No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
Dose reduction using a dynamic, piecewise-linear attenuator
1. M. Gies, W. A. Kalender, H. Wolf, C. Suess, and M. T. Madsen, “Dose reduction in CT by anatomically adapted tube current modulation. I. Simulation studies,” Med. Phys. 26, 2235–2247 (1999).
2. W. A. Kalender, H. Wolf, and C. Suess, “Dose reduction in CT by anatomically adapted tube current modulation. II. Phantom measurements,” Med. Phys. 26, 2248–2253 (1999).
3. J. Hsieh, Computed Tomography: Principles, Design, Artifacts, and Recent Advances (Society of Photo Optical, 2003).
4. T. G. Schmidt, R. Fahrig, N. J. Pelc, and E. G. Solomon, “An inverse-geometry volumetric CT system with a large-area scanned source: A feasibility study,” Med. Phys. 31, 2623–2627 (2004).
5. T. G. Schmidt, J. Star-Lack, N. R. Bennett, S. R. Mazin, E. G. Solomon, R. Fahrig, and N. J. Pelc, “A prototype table-top inverse-geometry volumetric CT system,” Med. Phys. 33, 1867–1878 (2006).
6. S. R. Mazin, J. Star-Lack, N. R. Bennett, and N. J. Pelc, “Inverse-geometry volumetric CT system with multiple detector arrays for wide field-of-view imaging,” Med. Phys. 34, 2133–2142 (2007).
7. S. Bartolac, S. Graham, J. Siewerdsen, and D. Jaffray, “Fluence field optimization for noise and dose objectives in CT,” Med. Phys. 38, S2–S17 (2011).
8. J. Sperl, D. Beque, B. Claus, B. De Man, B. Senzig, and M. Brokate, “Computer-assisted scan protocol and reconstruction (CASPAR)—Reduction of image noise and patient dose,” IEEE Trans.Med. Imaging 29(3), 724–732 (2010).
10. T. P. Szczykutowicz and C. A. Mistretta, “Design of a digital beam attenuation system for computed tomography: Part I. System design and simulation framework,” Med. Phys. 40, 021905 (12pp.) (2013).
11. T. P. Szczykutowicz and C. A. Mistretta, “Design of a digital beam attenuation system for computed tomography. Part II. Performance study and initial results,” Med. Phys. 40, 021906 (9pp.) (2013).
12. T. Szczykutowicz and C. Mistretta, “Practical considerations for intensity modulated CT,” Proc. SPIE 8313, 83134E–183134E–11 (2012).
13. X. Tang, J. Hsieh, R. A. Nilsen, S. Dutta, D. Samsonov, and A. Hagiwara, “A three-dimensional-weighted cone beam filtered backprojection (CB-FBP) algorithm for image reconstruction in volumetric CT—Helical scanning,” Phys. Med. Biol. 51, 855–874 (2006).
15. A. Kak and M. Slaney, Principles of Computerized Tomographic Imaging (SIAM, Philadelphia, 1988).
16. S. Agostinelli, J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce, M. Asai, D. Axen, S. Banerjee, and G. Barrand, “Geant4-a simulation toolkit,” Nucl. Instrum. Methods Phys. Res.: Sect. A 506(3), 250–303 (2003).
18. M. Grant and S. Boyd, CVX: Matlab software for disciplined convex programming, version 1.21 (2011).
19. M. Grant and S. Boyd, “Graph implementations for nonsmooth convex programs,” in Recent Advances in Learning and Control , edited by V. Blondel, S. Boyd, and H. Kimura (Springer-Verlag Limited, 2008), pp. 95–110.
21. A. Berrington de Gonzalez, M. Mahesh, K. P. Kim, M. Bhargavan, R. Lewis, F. Mettler, and C. Land, “Projected cancer risks from computed tomographic scans performed in the United States in 2007,” Arch. Intern. Med. 169(22), 2071–2077 (2009).
22. J. R. Mayo, K. P. Whittall, A. N. Leung, T. E. Hartman, C. S. Park, S. L. Primack, G. K. Chambers, M. K. Limkeman, T. L. Toth, and S. H. Fox, “Simulated dose reduction in conventional chest CT: Validation study,” Radiology 202(2), 453–457 (1997).
23. J. Li, U. K. Udayasankar, T. L. Toth, J. Seamans, W. C. Small, and M. K. Kalra, “Automatic patient centering for MDCT: Effect on radiation dose,” Am. J. Roentgenol. 188(2), 547–552 (2007).
25. J. B. Thibault, K. D. Sauer, C. A. Bouman, and J. Hsieh, “A three-dimensional statistical approach to improved image quality for multislice helical CT,” Med. Phys. 34, 4526–4544 (2007).
26. K. Taguchi, S. Srivastava, H. Kudo, and W. C. Barber, in Nuclear Science Symposium Conference Record on Enabling Photon Counting Clinical X-ray CT (IEEE, 2009), pp. 3581–3585 (2009).
Article metrics loading...
The authors recently proposed a dynamic, prepatient x-ray attenuator capable of producing a piecewise-linear attenuation profile customized to each patient and viewing angle. This attenuator was intended to reduce scatter-to-primary ratio (SPR), dynamic range, and dose by redistributing flux. In this work the authors tested the ability of the attenuator to reduce dose and SPR in simulations.
The authors selected four clinical applications, including routine full field-of-view scans of the thorax and abdomen, and targeted reconstruction tasks for an abdominal aortic aneurysm and the pancreas. Raw data were estimated by forward projection of the image volume datasets. The dynamic attenuator was controlled to reduce dose while maintaining peak variance by solving a convex optimization problem, assuminga priori knowledge of the patient anatomy. In targeted reconstruction tasks, the noise in specific regions was given increased weighting. A system with a standard attenuator (or “bowtie filter”) was used as a reference, and used either convex optimized tube current modulation (TCM) or a standard TCM heuristic. The noise of the scan was determined analytically while the dose was estimated using Monte Carlo simulations. Scatter was also estimated using Monte Carlo simulations. The sensitivity of the dynamic attenuator to patient centering was also examined by shifting the abdomen in 2 cm intervals.
Compared to a reference system with optimized TCM, use of the dynamic attenuator reduced dose by about 30% in routine scans and 50% in targeted scans. Compared to the TCM heuristics which are typically used withouta priori knowledge, the dose reduction is about 50% for routine scans. The dynamic attenuator gives the ability to redistribute noise and variance and produces more uniform noise profiles than systems with a conventional bowtie filter. The SPR was also modestly reduced by 10% in the thorax and 24% in the abdomen. Imaging with the dynamic attenuator was relatively insensitive to patient centering, showing a 17% increase in peak variance for a 6 cm shift of the abdomen, instead of an 82% increase in peak variance for a fixed bowtie filter.
A dynamic prepatient x-ray attenuator consisting of multiple wedges is capable of achieving substantial dose reductions and modest SPR reductions.
Full text loading...
Most read this month