A classical X-ray computerized tomographic (CT) scanner provides cross-sectional images of objects in a nondestructive
manner. This approach gives erroneous results when the object under consideration has certain time-dependent properties.
These results can be improved by certain corrections that can be applied in the reconstruction process.
An experiment is performed (at IIT Kanpur) on a mini-CT scanner (Procon X-ray GmbH) for dynamic imaging of a circular
20 mm dia object made of Perspex. This CT set-up has a 7 micron focal spot X-ray unit and a flat panel detector of 1024 x
1024 photo-diodes. It gives 3D projection data in cone-beam geometry configuration. Five specimens (made of Perspex) were
scanned for 400 projections. These specimens represent the state of a time-dependent object at five different measurement
times. They have several holes which represent voids. The positions of hollow cylinders (holes) are changed in all five specimens
assuming that voids appear at different positions (on the cross-section) at different times due to the moving boundaries
of the object (e.g. the human heart).
Convolution back-projection algorithm is used for reconstruction purposes. Dynamic bias correction is applied and corrected
cross-sections are characterized with “KT-2” signature. This approach is based on Sobolev space framework and it is “global”
in nature. It is useful for predicting the type of flow pattern and the size of bubbles.
1. Harms, A. and F.A.R. Laratta, “The Dynamic-Bias in radiation interrogation of two-phase flow,” Int. J. Heat Mass
Transfer 16, pp.1459-1465, 1973.
2. Laratta, F.A.R. and A.A. Harms, “A Reduced formula for the Dynamic-Bias in radiation interrogation of two-phase
flow,” Int. J. Heat Mass Transfer 17, pp.464, 1974.
3. Jayakumar, P. and P. Munshi, “A comprehensive study of measurement uncertainty in tomographic reconstruction of void
profiles in a mercury-nitrogen flow,” Experiments in Fluids 26, pp.535-541, 1999.
4. Shakya, S., P. Munshi, M. Behling, A. Luke and D. Mewes, “Analysis of Dynamic Bias Error in X-ray Tomographic
Reconstructions of a Three-phase Flow System,” Int. J. Multiphase Flow 58, pp.57-71, 2014.
5. Shakya S., G. Bable, A. Maan and P. Munshi, “Tomographic Imaging of Rotating Objects,” Proc. of 14th Asia Pacific
Conference on Non-Destructive Testing, Mumbai, India, Paper 110.
6. Munshi, P., R.K.S. Rathore and S. Vijay, “A numerical study of an inverse theorem for computerized tomography,”
European Journal of NDT 3, pp.104-107, 1994.
7. Shakya S., P. Munshi, A. Luke and D. Mewes, “Application of Tomographic NDT Techniques in Oil Industry,” Proc.
ASNT 22nd Research Symposium, Memphis, pp.179-183, 2013.
10. Gulati, S., M. Behling, P. Munshi, A. Luke and D. Mewes, “Tomographic KT-1 signature of phase-fraction distributions
in multiphase bubble columns,” Flow Measurement and Instrumentation 21, pp.249-254, 2010.
11. Athe, P., S. Shakya, P. Munshi, A. Luke and D. Mewes, “Characterization of multiphase flow in bubble columns using
KT-1 signature and fractal dimension,” Flow Measurement and Instrumentation 33, pp.122-137, 2013.
12. Munshi, P., R.K.S. Rathore, K.S. Ram and M.S. Kalra, “Error Analysis of Tomographic Filters II: Results,” NDT & E
International 26, pp.235-240, 1993.