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Multivariate Regularized Newton and Levenberg-Marquardt methods: a comparison on synthetic data of tumor hypoxia in a kinetic framework Cover

Multivariate Regularized Newton and Levenberg-Marquardt methods: a comparison on synthetic data of tumor hypoxia in a kinetic framework

Open Access
|May 2019

References

  1. 1. K. W-j, J. S. Rasey, M. L. Evans, J. R. Grierson, T. K. Lewellen, M. M. K. K. A. Graham, and G. T. W, Imaging of hypoxia in human tumors with [f-18]fluoromisonidazole int, J. Radiat. Oncol. Biol. Phys., vol. 22, pp. 199–212, 1992.10.1016/0360-3016(92)91001-4
  2. 2. J. S. Rasey, K. W-j, M. L. Evans, L. M. Peterson, T. K. G. M. M. Lewellen, and K. K. A, Quantifying regional hypoxia in human tumors with positron emission tomography of [18f]fluoromisonidazole: a pretherapystudy of 37 patients int, J. Radiat. Oncol. Biol. Phys., vol. 36, pp. 417–28, 1996.10.1016/S0360-3016(96)00325-2
  3. 3. M. Nordsmark, M. Overgaard, and J. Overgaard, Pretreatment oxygenation predicts radiation response in advanced squamous cell carcinoma of the head and neck radiother, Oncol., vol. 41, pp. 31–9, 1996.10.1016/S0167-8140(96)91811-3
  4. 4. M. Nordsmark and J. Overgaard, A confirmatory prognostic study on oxygenation status and loco-regional control in advanced head and neck squamous cell carcinoma treated by radiation therapy radiother, Oncol., vol. 57, pp. 39–43, 2000.10.1016/S0167-8140(00)00223-1
  5. 5. J. J. G. M. M. Casciari and R. J. S, A modeling approach for quantifying tumor hypoxia with [f-18]fluoromisonidazole pet time-activity data, Phys. Med., vol. 22, pp. 1127–39, 1995.10.1118/1.597506
  6. 6. D. Thorwarth, S. M. Eschmann, F. Paulsen, and M. Alber, A kinetic model for dynamic [18f]-fmiso pet data to analyse tumour hypoxia, Phys. Med., pp. 2209–24, 2005.10.1088/0031-9155/50/10/002
  7. 7. F. Delbary, S. Garbarino, and V. Vivaldi, Compartmental analysis of dynamic nuclear medicine data: models and identifiability, Inverse Problems, vol. 32, no. 12, p. 125010, 2016.10.1088/0266-5611/32/12/125010
  8. 8. N. M. Alpert, R. D. Badgaiyan, and E. F. A. J. Livni, A novel method for noninvasive detection of neuromodulatory changes in specific neurotransmitter systems, Neuroimage, vol. 19, pp. 1049–60, 2003.10.1016/S1053-8119(03)00186-1
  9. 9. F. Delbary and S. Garbarino, Compartmental analysis of dynamic nuclear medicine data: regularization procedure and application to physiology arxiv, Inverse Problems in Science and Engineering, vol. 0, pp. 1–19, 2019.
  10. 10. H. M. Hudson and R. S. Larkin, Accelerated image reconstruction ordered subsets of projection data,, IEEE Trans Med Imaging, vol. 13, pp. 601–9, 1994.10.1109/42.36310818218538
  11. 11. T. Sasser, Preclinical imaging: improving translational power in oncology drug discovery, Drug Discovery, vol. 1, 2016.
  12. 12. S. R. Golish, J. D. Hove, and H. R. G. S. S. Schelbert, A fast nonlinear method for parametric imaging of myocardial perfusion by dynamic 13n- ammonia pet, J. Nucl. Med, vol. 42, pp. 924–31, 2001.
Language: English
Page range: 47 - 53
Submitted on: Nov 20, 2016
Accepted on: Jul 24, 2017
Published on: May 11, 2019
Published by: Italian Society for Applied and Industrial Mathemathics
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2019 Sara Garbarino, Giacomo Caviglia, published by Italian Society for Applied and Industrial Mathemathics
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.