title: Development of a mathematical model to estimate intra-tumor oxygen concentrations through multi-parametric imaging.
abstract: BACKGROUND
  Tumor hypoxia is involved in every stage of solid tumor development: formation, progression, metastasis, and apoptosis. Two types of hypoxia exist in tumors-chronic hypoxia and acute hypoxia. Recent studies indicate that the regional hypoxia kinetics is closely linked to metastasis and therapeutic responses, but regional hypoxia kinetics is hard to measure. We propose a novel approach to determine the local pO2by fusing the parameters obtained from in vivo functional imaging through the use of a modified multivariate Krogh model.
  
  
  METHODS
  To test our idea and its potential to translate into an in vivo setting through the use of existing imaging techniques, simulation studies were performed comparing the local partial oxygen pressure (pO2) from the proposed multivariate image fusion model to the referenced pO2derived by Green's function, which considers the contribution from every vessel segment of an entire three-dimensional tumor vasculature to profile tumor oxygen with high spatial resolution.
  
  
  RESULTS
  pO2derived from our fusion approach were close to the referenced pO2with regression slope near 1.0 and an r2higher than 0.8 if the voxel size (or the spatial resolution set by functional imaging modality) was less than 200 μm. The simulation also showed that the metabolic rate, blood perfusion, and hemoglobin concentration were dominant factors in tissue oxygenation. The impact of the measurement error of functional imaging to the pO2precision and accuracy was simulated. A Gaussian error function with FWHM equal to 20 % of blood perfusion or fractional vascular volume measurement contributed to average 7 % statistical error in pO2.
  
  
  CONCLUSIONS
  The simulation results indicate that the fusion of multiple parametric maps through the biophysically derived mathematical models can monitor the intra-tumor spatial variations of hypoxia in tumors with existing imaging methods, and the potential to further investigate different forms of hypoxia, such as chronic and acute hypoxia, in response to cancer therapies.
authors.affiliation: School of Health Science, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN, 47907, USA.
authors.forename: Chung-Wein
authors.initials: CW
authors.lastname: Lee
authors.affiliation: School of Health Science, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN, 47907, USA. kstantz@purdue.edu.
authors.forename: Keith M
authors.initials: KM
authors.lastname: Stantz
mesh_terms: D000818:Animals; D015687:Cell Hypoxia; D006439:Hemodynamics; D007091:Image Processing, Computer-Assisted; D055806:Microvessels / Q000503:physiopathology; D008954:Models, Biological; D057054:Molecular Imaging; D009369:Neoplasms / Q000000981:diagnostic imaging / Q000378:metabolism* / Q000473:pathology* / Q000503:physiopathology; D010100:Oxygen / Q000378:metabolism*
chemical_list: D010100:Oxygen
keywords: Dynamic contrast enhanced computed tomography; In vivo functional imaging; Multi-variate Krogh model; Photoacoustic spectroscopy