[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]Search in Google Scholar
[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]Search in Google Scholar
[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]Search in Google Scholar
[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]Search in Google Scholar
[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]Search in Google Scholar
[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]Search in Google Scholar
[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]Search in Google Scholar
[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]Search in Google Scholar
[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.]Search in Google Scholar
[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]Search in Google Scholar
[11. T. Sasser, Preclinical imaging: improving translational power in oncology drug discovery, Drug Discovery, vol. 1, 2016.]Search in Google Scholar
[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.]Search in Google Scholar