We implemented a tracer-kinetic model within a Bayesian framework which infers full posterior probability distributions for parameter estimates. We validate our Bayesian model using a digital reference object and compare it to a standard non-linear least squares approach. Furthermore, we use this approach to obtain pharmacokinetic parameter distributions during the course of a therapy for breast cancer DCE-MRI data, and we demonstrate how Bayesian posterior distributions can be utilized to assess treatment response.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords