Simulations and experimental verification of medical X-ray sources: CT case
description
Transcript of Simulations and experimental verification of medical X-ray sources: CT case
1
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.
[email protected]@cbm.uo.edu.cu
BIOFISICA MEDICA
Workshop on Instruments and Sensors on the GRID
2
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
Optimisation Principle (ALARA)
Doses involved in medical XR applications must be As Low As Reasonably As possible with
the best image quality achievable.
5
Instruments and Sensors used in X-ray dosimetry
6
7
Instruments and Sensors used in X-ray (XR) dosimetry
8
Instruments and Sensors used in X-ray (XR) dosimetry
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
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.
11
Phantoms for Dosimetry
12
Monte Carlo Simulation Systems
13
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
15
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
nn
ing
pe
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
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
An Optimization Approach in CT (AMAR)
• Attributes of patient,
• Modulation of scanning factors,
• Advances in Technology,
• Required diagnostic image quality.
18
Attributes of Patient
0
1
2
-14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12cm
Do
sis
rela
tiva
Axial single 360 scanning
20
Advances in TechnologyCARE Dose 4D – SIEMENS (AMTC,z)
- User selects an Eff. mAs
21
Advances in Technology Dose Right (DOM) – PHILIPS
(MACT,z)
- Based on the squared root of obtained in previous anterior angular projection
22
Advances in Technology FlexmA – SHIMADZU (MACTz)
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
Advances in Technology Real E.C. – TOSHIBA (MACT,z)
The user selects a mA and quantum noise reference levels
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
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.