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Dose reduction using a dynamic, piecewise-linear attenuator
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/content/aapm/journal/medphys/41/2/10.1118/1.4862079
1.
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, 22352247 (1999).
http://dx.doi.org/10.1118/1.598779
2.
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, 22482253 (1999).
http://dx.doi.org/10.1118/1.598738
3.
3. J. Hsieh, Computed Tomography: Principles, Design, Artifacts, and Recent Advances (Society of Photo Optical, 2003).
4.
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, 26232627 (2004).
http://dx.doi.org/10.1118/1.1786171
5.
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, 18671878 (2006).
http://dx.doi.org/10.1118/1.2192887
6.
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, 21332142 (2007).
http://dx.doi.org/10.1118/1.2737168
7.
7. S. Bartolac, S. Graham, J. Siewerdsen, and D. Jaffray, “Fluence field optimization for noise and dose objectives in CT,” Med. Phys. 38, S2S17 (2011).
http://dx.doi.org/10.1118/1.3574885
8.
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), 724732 (2010).
http://dx.doi.org/10.1109/TMI.2009.2034515
9.
9. S. S. Hsieh and N. J. Pelc, “The feasibility of a piecewise-linear dynamic bowtie filter,” Med. Phys. 40(3), 031910 (12pp.) (2013).
http://dx.doi.org/10.1118/1.4789630
10.
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).
http://dx.doi.org/10.1118/1.4773879
11.
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).
http://dx.doi.org/10.1118/1.4773880
12.
12. T. Szczykutowicz and C. Mistretta, “Practical considerations for intensity modulated CT,” Proc. SPIE 8313, 83134E183134E11 (2012).
http://dx.doi.org/10.1117/12.911355
13.
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, 855874 (2006).
http://dx.doi.org/10.1088/0031-9155/51/4/007
14.
14. D. A. Chesler, S. J. Riederer, and N. J. Pelc, “Noise due to photon counting statistics in computed x-ray tomography,” J. Comput. Assist. Tomogr. 1(1), 6474 (1977).
http://dx.doi.org/10.1097/00004728-197701000-00009
15.
15. A. Kak and M. Slaney, Principles of Computerized Tomographic Imaging (SIAM, Philadelphia, 1988).
16.
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), 250303 (2003).
http://dx.doi.org/10.1016/S0168-9002(03)01368-8
17.
17. P. M. Joseph and R. D. Spital, “A method for correcting bone induced artifacts in computed tomography scanners,” J. Comput. Assist. Tomogr. 2(1), 100108 (1978).
http://dx.doi.org/10.1097/00004728-197801000-00017
18.
18. M. Grant and S. Boyd, CVX: Matlab software for disciplined convex programming, version 1.21 (2011).
19.
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. 95110.
20.
20.CASIMAGE radiology teaching files database available from http://pubimage.hcuge.ch.
21.
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), 20712077 (2009).
http://dx.doi.org/10.1001/archinternmed.2009.440
22.
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), 453457 (1997).
23.
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), 547552 (2007).
http://dx.doi.org/10.2214/AJR.06.0370
24.
24. T. Toth, Z. Ge, and M. P. Daly, “The influence of patient centering on CT dose and image noise,” Med. Phys. 34, 30933101 (2007).
http://dx.doi.org/10.1118/1.2748113
25.
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, 45264544 (2007).
http://dx.doi.org/10.1118/1.2789499
26.
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. 35813585 (2009).
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/41/2/10.1118/1.4862079
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Figures

Image of FIG. 1.

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FIG. 1.

The piecewise-linear dynamic attenuator, consisting of a series of wedges attached to actuators (not shown). The fan beam is also shown, and the x-ray focal spot sits at the apex of the fan. (a) The set of N wedges. Two of the wedges have been shaded and translated. (b) A cross section of the wedges, showing the triangular cross sections in the plane of the fan beam. The wedge which was translated downward introduces only a small amount of attenuation, while the wedge that was translated upward introduces more attenuation.

