SavedAdd your journal

Journals / International Journal of 3D Printing Technologies and Digital Industry / 2026 / Cilt: 10 Sayı: 2

OPTIMIZATION OF TOMOGRAPHIC IMAGE QUALITY USING AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS)

Ebuzer Aygül(HAKKARİ ÜNİVERSİTESİ, SAĞLIK HİZMETLERİ MESLEK YÜKSEKOKULU, TIBBİ HİZMETLER VE TEKNİKLER BÖLÜMÜ)0000-0003-0930-3975
Pages
332–339
Publication Date
2026-08-31
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This advancement holds substantial significance for contemporary diagnostic clinical applications.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Computed tomography (CT) is an advanced imaging modality widely employed in clinical practice for diagnostic and therapeutic planning purposes.
📊
Key Findings & Data:However, due to its reliance on ionizing radiation, CT imaging may increase the long-term risk of malignancy, particularly among vulnerable populations such as pediatric patients, pregnant women, and individuals with chronic illnesses.
💡
Conclusion & Impact:This advancement holds substantial significance for contemporary diagnostic clinical applications.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:ANFIS · Optimization · Rules

Abstract

Computed tomography (CT) is an advanced imaging modality widely employed in clinical practice for diagnostic and therapeutic planning purposes. However, due to its reliance on ionizing radiation, CT imaging may increase the long-term risk of malignancy, particularly among vulnerable populations such as pediatric patients, pregnant women, and individuals with chronic illnesses. Consequently, minimizing radiation exposure constitutes a fundamental principle in CT imaging and other radiation-based diagnostic applications. Nevertheless, reducing radiation dose often results in degraded image quality, thereby complicating disease detection and adversely affecting diagnostic accuracy in clinical practice. Although conventional filtering techniques are commonly utilized to enhance image quality, the associated loss of fine structural details highlights the need for more robust and efficient approaches. In this context, an Adaptive Neuro-Fuzzy Inference System (ANFIS) unit offers a promising solution by leveraging learning-based algorithms to suppress visual artifacts while preserving critical image information. Moreover, advanced implementations of such methodologies have the potential to reconstruct higher-quality images from low-dose acquisitions, thereby enabling reduced radiation exposure without compromising diagnostic performance. This advancement holds substantial significance for contemporary diagnostic clinical applications.

Özet

Computed tomography (CT) is an advanced imaging modality widely employed in clinical practice for diagnostic and therapeutic planning purposes. However, due to its reliance on ionizing radiation, CT imaging may increase the long-term risk of malignancy, particularly among vulnerable populations such as pediatric patients, pregnant women, and individuals with chronic illnesses. Consequently, minimizing radiation exposure constitutes a fundamental principle in CT imaging and other radiation-based diagnostic applications. Nevertheless, reducing radiation dose often results in degraded image quality, thereby complicating disease detection and adversely affecting diagnostic accuracy in clinical practice. Although conventional filtering techniques are commonly utilized to enhance image quality, the associated loss of fine structural details highlights the need for more robust and efficient approaches. In this context, an Adaptive Neuro-Fuzzy Inference System (ANFIS) unit offers a promising solution by leveraging learning-based algorithms to suppress visual artifacts while preserving critical image information. Moreover, advanced implementations of such methodologies have the potential to reconstruct higher-quality images from low-dose acquisitions, thereby enabling reduced radiation exposure without compromising diagnostic performance. This advancement holds substantial significance for contemporary diagnostic clinical applications.

🔖

Cite This Article

Aygül, E. (2026). OPTIMIZATION OF TOMOGRAPHIC IMAGE QUALITY USING AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS). International Journal of 3D Printing Technologies and Digital Industry, Cilt: 10 Sayı: 2, 332–339. https://doi.org/10.46519/ij3dptdi.1899111

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (29)

