1. Improving Image Accuracy of ROI in CT Using Prior Image
Jiseoc Lee1, Seungryong Cho1, Jinsung Kim2
1Dept. Nuclear & Quantum Engineering, KAIST
2Samsung Medical Center
May 26-31, 2012, Beijing, China

2. Contents Introduction
Origin of artifacts in the reconstructed image from truncated data
Estimation of lost data
Materials and methods
Simulation study
Experimental study
Results
Conclustion

3. Introduction Accurate CT numbers are required for clinical purposes.
< Liver abscess >
< Coronary calcium scoring >

4. Introduction – Origin of Artifact
ROI imaging and truncation artifacts
< Schematic of ROI imaging >
< Filtering of truncated projection1>
B. Ohnesorge, T. Flohr, J. P. Heiken et al. (2000) Efficient correction for CT image artifacts caused by objects extending outside the scan field of view. Med. Phys. 27, 39-46
Water-based ceramic slurry is freezing while controlling the growth direction of ice, and sublimation of the ice are generated by drying it at a reduced pressure. Sintering this green body, a porous ceramic with complex pore strucutre is obtained.

5. Introduction – Estimation of Lost Data
Estimation methods
Mirroring and extrapolation
< Symmetric mirroring extrapolation1 >
< Extrapolation using fitted water cylinder2 >
1. B. Ohnesorge, T. Flohr, J. P. Heiken et al. (2000) Efficient correction for CT image artifacts caused by objects extending outside the scan field of view. Med. Phys. 27, 39-46
2. J. Hsieh, E. Chao, et al. (2004) A novel reconstruction algorithm to extend the CT scan filed-of-view. Med. Phys. 31,

6. Introduction – Proposed Method
Reduction of image artifacts using prior CT images
Inside ROI : Current CT
Outside ROI : Prior CT
Registration (Alignment)
Sinogram merging
 Hope to remove truncation artifacts
< Merging process >

7. Simulation Study Simulation conditions Object : XCAT Phantom
Scanning condition
Source to isocenter : mm
Isocenter to detector : mm
Detector size : 410 x 410 mm
Two different phantoms : Prior and current ROI (Half of liver and stomach lost)
Sinogram merging : Smoothing filter – Moving averaging filter
< Two numerical phantoms >
Prior
ROI
Liver
809.75
Stomach
399.33
202.6
< Size of liver and stomach in prior and ROI image (ml) >

8. Simulation Study Data acquisition Phantom preparation A prior phantom
No correction (Reference)
Extreme ROI condition : Outside the ROI have zero pixel value
Correction methods
Linear extrapolation
Merging prior data
Phantom preparation
A prior phantom
ROI phantom (Liver & stomach reduced)
Projection data
Merge two log-data in sinogram

9. Simulation Study Comparison of three methods
Projections and reconstructed images
Prior
ROI
Linear Extrapol.
Proposed method
Prior
ROI
Linear Extrapol.
Proposed method
(a)
(b)
(c)
(d)
< Projection images >
< CX of recon. 3D images >

10. Simulation Study More quantitative comparison
Profiles in projection and reconstructed images
< Profiles at red line in projection images >
< Profiles at red line in recon. images >

11. Experimental Study Two real cone-beam CT data sets
Post process
Reconstruction and registration
Reprojection
Follow the same process of simulation study
Reconstruction
Two cone-beam CT data sets
1st : A prior data
2nd : Present data
Aligned 1st & 2nd data set
Registration
Reprojection
Same process of simulation study
Two aligned data sets

12. Experimental Study Comparison of three methods Reconstructed images
ROI
Linear Extrapol.
Proposed method
< Cross section of reconstructed 3D images >

13. Experimental Study Profiles In reconstructed images
< Profiles at red lines in previous images >

14. Conclusion
Image artifacts due to truncated data were much reduced by use of prior image data.
Image accuracy in the ROI was greatly improved by use of the proposed method.
A feasibility of fully truncated ROI imaging as a viable option to low dose CT was demonstrated.

15. Acknowledgement
Special thanks to Dr. Jinsung Kim in Samsung Medical Center in Korea

16. Thank you for kind attention
Any question or advice would be appreciated