Cone-beam spiral computed tomography

Cone-beam spiral computed tomography (CT) is a medical imaging technology that has impacted healthcare since its development in the early 1990s. ^{[1] [2]} This technology offers advancements over traditional fan-beam CT, including faster scanning speed, higher image quality, and the ability to generate true three-dimensional volumes, even with contrast-enhancement. It is estimated that the majority of the approximately 300 million CT scans performed annually worldwide use spiral cone-beam technology.

History

The concept of cone-beam spiral CT was first proposed by Ge Wang in 1991, ^[3] who also introduced algorithms for approximate image reconstruction. A number of researchers and companies have contributed to the development of this technology. ^[4]

In 2002, Alexander Katsevich formulated the first theoretically exact cone-beam spiral CT algorithm. ^{[5] [6]} The work on cone-beam spiral CT has become a foundational aspect of modern medical imaging, allowing for accurate volumetric image reconstruction from truncated x-ray cone-beam projections. ^[7]

Principles

Cone-beam spiral CT uses an X-ray source and multiple detector rows that rotate spirally around the patient. The cone-shaped X-ray beam captures a large volume of data in a single pass, enabling the reconstruction of high-resolution volumetric and dynamic images. Key steps in the cone-beam spiral CT scanning process include:

• Cone-Beam Projection: Unlike fan-beam CT, which uses a single detector row, cone-beam CT employs multiple detector rows, sometimes numbering in the hundreds, to capture a wider field of view.
• Spiral Scanning: The CT system performs both the rotation of the X-ray data acquisition system and the translation of the patient on a motorized table simultaneously. This creates a spiral or helical trajectory, resulting in continuous data acquisition within a short scan time.
• Image Reconstruction: Advanced algorithms such as Wang's generalized Feldkamp-Davis-Kress (FDK) algorithms and Katsevich-type formulas are used to reconstruct images from cone-beam projection data. ^[8]

Applications

Cone-beam spiral CT is employed in various medical imaging tasks, including:

• Lung Cancer Screening: It plays a crucial role in early detection and monitoring of lung cancer. ^[9]
• Oncology: Cone-beam CT is used to characterize tumors, plan radiation therapy, and assess treatment responses. ^[10]
• Cardiology: It is useful for evaluating coronary artery disease, planning interventions, and monitoring disease progression. ^[11]
• Orthopedics: The technology is effective in imaging complex bone structures and assisting in surgical planning. ^[12]
• Trauma Imaging: Cone-beam CT provides rapid assessment of traumatic injuries, particularly in emergency settings. ^[13]
• Interventional Radiology: It guides various minimally invasive procedures with high precision. ^[14]

References

1. Defrise, M., Noo, F., Kudo, H. "Physics in Medicine and Biology," 45(3):623-643, 2000.
2. La Riviere, P.J., Crawford, C.R. "Journal of Medical Imaging," 8(5): 052111-1-12, 2021.
3. "Proposed next generation nano-computed tomography system will enhance nanoscale research". Virginia Tech. Retrieved 15 September 2024.
4. Wang, G.; Lin, T.-H.; Cheng, P.; Shinozaki, D.M. (1993). "A general cone-beam reconstruction algorithm". IEEE Transactions on Medical Imaging. 12 (3): 486–496. doi:10.1109/42.241876. PMID   18218441.
5. A. Katsevich. Theoretically exact filtered backprojection-type inversion algorithm for Spiral CT. SIAM J. Appl. Math., 62 (2002), pp. 2012-2026
6. Anderson, Julia (13 October 2016). "- A Prize From a King". College of Sciences News.
7. "Medical Imaging in Increasing Dimensions". American Scientist. 10 August 2023.
8. Lv Y, Katsevich A, Zhao J, Yu H, Wang G: Fast exact/quasi-exact FBP algorithms for triple-source helical cone-beam CT. IEEE Trans. Medical Imaging 29:756-770, 2010
9. Verhoeven, Roel L. J.; Kops, Stephan E. P.; Wijma, Inge N.; ter Woerds, Desi K. M.; van der Heijden, Erik H. F. M. (30 August 2023). "Cone-beam CT in lung biopsy: a clinical practice review on lessons learned and future perspectives". Annals of Translational Medicine. 11 (10): 361. doi: 10.21037/atm-22-2845 . ISSN   2305-5839. PMC   10477635 . PMID   37675336.
10. Bapst, Blanche; Lagadec, Matthieu; Breguet, Romain; Vilgrain, Valérie; Ronot, Maxime (January 2016). "Cone Beam Computed Tomography (CBCT) in the Field of Interventional Oncology of the Liver". CardioVascular and Interventional Radiology. 39 (1): 8–20. doi:10.1007/s00270-015-1180-6. ISSN   1432-086X. PMID   26178776.
11. Hu, H., Duerinckx, A. J., Foley, W. D., & Cooper, C. (2000). Helical/spiral CT in cardiovascular disease. Journal of Thoracic Imaging, 15(4), 290-305. doi:10.1097/00005382-200010000-00004
12. Hutchison, Chad. "5 Advantages of Using CBCT (Cone Beam CT) in Orthopedics". mavenimaging.com. Retrieved 14 September 2024.
13. Wang, X., Wu, Z., & Liu, Y. (2019). The clinical application of cone-beam computed tomography in emergency trauma. Journal of Clinical Medicine Research, 11(7), 484-491. doi:10.14740/jocmr3844
14. Key, Brandon M.; Tutton, Sean M.; Scheidt, Matthew J. (July 2023). "Cone-Beam CT With Enhanced Needle Guidance and Augmented Fluoroscopy Overlay: Applications in Interventional Radiology" . American Journal of Roentgenology. 221 (1): 92–101. doi:10.2214/AJR.22.28712. ISSN   0361-803X. PMID   37095661.