation Dose Levels: A Phantom Study in Chest CT. Korean Journal of Radiology, 19(1), 119. https://doi.org/10.3348/kjr.2018.19.1.119

Otgonbaatar, C., Jeon, S.-H., Cha, S.-J., Shim, H., Kim, J. W., & Ahn, J.-H. (2025). Image quality and radiation dose of reduced-dose abdominopelvic computed tomography (CT) with silver filter and deep learning reconstruction. Scientific Reports, 15(1), 25757. https://doi.org/10.1038/s41598-025-11184-7

Sadia, R. T., Chen, J., & Zhang, J. (2024). CT image denoising methods for image quality improvement and radiation dose reduction. Journal of Applied Clinical Medical Physics, 25(2), e14270. https://doi.org/10.1002/acm2.14270

Shim, J., Lee, Y., & Kim, K. (2025a). Quantitative Evaluation of Low-Dose CT Image Quality Using Deep Learning Reconstruction: A Comparative Study of Philips Precise Image and GE TrueFidelity. Journal of Imaging, 11(9), 317. https://doi.org/10.3390/jimaging11090317

Shim, J., Lee, Y., & Kim, K. (2025b). Quantitative Evaluation of Low-Dose CT Image Quality Using Deep Learning Reconstruction: A Comparative Study of Philips Precise Image and GE TrueFidelity. Journal of Imaging, 11(9), 317. https://doi.org/10.3390/jimaging11090317

Suliman, I. I., Khouqeer, G. A., Ahmed, N. A., Abuzaid, M. M., & Sulieman, A. (2023). Low-Dose Chest CT Protocols for Imaging COVID-19 Pneumonia: Technique Parameters and Radiation Dose. Life, 13(4), 992. https://doi.org/10.3390/life13040992

Szczykutowicz, T. P., Toia, G. V., Dhanantwari, A., & Nett, B. (2022). A Review of Deep Learning CT Reconstruction: Concepts, Limitations, and Promise in Clinical Practice. Current Radiology Reports, 10(9), 101–115. https://doi.org/10.1007/s40134-022-00399-5

Unduhan

Diterbitkan

2025-09-30

Cara Mengutip

[1]
Wahyudi, W. dan Uinam, J. 2025. TINJAUAN SISTEMATIS TEKNIK REDUKSI NOISE PADA CITRA CT SCAN THORAKS UNTUK PENINGKATAN KUALITAS DIAGNOSTIK. SAINFIS: Jurnal Sains Fisika. 5, 2 (Sep 2025), 35–48. DOI:https://doi.org/10.24252/sainfis.v5i2.63790.

Terbitan

Bagian

Artikel