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Simulations and experimental verification of medical X-ray sources: CT case

BIOFISICA MEDICA. Workshop on Instruments and Sensors on the GRID. Simulations and experimental verification of medical X-ray sources: CT case. R. A. Miller C. Department of Biophysics, Medical Biophysics Centre University of Orient. Santiago of Cuba. ramillerc@cbm.uo.edu.cu. Background.

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Simulations and experimental verification of medical X-ray sources: CT case

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  1. BIOFISICA MEDICA Workshop on Instruments and Sensors on the GRID Simulations and experimental verification of medical X-ray sources: CT case R. A. Miller C.Department of Biophysics, Medical Biophysics CentreUniversity of Orient. Santiago of Cuba.ramillerc@cbm.uo.edu.cu

  2. Background X-ray devices are important tools in various medical applications. However, the x-rays produced by such devices can pose a hazard to human health depending on radiation absorbed dose in tissue (ADT). For this reason, ADT estimation constitutes a key aspect in the use of medical x-ray sources.

  3. Optimisation Principle (ALARA) Doses involved in medical XR applications must be As Low As Reasonably As possible with the best image quality achievable.

  4. Instruments and Sensors used in X-ray dosimetry

  5. Instruments and Sensors used in X-ray (XR) dosimetry

  6. Instruments and Sensors used in X-ray (XR) dosimetry

  7. Due to impossibility of detectors positioning in most internal anatomical structures where doses need to be known, absorbed radiation doses are estimated by several Simulation Approaches.

  8. Existing XR Simulation Approaches • Monte Carlo Technique[1], [2], (following the path of each photon). • Deterministic, based on the integral photon transport equation.[3] • Computer Aided Drawing -CAD- models.[4], [5] • Segmentation Method (a pencil beam is segmented both in energy and solid angle).[6] [1] Lazos, D., Bliznakova, K., Kolitsi, Z. And Pallikarakis, N. An integrated research tool for X-ray imaging simulation. Comp. Meth. Prog. Biomed. 70, 241–251 (2003). [2] Winslow, M., Xu, X. G., Huda, W., Ogden, K. M. And Scalzetti, E. M. Monte Carlo simulations of patient X-ray images. Am. Nucl. Soc. Trans. 90, 459–460 (2004). [3] Inanc, F. ACT image based deterministic approach to dosimetry and radiography simulations. Phys. Med. Biol. 47, 3351–3368 (2002). [4] Duvauchelle, P., Freud, N., Kaftandjian, V. And Babot, D. A computer code to simulate X-ray imaging techniques. Nucl. Instrum. Methods Phys. Res. B 170, 245–258 (2000). [5] Ahn, S. K., Cho, G., Chi, Y. K., Kim, H. K. And Jae, M. A computer code for the simulation of X-ray imaging systems. In: Proceedings of the IEEE Nuclear Science Symposium. Conference Record, Oregon, USA, 19–25 October 2003 (Piscataway, NJ: IEEE) pp. 838–842 (2004). [6] Fanti V., Marzeddu R., Massazza G., Randaccio P., Brunetti A. and Golosio B. A SIMULATOR FOR X-RAY IMAGES. Radiation Protection Dosimetry (2005), Vol. 114, Nos 1-3, pp. 350–354.

  9. Phantoms for Dosimetry

  10. Monte Carlo Simulation Systems

  11. Simulation & Validation

  12. Why CT? USA CT contribution to Effective Dose with respect to every XR imaging Percentage CT examinations vs. total X rays imaging WORLD SCENARIO Percentage CT examinations vs. total Radiological examinations CT contribution to World’s Collective Effective Dose

  13. CT & World Population 2.7x106 examinations in children younger than 15 years in 2000 USA : 3.6x106 CT examinations in 1980 33 x106 CT examinations in 1998 X 10

  14. But… • Whereas CT contributes to higher values of Effective Dose, they are under the threshold for deterministic or stochastic effects, in which genetic effects depends on absorbed dose. • Cancer risk by abdominal CT scannings: 12,5/10 000.

  15. An Optimization Approach in CT (AMAR) • Attributes of patient, • Modulation of scanning factors, • Advances in Technology, • Required diagnostic image quality.

  16. Attributes of Patient Axial single 360 scanning

  17. Advances in TechnologyCARE Dose 4D – SIEMENS (AMTC,z)  - User selects an Eff. mAs

  18. Advances in TechnologyDose Right (DOM) – PHILIPS (MACT,z)  - Based on the squared root of obtained in previous anterior angular projection

  19. Advances in Technology FlexmA – SHIMADZU (MACTz)

  20. Advances in Technology3D Auto mA – General Electric MS (MACT,z) Z- Modulates mA to keep a user specified quantum noise. A pitch correction factor is used in helical mode. Uses the standard kernel as a reference.

  21. Advances in TechnologyReal E.C. – TOSHIBA (MACT,z) The user selects a mA and quantum noise reference levels

  22. Required diagnostic image quality • High Signal to Noise Ratio: • Solid Lung Tumours (except ground glass tumours). • Calcifications in Coronary Arteries. • Lung emphysema. • Low Signal to Noise Ratio: • Abdominal scannings (liver or kidney). • Diffuse Lung Illness. • Medium Signal to Noise Ratio: • Brain. • Abdominal / Thoracic (except for bleeding). • Lung illness.

  23. CT low dose protocols

  24. Challenges for XR sources Simulations and Validation • Personalized organ dose estimation and protocol optimization. • Acceptable clinical image quality threshold identification to optimize dose. • Initial mA user selection in some AMTC introduces subjective restrictions La (e.g. high mAs for big patients). • Simultaneous Modulation of kV and mAs.

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