  1. Goldman, L.W., “Principles of CT and CT technology”, Journal of Nuclear Medicine Technology, Vol. 35, Issue 3, Pages 115-128, 2007.
  2. Patel, P.R. and De Jesus, O., “CT scan”, StatPearls, StatPearls Publishing, 2023.
  3. Ma, X., et al., “X-ray attenuation of bone, soft and adipose tissue in CT from 70 to 140 kV and comparison with 3D printable additive manufacturing materials”, Scientific Reports, Vol. 12, Issue 1, 14580, 2022.
  4. Hermena, S. and Young, M., “CT-scan image production procedures”, StatPearls, StatPearls Publishing, 2023.
  5. Lftta, J.G.J., et al., “X-rays and their uses on the human body”, Current Clinical and Medical Education, Vol. 2, Issue 08, Pages 91-115, 2024.
  6. Bosch de Basea, M., et al., “Risk of hematological malignancies from CT radiation exposure in children, adolescents and young adults”, Nature Medicine, Vol. 29, Issue 12, Pages 3111-3119, 2023.
  7. Talapko, J., et al., “Health effects of ionizing radiation on the human body”, Medicina, Vol. 60, Issue 4, 653, 2024.
  8. Bos, D., et al., “Radiation exposure in computed tomography”, Deutsches Ärzteblatt International, Vol. 120, Issue 9, 135, 2023.
  9. Hussain, S., et al., “Modern diagnostic imaging technique applications and risk factors in the medical field: a review”, BioMed Research International, Vol. 2022, Issue 1, 5164970, 2022.
  10. Sadia, R.T., Chen, J. and Zhang, J., “CT image denoising methods for image quality improvement and radiation dose reduction”, Journal of Applied Clinical Medical Physics, Vol. 25, Issue 2, e14270, 2024.
  11. Rusanov, B., et al., “Deep learning methods for enhancing cone‐beam CT image quality toward adaptive radiation therapy: a systematic review”, Medical Physics, Vol. 49, Issue 9, Pages 6019-6054, 2022.
  12. Lavrova, E., et al., “Adaptive radiation therapy: a review of CT-based techniques”, Radiology: Imaging Cancer, Vol. 5, Issue 4, e230011, 2023.
  13. Feldle, P., et al., “Influence of helical pitch and gantry rotation time on image quality and file size in ultrahigh-resolution photon-counting detector CT”, Scientific Reports, Vol. 14, Issue 1, 9358, 2024.
  14. Ma, Y., et al., “Feasibility study of using low-kilovoltage, prospective gating, high-pitch, dual-source computed tomography prior to transcatheter aortic valve replacement: analysis of image quality and radiation dose”, Journal of Thoracic Disease, Vol. 15, Issue 12, 6848, 2023.
  15. Olatunji, O.M., et al., “Application of hybrid ANFIS-based non-linear regression modeling to predict the % oil yield from grape peels: effect of process parameters and FIS generation techniques”, Cleaner Engineering and Technology, Vol. 6, 100371, 2022.
  16. Rashed, B.M. and Popescu, N., “Medical image-based diagnosis using a hybrid adaptive neuro-fuzzy inference system (ANFIS) optimized by GA with a deep network model for features extraction”, Mathematics, Vol. 12, Issue 5, 633, 2024.
  17. Fan, C., et al., “Image quality assessment of the CARE kV for pediatric CT angiography: a feasibility study of over 50% effective dose reduction”, Journal of Radiation Research and Applied Sciences, Vol. 18, Issue 2, 101386, 2025.
  18. Ma, R., et al., “Evaluation of pericoronary adipose tissue attenuation on CT”, The British Journal of Radiology, Vol. 96, Issue 1145, 20220885, 2023.
  19. Najefi, A.-A., et al., “Assessing the rotation of the first metatarsal on computed tomography scans: a systematic literature review”, Foot & Ankle International, Vol. 43, Issue 1, Pages 66-76, 2022.
  20. Horvatinec, D., Ciprić, D. and Mihanović, F., “Impact of Pitch Factor on CT Examination Quality”, Radiološki Vjesnik: Radiologija, Radioterapija, Nuklearna Medicina, Vol. 49, Issue 2, Pages 17-22, 2025.
  21. Alberts, I., et al., “Long-axial field-of-view PET/CT: perspectives and review of a revolutionary development in nuclear medicine based on clinical experience in over 7000 patients”, Cancer Imaging, Vol. 23, Issue 1, 28, 2023.
  22. Dayarathna, S., et al., “Deep learning-based synthesis of MRI, CT and PET: review and analysis”, Medical Image Analysis, Vol. 92, 103046, 2024.
  23. King, L.R., “Novel metrics for assessment of image quality for non-linear computed tomography reconstruction algorithms”, PhD Thesis, The University of Manchester, United Kingdom, 2022.
  24. Adnan, R.M., et al., “Development of new machine learning model for streamflow prediction: case studies in Pakistan”, Stochastic Environmental Research and Risk Assessment, Vol. 36, Issue 4, Pages 999-1033, 2022.
  25. Hsieh, J., “Computed Tomography: Principles, Design, Artifacts, and Recent Advances”, SPIE Press, Bellingham, Washington, 2nd Edition, 2003.
  26. Krupinski, E.A., et al., “Digital radiography image quality: image processing and display”, Journal of the American College of Radiology, Vol. 4, Issue 6, Pages 389-400, 2007.
  27. Jang, J.-S. R., “ANFIS: adaptive-network-based fuzzy inference system”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, Issue 3, Pages 665-685, 1993.
  28. Zhou, Z., et al., “Low kilovoltage peak (kVp) with an adaptive statistical iterative reconstruction algorithm in computed tomography urography: evaluation of image quality and radiation dose”, American Journal of Translational Research, Vol. 8, Issue 9, Pages 3883-3891, 2016.
  29. Draper, N. R. and Smith, H., “Applied Regression Analysis”, John Wiley & Sons, 3rd Edition, 1998. 30.