Image of FIG. 2.

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FIG. 2.

Example attenuation functions for the thorax. In the lateral direction, the dynamic attenuator is able to modulate its shape to become more narrow. In the anterior/posterior or A/P direction, the dynamic attenuator can expand its shape and additionally compensate for the effect of the lung field.

Image of FIG. 3.

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FIG. 3.

Bowtie filter used in this study. An example of a possible attenuation function delivered by the piecewise-linear dynamic bowtie is also provided.

Image of FIG. 4.

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FIG. 4.

Datasets used in this study, consisting of (from left to right) an abdomen, thorax, pancreas, and abdominal aortic aneurysm (AAA). The pancreas and AAA are targeted scans, and the targeted region is displayed at regular intensity while the nontargeted region is darkened.

Image of FIG. 5.

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FIG. 5.

(Left column) Sinograms, (middle column) TCM profiles, and (right column) example piecewise-linear attenuation profiles for the (top row) abdomen, (top middle row) thorax, (middle row) AAA, (bottom middle row) pancreas, and (bottom row) abdomen shifted by 4 cm. The sinograms have three horizontal lines drawn in them, corresponding to different views for which the example piecewise-linear attenuation profiles are plotted. The bowtie filter is plotted as a reference to the example piecewise-linear attenuation profiles, with its attenuation converted to an equivalent thickness of iron at 60 keV. The conformity of the dynamic attenuator to the patient attenuation can be seen in several locations, including the middle example for the shifted abdomen.

Image of FIG. 6.

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FIG. 6.

Noise realizations using (top row) the standard bowtie filter with optimized TCM and (bottom row) the dynamic bowtie with optimized control. The datasets are (far left) centered abdomen, (center left) thorax, (center right) targeted AAA, and (far right) targeted pancreas. Within each dataset, the peak variance is held constant. This corresponds to dose reductions of 33%, 25%, 56%, 47% for the abdomen, thorax, AAA, and pancreas, respectively. The standard deviation of the noise is calculated in certain ROIs and is indicated on the corner of each image. Note that the ROI standard deviation is calculated from the noise image only, and does not include the variation from the anatomy. See text for details. In the ROI names, “left” and “right” refer to the location on the page, and not in the patient.

Image of FIG. 7.

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FIG. 7.

Same noise realizations as Fig. 6 but at constant dose. In this application, the dynamic attenuator is used for reduced noise instead of reduced dose.

Image of FIG. 8.

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FIG. 8.

Axial cross sections of variance maps using (a) the reference bowtie with square-root heuristic TCM, (b) the reference bowtie with optimized TCM, and (c) the dynamic attenuator with optimized control. Within each dataset, the variance maps are individually windowed. Variance maps represent analytically predicted variance with the mA tuned so that the average dose delivered is the same for all methods in each dataset.

Image of FIG. 9.

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FIG. 9.

Coronal reformats of Fig. 8 .

Image of FIG. 10.

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FIG. 10.

Dose for each dataset, with (left column) square-root TCM, (middle column) optimized TCM, and (right column) dynamic attenuator. The bottom row (40 mm shift) is the shifted abdomen. The dose has been integrated in the z direction, and is shown normalized to constant peak variance within each dataset.

Tables

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TABLE I.

System parameters.

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TABLE II.

Dose reduction found on different datasets and with different system options.

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TABLE III.

Relative dose required to achieve the same peak variance as the abdomen dataset is shifted off-center in 2 cm intervals. The optimized TCM and dynamic attenuator systems achieve a large dose benefit even with no shift, but this gap increases as the abdomen is shifted further off center.

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TABLE IV.

SPR comparison between a system using a standard bowtie and a system with a dynamic attenuator.

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/content/aapm/journal/medphys/41/2/10.1118/1.4862079
2014-01-30
2014-04-17

Abstract

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, assuming 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 without 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.

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Scitation: Dose reduction using a dynamic, piecewise-linear attenuator
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/41/2/10.1118/1.4862079
10.1118/1.4862079
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