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Evaluation of dual energy quantitative CT for determining the spatial distributions of red marrow and bone for dosimetry in internal emitter radiation therapy
1. Y. K. Dewaraja, A. M. Avram, P. L. Roberson, L. B. Smith, S. J. Wilderman, J. Shen, H. Savas, E. Youssef, M. S. Kaminski, and M. J. Schipper, “Tumor absorbed dose predicts progression free survival (PFS) following I-131 radioimmunotherapy (RIT),” J. Nucl. Med. 54, 16P (2013).
2. A. P. Shah, W. E. Bolch, D. A. Rajon, P. W. Patton, and D. W. Jokisch, “A paired-image radiation transport model for skeletal dosimetry,” J. Nucl. Med. 46, 344–353 (2005).
3. S. J. Wilderman, J. P. L. Roberson, W. E. Bolch, and Y. K. Dewaraja, “Investigation of effect of variations in bone fraction and red marrow cellularity on bone marrow dosimetry in radio-immunotherapy,” Phys. Med. Biol. 58(14), 4717–4731 (2013).
4. K.-Y. Ho, H. H. Hu, J. C. Keyak, P. M. Colletti, and C. M. Powers, “Measuring bone mineral density with fat–water MRI: Comparison with computed tomography,” J. Mag. Res. Imaging 37(1), 237–242 (2013).
5. D. Ballon, A. Jakubowski, J. Gabrilovem, C. Graham, M. Zakowski, C. Sheridan, and J. A. Koutcher, “In vivo measurements of bone marrow cellularity using volume-localized proton NMR spectroscopy,” Mag. Res. Med. 19, 85–95 (1991).
6. D. Ballon, A. A. Jakubowski, M. C. Graham, E. Schneider, and J. A. Koutcher, “Spatial mapping of the percentage cellularity in human bone marrow using magnetic resonance imaging,” Med. Phys. 23(2), 243–250 (1996).
7. H. Ishizaka, H. Horikoshi, T. lnoue, T. Fukusato, and M. Matsumoto, “Bone marrow cellularity: Quantification by chemical-shift misregistration in magnetic resonance imaging and comparison with histomorphometrical techniques,” Australas Radiol. 39, 411–414 (1995).
9. Y. K. Dewaraja et al., “131I-tositumomab radioimmunotherapy: Initial tumor dose-response results using 3-dimensional dosimetry including radiobiologic modeling,” J. Nucl. Med. 51, 1155–1162 (2010).
11. M. M. Goodsitt, R. H. Johnson, and C. H. Chesnut, “A new set of calibration standards for estimating the fat and mineral content of vertebrae via dual energy QCT,” Bone Miner. 13, 217–233 (1991).
12. International Commission on Radiological Protection, “Report of the Task Group on Reference Man,” ICRP Publication 23 (Pergamon, Oxford, England, 1975).
14. P. Steiger, J. Block, J. S. Steiger, A. F. Heuck, A. Friedlander, B. Ettinger, S. T. Haris, C. C. Gluer, and H. K. Genant, “Spinal bone-mineral density measured with quantitative CT—Effect of region of interest, vertebral level, and technique,” Radiology 175, 537–543 (1990).
16. H. B. Mann and D. R. Whitney, “On a test of whether one of two random variables is stochastically larger than the other,” Ann. Math. Stat. 18(1), 50–60 (1947).
18. M. Hollander and D. A. Wolfe, Nonparametric Statistical Methods (Wiley, New York, 1973).
19. W. E. Bolch, P. W. Paton, D. A. Rajon, A. P. Shah, D. W. Jokish, and B. A. Inglis, “Considerations of marrow cellularity in 3-dimensional dosimetric models of the trabecular skeleton,” J. Nucl. Med. 43, 97–108 (2002).
20. D. Schellinger, C. S. Lin, J. Lim, H. G. Hatipoglu, J. C. Pezzullo, and A. J. Singer, “Bone marrow fat and bone mineral density on proton MR spectroscopy and dual-energy X-ray absorptiometry: Their ratio as a new indicator of bone weakening,” Am. J. Roentgenol. 183(6), 1761–1765 (2004).
