title: Simulation of tissue activity curves of (64)Cu-ATSM for sub-target volume delineation in radiotherapy.
abstract: There is much interest in positron emission tomography (PET) for measurements of regional tracer concentration in hypoxic tumour-bearing tissue, focusing on the need for accurate radiotherapy treatment planning. Generally, relevant data are taken over multiple time frames in the form of tissue activity curves (TACs), thus providing an indication of vasculature structure and geometry. This is a potential key in providing information on cellular perfusion and limited diffusion. A number of theoretical studies have attempted to describe tracer uptake in tissue cells in an effort to understand such complicated behaviour of cellular uptake and the mechanism of washout. More recently, a novel computerized reaction diffusion equation method was developed by Kelly and Brady (2006 A model to simulate tumour oxygenation and dynamic [18F]-FMISO PET data Phys. Med. Biol. 51 5859-73), where they managed to simulate the realistic dynamic TACs of (18)F-FMISO. The model was developed over a multi-step process. Here we present a refinement to the work of Kelly and Brady, such that the model allows simulation of a realistic tissue activity curve (TAC) of any hypoxia selective PET tracer, in a single step process. In this work we show particular interest in simulating the TAC of perhaps the most promising hypoxia selective tracer, (64)Cu-ATSM. In addition, we demonstrate its potential role in tumour sub-volume delineation for radiotherapy treatment planning. Simulation results have demonstrated the significant high contrast of imaging using ATSM, with a tumour to blood ratio ranging from 2.24 to 4.1.
authors.affiliation: Department of Physics, University of Surrey, Guildford, UK. e.dalah@surrey.ac.uk
authors.forename: E
authors.initials: E
authors.lastname: Dalah
authors.forename: D
authors.initials: D
authors.lastname: Bradley
authors.forename: A
authors.initials: A
authors.lastname: Nisbet
mesh_terms: D000465:Algorithms; D001808:Blood Vessels / Q000378:metabolism; D015687:Cell Hypoxia; D003198:Computer Simulation; D003287:Contrast Media / Q000493:pharmacokinetics*; D056831:Coordination Complexes; D003301:Copper Radioisotopes / Q000493:pharmacokinetics*; D004058:Diffusion; D006801:Humans; D008920:Misonidazole / Q000031:analogs & derivatives / Q000493:pharmacokinetics; D008954:Models, Biological; D009369:Neoplasms / Q000098:blood supply / Q000378:metabolism / Q000532:radiotherapy; D009942:Organometallic Compounds / Q000493:pharmacokinetics*; D010100:Oxygen / Q000378:metabolism; D049268:Positron-Emission Tomography / Q000379:methods; D011878:Radiotherapy / Q000379:methods*; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D013882:Thiosemicarbazones / Q000493:pharmacokinetics*
chemical_list: D003287:Contrast Media; D056831:Coordination Complexes; D003301:Copper Radioisotopes; D009942:Organometallic Compounds; D013882:Thiosemicarbazones; C089548:copper (II) diacetyl-di(N(4)-methylthiosemicarbazone); C031843:fluoromisonidazole; D008920:Misonidazole; D010100:Oxygen