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

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

R. A. Miller C.R. A. Miller C.Department of Biophysics, Medical Biophysics CentreDepartment of Biophysics, Medical Biophysics Centre

University of Orient. Santiago of Cuba.University of Orient. Santiago of Cuba.

ramillerc@cbm.uo.edu.curamillerc@cbm.uo.edu.cu

BIOFISICA MEDICA

Workshop on Instruments and Sensors on the GRID

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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.

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Optimisation Principle (ALARA)

Doses involved in medical XR applications must be As Low As Reasonably As possible with

the best image quality achievable.

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Instruments and Sensors used in X-ray dosimetry

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

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

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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.

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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-rayimaging 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.

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Phantoms for Dosimetry

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Monte Carlo Simulation Systems

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Simulation & Validation

Why CT?

20%

40%

-5%

5%

15%

25%

35%

45%

1990 1999

CT Effective Dose Contribution to Colective Effective Dose (United Kingdom)

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

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CT & World Population

X 10

USA : 3.6x10USA : 3.6x106 6 CT examinations in 1980CT examinations in 1980

33 x1033 x1066 CT examinations in 1998 CT examinations in 1998

2.7x106 examinations in children younger than 15 years in 2000

CT examinations - Annual Rate in Developed Countries (1985 - 1990)

14.5

3035

50

97

0

20

40

60

80

100

120

USA Australia Germany Belgium Japan

Av

era

ge

an

nu

al r

ate

of

CT

s

ca

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r 1

,00

0 p

eo

ple

6.1

44

0

10

20

30

40

50

1970 - 1979 1985 - 1990

Annual Global Rate of CT examinations per 1000 people

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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.

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An Optimization Approach in CT (AMAR)

• Attributes of patient,

• Modulation of scanning factors,

• Advances in Technology,

• Required diagnostic image quality.

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Attributes of Patient

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1

2

-14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12cm

Do

sis

rela

tiva

Axial single 360 scanning

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Advances in TechnologyCARE Dose 4D – SIEMENS (AMTC,z)

- User selects an Eff. mAs

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Advances in Technology Dose Right (DOM) – PHILIPS

(MACT,z)

- Based on the squared root of obtained in previous anterior angular projection

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Advances in Technology FlexmA – SHIMADZU (MACTz)

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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.

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Advances in Technology Real E.C. – TOSHIBA (MACT,z)

The user selects a mA and quantum noise reference levels

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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.

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CT low dose protocols

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.