21. G. P. Liney, C. P. Bernard, D. J. Manton, L. S. Turnbull, and C. M. Langton, “Age, gender, and skeletal variation in bone marrow composition: A preliminary study at 3.0 Tesla,” J. Magn. Reson. Imaging 26(3), 787–793 (2007).
23. W. D. Reinbold, C. P. Adler, W. A. Kalender, and R. Lente, “Accuracy of vertebral mineral determination by dual-energy quantitative computed tomography,” Skeletal Radiol. 20(1), 25–9 (1991).
24. M. M. Goodsitt, P. Hoover, M. S. Veldee, and S. L. Hsueh, “The composition of bone marrow for a dual-energy quantitative tomography technique: A cadaver and computer simulation study,” Invest. Radiol. 29, 695–704 (1994).
25. J. Nuyts, B. De Man, and J. A. Fessler, “Modeling the physics in the iterative reconstruction for transmission computed tomography,” Phys. Med. Biol. 58(12), R63–R96 (2013).
26. A. N. Primak, J. C. Ramirez Giraldo, X. Liu, L. Yu, and C. H. McCollough, “Improved dual-energy material discrimination for dual-source CT by means of additional spectral filtration,” Med. Phys. 36(4), 1359–1369 (2009).
27. G. S. Fung, S. Kawamoto, B. R. Matlaga, K. Taguchi, X. Zhou, E. K. Fishman, and B. M. Tsui, “Differentiation of kidney stones using dual-energy CT with and without a tin filter,” Am. J. Roentgenol. 198(6), 1380–1386 (2012).
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To evaluate a three-equation three-unknown dual-energy quantitative CT (DEQCT) technique for determining region specific variations in bone spongiosa composition for improved red marrow dose estimation in radionuclide therapy.
The DEQCT method was applied to 80/140 kVp images of patient-simulating lumbar sectional body phantoms of three sizes (small, medium, and large). External calibration rods of bone, red marrow, and fat-simulating materials were placed beneath the body phantoms. Similar internal calibration inserts were placed at vertebral locations within the body phantoms. Six test inserts of known volume fractions of bone, fat, and red marrow were also scanned. External-to-internal calibration correction factors were derived. The effects of body phantom size, radiation
dose, spongiosa region segmentation granularity [single (∼17 × 17 mm) region of interest (ROI), 2 × 2, and 3 × 3 segmentation of that single ROI], and calibration method on the accuracy of the calculated volume fractions of red marrow (cellularity) and trabecular bone were evaluated.
For standard low dose DEQCT x-ray technique factors and the internal calibration method, the RMS errors of the estimated volume fractions of red marrow of the test inserts were 1.2–1.3 times greater in the medium body than in the small body phantom and 1.3–1.5 times greater in the large body than in the small body phantom. RMS errors of the calculated volume fractions of red marrow within 2 × 2 segmented subregions of the ROIs were 1.6–1.9 times greater than for no segmentation, and RMS errors for 3 × 3 segmented subregions were 2.3–2.7 times greater than those for no segmentation. Increasing the dose by a factor of 2 reduced the RMS errors of all constituent volume fractions by an average factor of 1.40 ± 0.29 for all segmentation schemes and body phantom sizes; increasing the dose by a factor of 4 reduced those RMS errors by an average factor of 1.71 ± 0.25. Results for external calibrations exhibited much larger RMS errors than size matched internal calibration. Use of an average body size external-to-internal calibration correction factor reduced the errors to closer to those for internal calibration. RMS errors of less than 30% or about 0.01 for the bone and 0.1 for the red marrow volume fractions would likely be satisfactory for human studies. Such accuracies were achieved for 3 × 3 segmentation of 5 mm slice images for: (a) internal calibration with 4 times dose for all size body phantoms, (b) internal calibration with 2 times dose for the small and medium size body phantoms, and (c) corrected external calibration with 4 times dose and all size body phantoms.
Phantom studies are promising and demonstrate the potential to use dual energy quantitative CT to estimate the spatial distributions of red marrow and bone within the vertebral spongiosa.
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