1. 1/12 Optimising X-ray computer tomography images with a CT-simulator Philippe Van Marcke K.U.Leuven

2. Introduction Development of the simulator Conclusions Example of image optimisation Introduction Development of the simulator Conclusions Example of image optimisation Introduction CT-simulator:  useful tool for optimising image quality by trial and error computer program that immitates the working of a CT-scanner makes it possible to manipulate all parameters involved in the scanning process and investigate their effect generating a lot of measurements in short time

3. Introduction Development of the simulator Conclusions Example of image optimisation Defining objects Development of the simulator Conclusions Example of image optimisation Development of the simulator Defining objects in the simulator: superposing object parts µ = µ foreground - µ background different resolution for different parts 1 2

4. Introduction Development of the simulator Conclusions Example of image optimisation Defining objects Development of the simulator Conclusions Example of image optimisation Development of the simulator Defining objects in the simulator: superposing object parts µ = µ foreground - µ background different resolution for different parts 1 2 i

5. Introduction Development of the simulator Conclusions Example of image optimisation superposing object parts µ p1 = µ 1 intensity of a monochromatic beam i passed through object part 1: Defining objects Development of the simulator Conclusions Example of image optimisation Development of the simulator Defining objects in the simulator: 1 i

6. Introduction Development of the simulator Conclusions Example of image optimisation superposing object parts µ p2 = µ 2 - µ 1 intensity of a monochromatic beam i passed through object part 2: Defining objects Development of the simulator Conclusions Example of image optimisation Development of the simulator Defining objects in the simulator: 2 i

7. Introduction Development of the simulator Conclusions Example of image optimisation Simulator formula Development of the simulator Conclusions Example of image optimisation Development of the simulator Intensity of a monochromatic beam i passed through an object consisting of P object parts:  the simulator repeats this calculation a large number of times

8. Introduction Development of the simulator Conclusions Example of image optimisation Sampling the spectrum Development of the simulator Conclusions Example of image optimisation Development of the simulator Monochromatic beams are grouped into polychromatic beams Spectrum is divided into K regions with an equal area

9. Introduction Development of the simulator Conclusions Example of image optimisation Sampling the spectrum Development of the simulator Conclusions Example of image optimisation Development of the simulator Monochromatic beams are grouped into polychromatic beams The formula for the monochromatic beam i:

10. Introduction Development of the simulator Conclusions Example of image optimisation Sampling the spectrum Development of the simulator Conclusions Example of image optimisation Development of the simulator Monochromatic beams are grouped into polychromatic beams is extended to a polychromatic beam i: The formula for the monochromatic beam i:

11. Introduction Development of the simulator Conclusions Example of image optimisation Sampling source and detector Development of the simulator Conclusions Example of image optimisation Development of the simulator Finite sizes of the source and detector elements are modelled by sampling both parts source is modelled by S source samples detector elements are modelled by D detector samples

12. Introduction Development of the simulator Conclusions Example of image optimisation Sampling source and detector Development of the simulator Conclusions Example of image optimisation Development of the simulator Finite sizes of the source and detector elements are modelled by sampling both parts The formula for the polychromatic beam i is repeated for all source and detector samples:  noise Implementing additional features:  using hardware filters

13. Introduction Development of the simulator Conclusions Example of image optimisation Implementing noise and filters Development of the simulator Conclusions Example of image optimisation Development of the simulator  Noise:  Filtration of the X-ray spectrum: f R number of frames used µ i attenuation coefficient of filter i t i thickness of filter i

14. Introduction Development of the simulator Conclusions Example of image optimisation Image optimisation Development of the simulator Conclusions Example of image optimisation (a) picture of sample (b) polychromatic simulation (1 frame) (c) polychromatic simulation (16 frames) Simulating a dolomite sample (8 mm) with calcite veins

15. Introduction Development of the simulator Conclusions Example of image optimisation Image optimisation Development of the simulator Conclusions Example of image optimisation Simulating a dolomite sample (8 mm) with calcite veins (d) monochromatic simulation (e) polychromatic simulation (16 frames) using a 0,01 mm copper filter (f) polychromatic simulation (16 frames) using a 0,1 mm copper filter

16. Introduction Development of the simulator Conclusions Example of image optimisation Conclusions Development of the simulator Conclusions Example of image optimisation Conclusions Objects defined by superposing object parts Polychromacity of the X-ray spectrum is modelled by averaging over a number of monochromatic simulations The finite sizes of the source and detector elements are modelled by sampling both parts It is possible to include noise and filters in the scanning process Optimising image quality by trial and error experiments Conclusions: