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random field theory; Euler characteristic; PET imaging; PET image quality
This work presents new application of the random field theory in medical imaging. Results from both integral geometry and random field theory can be used to detect locations with significantly increased radiotracer uptake in images from positron emission tomography (PET). The assumptions needed to use these results are verified on a set of real and simulated phantom images. The proposed method of detecting activation (locations with increased radiotracer concentration) is used to quantify the quality of simulated PET images. Dependence of the quality on the injection dose (amount of applied radiotracer) and patient's body parameters is estimated. It is used to derive curves of constant quality determining the injection dose needed to achieve desired quality of the resulting images. The curves are compared with the formula currently used in medical practice.
[1] Abbey, C. K., Barrett, H. H.: Human- and model-observer performance in ramp-spectrum noise: effects of regularization and object variability. J. Opt. Soc. Amer. A 18 (2001), 473-488. DOI 10.1364/JOSAA.18.000473
[2] Accorsi, R., Karp, J. S., Surti, S.: Improved dose regimen in pediatric PET. J. Nucl. Med. 51 (2010), 293-300. DOI 10.2967/jnumed.109.066332
[3] Adler, R. J.: The Geometry of Random Fields. Wiley, London 1981. MR 0611857 | Zbl 1182.60017
[4] Adler, R. J., Taylor, J. E.: Random Fields and Geometry. Springer, New York 2007. MR 2319516 | Zbl 1149.60003
[5] Boellaard, R.: FDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging: version 1.0. European J. Nucl. Med. Mol. Imaging 37 (2010), 181-200. DOI 10.1007/s00259-009-1297-4
[6] Boldyš, J.: Monte Carlo simulation of PET images for injection dose optimization. In: Proc. III ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing: VipIMAGE 2011. Taylor and Francis, London 2012.
[7] Brasse, D.: Correction methods for random coincidences in fully 3D whole-nody PET: Impact on data and image quality. J. Nucl. Med. 46 (2005), 859-867.
[8] Cao, J., Worsley, K. J.: Applications of random fields in human brain mapping. In: Spatial Statistics: Methodological Aspects and Applications. Springer Lecture Notes in Statistics 169 (2001), pp. 169-182. DOI 10.1007/978-1-4613-0147-9_8 | Zbl 1022.92021
[9] Danna, M.: Optimization of tracer injection for 3D $^{18}$F-FDG whole body (WB) PET studies using an acquisition-specific NEC (AS-NEC) curve generation. IEEE Nucl. Sci. Conf. R. (2004), 2615-2619.
[10] Everaert, H.: Optimal dose of $^{18}$F-FDG required for whole-body PET using an LSO PET camera. European J. Nucl. Med. Mol. Imaging 30 (2003), 1615-1619. DOI 10.1007/s00259-003-1317-8
[11] Gifford, H. C.: Channelized Hotelling and human observer correlation for lesion detection in hepatic SPECT imaging. J. Nucl. Med. 41 (2000), 514-521.
[12] Halpern, B. S.: Optimizing imaging protocols for overweight and obese patients: a lutetium orthosilicate PET/CT study. J. Nucl. Med. 46 (2005), 603-607.
[13] Jacobs, F.: Optimised tracer-dependent dosage cards to obtain weight-independent effective doses. European J. Nucl. Med. Mol. Imaging 32 (2005), 581-588. DOI 10.1007/s00259-004-1708-5
[14] Jan, S.: GATE: a simulation toolkit for PET and SPECT. Phys. Med. Biol. 49 (2004), 4543-4561. DOI 10.1088/0031-9155/49/19/007
[15] Mizuta, T.: NEC density and liver ROI S/N ratio for image quality control of whole-body FDG-PET scans: comparison with visual assessment. Mol. Imaging Biol. 11 (2009), 480-486. DOI 10.1007/s11307-009-0214-3
[16] Powsner, R. A., Powsner, E. R.: Essential Nuclear Medicine Physics. Second edition. Wiley-Blackwell, 2006.
[17] Székely, G. J., Rizzo, M. L.: A new test for multivariate normality. J. Multivariate Anal. 93 (2005), 58-80. DOI 10.1016/j.jmva.2003.12.002 | MR 2119764 | Zbl 1087.62070
[18] Strother, S. C., Casey, M. E., Hoffman, E. J.: Measuring PET scanner sensitivity: relating countrates to image signal-to-noise ratios using noise equivalents counts. IEEE Trans. Nucl. Sci. 37 (1990), 783-788. DOI 10.1109/23.106715
[19] Taylor, J. E., Worsley, K. J., Gosselin, F.: Maxima of discretely sampled random fields, with an application to 'bubbles'. Biometrika 94 (2007), 1-18. DOI 10.1093/biomet/asm004 | MR 2307898 | Zbl 1143.62059
[20] Thode, H. C.: Testing for Normality. Marcel Dekker, New York 2002. MR 1989476 | Zbl 1032.62040
[21] Watson, C. C.: Count rate dependence of local signal-to-noise ratio in positron emission tomography. IEEE Trans. Nucl. Sci. 51 (2004), 2670-2680. DOI 10.1109/TNS.2004.835743
[22] al., C. C. Watson et: Optimizing injected dose in clinical PET by accurately modeling the counting-rate response functions specific to individual patient scans. J. Nucl. Med. 46 (2005), 1825-1834.
[23] Watson, C. C., Newport, D., Casey, M. E.: Evaluation of simulation-based scatter correction for 3D PET cardiac imaging. IEEE Trans. Nucl. Sci. 44 (1997), 90-97. DOI 10.1109/23.554831
[24] Worsley, K. J.: A three-dimensional statistical analysis for CBF activation studies in human brain. J. Cereb. Blood Flow Metab. 12 (1992), 900-918. DOI 10.1038/jcbfm.1992.127
[25] Worsley, K. J.: Boundary corrections for the expected Euler characteristic of excursion sets of random fields, with an application to astrophysics. Adv. in Appl. Probab. 27 (1995), 943-959. DOI 10.2307/1427930 | MR 1358902 | Zbl 0836.60043
[26] Worsley, K. J.: Estimating the number of peaks in a random field using the Hadwiger characteristic of excursion sets, with applications to medical images. Ann. Statist. 23 (1995), 640-669. DOI 10.1214/aos/1176324540 | MR 1332586 | Zbl 0898.62120
[27] Worsley, K. J.: Searching scale space for activation in PET images. Hum. Brain Mapp. 4 (1996), 74-90. DOI 10.1002/(SICI)1097-0193(1996)4:1<74::AID-HBM5>3.0.CO;2-M
[28] Worsley, K. J.: Detecting changes in non-isotropic images. Hum. Brain Mapp. 8 (1999), 98-101. DOI 10.1002/(SICI)1097-0193(1999)8:2/3<98::AID-HBM5>3.0.CO;2-F
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