Protocol optimization for MRI studies in radiation oncology: I. phantoms

MRI plays an increasing role at different stages in the radiation oncology workflow, including tumor detection and delineation, the prediction of margins, fiducial marker detection, the estimation of organ and tumor motion, biofunctional avoidance and treatment response assessment using quantitative imaging biomarkers. Where conventional anatomical contrast-weighted MRI scans are a first-line strategy used for tumor detection and delineation, quantitative biofunctional MRI can provide predictors of tumor response or regional information on functional organs at risk.Unfortunately, the implementation of advanced quantitative MRI (qMRI) techniques is prone to bias and uncertainties as a result of an over-reliance on vendor provided pulse sequences, image processing and analysis methods that have not been designed in the light of imaging biomarkers for radiotherapy guidance.Bias in quantitative MRI measurements can originate from the implementation of oversimplified physical models, unrecognized image artefacts and poor parametric fitting methods. Moreover, inconsistency in the use of imaging parameters and image post-processing methods across different centers results in a large variability in reported quantitative parameters. Much bias slips under the radar of both the medical physicist and MRI technologists because of the intrinsic variability between different human subjects which may remain undetected even when the imaging protocol is optimized with commercial quality assurance (QA) phantoms. Adequate pulse sequence optimization, harmonization of protocols and QA is essential to safeguard the reliability and robustness of the MRI protocols and can provide objective measures of uncertainty in quantitative parametric MRI maps. The increasing introduction of compressed sensing and data driven approaches in MRI image reconstruction demands for a different approach in imaging QA. In this first part, in-house-built phantoms are discussed that have proven useful in the optimization and QA of MRI sequences, protocols and post-processing methods.