1. 1 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 BIOFISICA MEDICA Workshop on Instruments and Sensors on the GRID

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3. 3 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.

4. 4 Optimisation Principle (ALARA) Doses involved in medical XR applications must be As Low As Reasonably As possible with the best image quality achievable.

5. 5 Instruments and Sensors used in X-ray dosimetry

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7. 7 Instruments and Sensors used in X-ray (XR) dosimetry

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9. 9 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.

10. 10 Existing XR Simulation Approaches Monte Carlo Technique [1], [2], (following the path of each photon).[1][2] Deterministic, based on the integral photon transport equation.[3][3] Computer Aided Drawing -CAD- models.[4], [5][4][5] Segmentation Method (a pencil beam is segmented both in energy and solid angle).[6][6] [1][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][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][3] Inanc, F. ACT image based deterministic approach to dosimetry and radiography simulations. Phys. Med. Biol. 47, 3351–3368 (2002). [4][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][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][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.

11. 11 Phantoms for Dosimetry

12. 12 Monte Carlo Simulation Systems

13. 13 Simulation & Validation

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

15. 15 CT & World Population X 10 USA : 3.6x10 6 CT examinations in 1980 33 x10 6 CT examinations in 1998 33 x10 6 CT examinations in 1998 2.7x10 6 examinations in children younger than 15 years in 2000

16. 16 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.

17. 17 An Optimization Approach in CT (AMAR) Attributes of patient, Modulation of scanning factors, Advances in Technology, Required diagnostic image quality.

18. 18 Attributes of Patient Axial single 360  scanning

19. 19 Modulation of Scanning Factors Patient Dose Noise Spatial Resolution Detail kV mAs Slice Thickness Matrix Size Kernel Algorithms Field of View Focus Size Tube heating Contrast Windowing Rotation Time Geometry Scanning Mode Scanning Long. Table Speed Pitch Shielding Prescription AMTC*

20. 20 Advances in Technology CARE Dose 4D – SIEMENS (AMTC ,z )  - User selects an Eff. mAs

21. 21 Advances in Technology Dose Right (DOM) – PHILIPS (MACT ,z )  - Based on the squared root of  obtained in previous anterior angular projection

22. 22 Advances in Technology FlexmA – SHIMADZU (MACT z )

23. 23 Advances in Technology 3D 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.

24. 24 Advances in Technology Real E.C. – TOSHIBA (MACT ,z ) The user selects a mA and quantum noise reference levels

25. 25 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.

26. 26 CT low dose protocols

27. 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.