Keywords:
Dedicated application-specific CT systems are popular solutions to high-resolution clinical needs. Some applications, such as mammography and extremities imaging, require spatial resolution beyond current capabilities. Thorough understanding of system properties may help tailor system design, acquisition protocols, and reconstruction algorithms to improve image quality. As resolution requirements increase, more accurate models of system properties are needed. Using a high-fidelity measurement model, we analyze the effects of shift-variant focal spot blur due to depth-dependence and anode angulation on image quality throughout the three-dimensional field of view of a simulated extremities scanner. A model of the shift-variant blur associated with this device is then incorporated into a Model-Based Iterative Reconstruction (MBIR) algorithm, which is then compared to FDK and MBIR with simpler blur models (i.e., no projection blur and shift-invariant projection blur) at select locations throughout the field of view. We show that shift-variant focal spot blur leads to location-dependent imaging performance. Furthermore, changing the orientation of the X-ray tube alters this spatial dependence. The analysissuggests methods to improve imaging performance based onspecific image quality needs. For example, for small region of interest imaging, a transaxial X-ray tube orientation provides the best local image quality at a specific location, while for large objects an axial X-ray tube orientation provides better image quality uniformity. The results also demonstrate that image quality can be improved by combining accurate blur modeling with MBIR. Specifically, across the entire field of view, MBI Rwith shift-variant blur modeling yielded the best image quality, followed by MBIR with a shift-invariant blur model, MBIR with an identity blur model, and FDK, respectively. These results suggest a number of opportunities for the optimization of imaging system performance in the hardware setup, the imaging protocol, and the reconstruction approach. While the high-fidelity models used here are applied using the specifications of a dedicated extremities imaging system, the methods are general and may be applied to optimize imaging performance in any CT system.
- Steven Tilley II
- Johns Hopkins University, USA
- Wojciech Zbijewski
- Johns Hopkins University, USA
- J. Webster Stayman
- Johns Hopkins University, USA
- C.-J. Lai, C. C. Shaw, L. Chen, M. C. Altunbas, X. Liu, T. Han, T. Wang, W. T. Yang, G. J. Whitman, and S.-J. Tu, “Visibility of microcalcification in cone beam breast CT: Effects of X-ray tube voltage and radiation dose.” Medical physics, vol. 34, no. 7, pp. 2995–3004, 2007.
- A. L. C. Kwan, J. M. Boone, K. Yang, and S.-Y. Huang, “Evaluation of the spatial resolution characteristics of a cone-beam breast CT scanner.” Medical Physics, vol. 34, no. 1, pp. 275–281, 2007.
- J. A. Carrino, A. Al Muhit, W. Zbijewski, G. K. Thawait, J. W. Stayman, N. Packard, R. Senn, D. Yang, D. H. Foos, J. Yorkston, and J. H. Siewerdsen, “Dedicated cone-beam CT system for extremity imaging.” Radiology, vol. 270, no. 3, pp. 816–24, 2014.
- E. Marinetto, M. Brehler, A. Sisniega, Q. Cao, J. W. Stayman, J. Yorkston, J. H. Siewerdsen, and W. Zbijewski, “Quantification of bone microarchitecture in ultrahigh resolution extremities conebeam CT with a CMOS detector and compensation of patient motion,” in Computer Assisted Radiology 30th International Congress and Exhibition, Heidelberg, Germany, Jun. 2016.
- J. Xu, A. Sisniega, W. Zbijewski, H. Dang, J. W. Stayman, M. Mow, X. Wang, D. H. Foos, V. E. Koliatsos, N. Aygun, and J. H. Siewerdsen, “Technical assessment of a prototype cone-beam CT system for imaging of acute intracranial hemorrhage,” Medical Physics, vol. 43, no. 10, pp. 5745–5757, Oct. 2016.
- B. M. W. Tsui, H. B. Hu, D. R. Gilland, and G. T. Gullberg, “Implementation of Simultaneous Attenuation and Detector Response Correction in Spect.” IEEE Transactions on Nuclear Science, vol. 35, no. 1, pp. 778–783, 1987.
- E. U. Mumcuoglu, R. M. Leahy, S. R. Cherry, and E. Hoffman, “Accurate geometric and physical response modelling for statistical image reconstruction in high resolution PET,” in , 1996 IEEE Nuclear Science Symposium, 1996. Conference Record, vol. 3, Nov. 1996, pp. 1569–1573 vol.3.
- P. J. La Rivi`ere and P. Vargas, “Correction for resolution nonuniformities caused by anode angulation in computed tomography,” IEEE transactions on medical imaging, vol. 27, no. 9, pp. 1333–1341, 2008.
- S. Tilley II, W. Zbijewski, J. H. Siewerdsen, and J. W. Stayman, “Modeling shift-variant X-ray focal spot blur for high-resolution flatpanel cone-beam CT,” in Proc. 4th Intl. Mtg. on Image Formation in X-Ray CT, 2016.
- S. Tilley II, M. Jacobson, Q. Cao, M. Brehler, A. Sisniega, W. Zbijewski, and J. W. Stayman, “Penalized-Likelihood Reconstruction with High-Fidelity Measurement Models for High-Resolution Cone-Beam Imaging,” IEEE Transactions on Medical Imaging (submitted), 2017.
- S. Tilley II, J. H. Siewerdsen, W. Zbijewski, and J. W. Stayman, “Nonlinear statistical reconstruction for flat-panel cone-beam CT with blur and correlated noise models,” in SPIE 9783 Medical Imaging 2016: Physics of Medical Imaging, vol. 9783, 2016, pp. 97 830R–97 830R–6.
- P. J. Huber, Robust Statistics. New York: Wiley, 1981.
- Y. Nesterov, “Smooth minimization of non-smooth functions,” Mathematical Programming Journal, Series A, vol. 103, pp. 127–152, 2005.
- D. Kim, S. Ramani, and J. A. Fessler, “Combining Ordered Subsets and Momentum for Accelerated X-Ray CT Image Reconstruction,” IEEE transactions on medical imaging, vol. 34, no. 1, pp. 167–178, 2015.
- Q. Cao, W. Zbijewski, A. Sisniega, J. Yorkston, J. H. Siewerdsen, and J. W. Stayman, “Multiresolution iterative reconstruction in highresolution extremity cone-beam CT,” Physics in Medicine and Biology, vol. 61, no. 20, p. 7263, 2016.