Technical development and In Silico implementation of SyntheticMR in head and neck adaptive radiation therapy: A prospective R-IDEAL stage 0/1 technology development report.

BACKGROUND

SyntheticMR has the capability of generating quantitative relaxometry maps and synthetic contrast-weighted MRI images in rapid acquisition times. Recently, it has gained attention in the diagnostic community, however, no studies have investigated its feasibility on the MR-Simulation or MR-Linac systems, especially as part of the head and neck adaptive radiation oncology workflow.

PURPOSE

Demonstrating its feasibility will facilitate rapid quantitative biomarker extraction, which can be leveraged to guide adaptive radiation therapy decision making.

METHODS

Two phantoms, two healthy volunteers, and one patient were scanned using SyntheticMR on the MR-Simulation and MR-Linac devices with scan times between 4 to 6 min. The correlation between measured and reference quantitative T1, T2, and PD values were determined across clinical ranges in the phantom. Distortion was also studied. Contours of head and neck organs-at-risk (OAR) were drawn and applied to extract T1, T2, and PD. These values were plotted against each other, clusters were computed, and their separability significance was determined to evaluate SyntheticMR for differentiating tumor and normal tissue.

RESULTS

The Lin's Concordance Correlation Coefficient between the measured and phantom reference values was above 0.97 for both the MR-Sim and MR-Linac. No significant levels of distortion were measured. The mean bias between the measured and phantom reference values across repeated scans was below 6% for T1, 11% for T2, and 6% for PD for both the MR-Sim and MR-Linac. For T1 versus T2 and T1 versus PD, the GTV contour exhibited perfect purity against neighboring OARs, while being 0.7 for T2 versus PD. All cluster significance levels between the GTV and the nearest OAR, the tongue, using the SigClust method was p < 0.001.

CONCLUSIONS

The technical feasibility of SyntheticMR was confirmed. Application of this technique to the head and neck adaptive radiation therapy workflow can enrich the current quantitative biomarker landscape.