title: The simplified reference tissue model: model assumption violations and their impact on binding potential.
abstract: Reference tissue models have gained significant traction over the last two decades as the methods of choice for the quantification of brain positron emission tomography data because they balance quantitative accuracy with less invasive procedures. The principal advantage is the elimination of the need to perform arterial cannulation of the subject to measure blood and metabolite concentrations for input function generation. In particular, the simplified reference tissue model (SRTM) has been widely adopted as it uses a simplified model configuration with only three parameters that typically produces good fits to the kinetic data and a stable parameter estimation process. However, the model's simplicity and its ability to generate good fits to the data, even when the model assumptions are not met, can lead to misplaced confidence in binding potential (BPND) estimates. Computer simulation were used to study the bias introduced in BPND estimates as a consequence of violating each of the four core SRTM model assumptions. Violation of each model assumption led to bias in BPND (both over and underestimation). Careful assessment of the bias in SRTM BPND should be performed for new tracers and applications so that an appropriate decision about its applicability can be made.
authors.affiliation: 1] Imanova, Centre for Imaging Sciences, Imperial College London, Hammersmith Hospital, London, UK [2] Imaging, Merck Research Laboratories, West Point, Pennsylvania, USA.
authors.forename: Cristian A
authors.initials: CA
authors.lastname: Salinas
authors.affiliation: Imanova, Centre for Imaging Sciences, Imperial College London, Hammersmith Hospital, London, UK.
authors.forename: Graham E
authors.initials: GE
authors.lastname: Searle
authors.affiliation: 1] Imanova, Centre for Imaging Sciences, Imperial College London, Hammersmith Hospital, London, UK [2] Division of Brain Sciences, Department of Medicine, Imperial College London, London, UK [3] Department of Engineering Science, University of Oxford, Oxford, UK.
authors.forename: Roger N
authors.initials: RN
authors.lastname: Gunn
mesh_terms: D000818:Animals; D001921:Brain / Q000098:blood supply* / Q000000981:diagnostic imaging; D003198:Computer Simulation; D006801:Humans; D008955:Models, Cardiovascular; D049268:Positron-Emission Tomography / Q000379:methods; D011859:Radiography