1. tomography: from medicine to accelerator physics, and back again
Ander Biguri, Manjit Dosanjh,
Steven Hancock, Manuchehr Soleimani

2. Index Lung Cancer and Image guided radiation therapy
X-ray tomography: FDK, iterative algorithms
TIGRE toolbox
Algorithms
Examples
Motion in Tomography
Tomography at CERN: phase space tomography
Motion problem “solved”
Conclusions

3. Cancer – a growing challenge
More than 3 million new cancer cases in Europe each year and million associated deaths
Increase by 2030: 75% in developed countries and 90% in developing countries

4. Radiotherapy in the 21st century
Cure (~ 50% cancer cases are cured)
Conservative (non-invasive, few side effects)
Cheap ( 5-10% of total cost of cancer on RT)
There is no substitute for RT in the near future
The rate of patients treated with RT is increasing
More than 50% patients treated with RT

5. X-ray tomography X-ray images from different angles
Generally solved using the “Radon transform” (FDK)
In medicine, FDK is king
Iterative algorithms!

6. Iterative algorithms express
A: For 5123 image, 5122 detector, ~320Gb of memory
[A·x , AT·b] ,1 of each per iteration by operator
GPU:
[A·x , AT·b] → [A(x) , AT(b)]
> 99% of time
Multiple processors (> 60K in a TESLA k40)
Optimized memory
Hardware accelerated interpolation

7. Why do we like iterative algorithms
Better performance against noise
Better performance with low projections
“Tuneable” e.g. Total variation
But…..
1 iteration is slower than FBP/FDK
Memory expensive

8. TIGRE toolbox
BSD Licensed, more info at github.com/CERN/TIGRE

9. Algorithms in TIGRE 5122 detector 2563 image FDK
“SART type” algorithms (gradient descend)
SART
OS-SART
SIRT
Krylov Subspace
CGLS
Statistical inversion
MLEM
Total Variation
ASD-POCS
OSC-TV
B-ASD-POCS- β
SART-TV
5122 detector 2563 image

10. Examples – Gradient descend
SART
“Real”
SIRT
OS-SART

11. Example- few projections
FDK
Normally 300~500 in medical scenario
Potential reduction of
dose for the same
quality of image
ASD-POCS
OSC-TV
SART-TV
B-ADS-POCS- β

12. Motion in X-rays: the problem
Image reconstruction

13. Tomography at CERN Phase Space tomography:
Particles “move” between projections.
“Motion” information is measured and encoded system matrix (A)
Images are reconstructed at arbitrary time using SART
cern.ch/tomography , 1999!

14. Motion: solution

15. Motion: solution

16. Conclusions and future work
Fast, complete MATLAB-CUDA CBCT toolbox
Choice of algorithm matters! Iterative algorithms are superior than FDK (but slower)
CERN to medical applications: on the fly computation of operators A(x) , AT(b) allows to introduce motion knowledge. Full 4D imaging is the next step

17. Thanks CERN and EPSRC The Christie Hospital
Manjit Dosanjh Steven Hancock Imanol Luengo Manucherh Soleimani
NVIDIA

18. Questions