Optimal Setup and Parameters of Diffusion-Weighted Magnetic Resonance Imaging for Translational Evaluation of a Tumor Progression Model for Soft Tissue Sarcomas.

PURPOSE

Defining a microscopic tumor infiltration boundary is critical to the success of radiation therapy. Currently, radiation oncologists use margins to geometrically expand the visible tumor for radiation treatment planning in soft tissue sarcomas (STS). Image-based models of tumor progression would be critical to personalize the treatment radiation field to the pattern of sarcoma spread. Evaluation of these models is necessary to demonstrate feasibility in the clinical setting. This study presents an imaging protocol for the preclinical evaluation of a tumor progression model in extremity STS.

METHODS AND MATERIALS

We recruited 7 healthy volunteers and acquired diffusion-weighted magnetic resonance imaging (DW-MRI) images of the thigh on a magnetic resonance imaging scanner used for imaging cancer patients in a radiation oncology department. We developed a protocol that includes positioning the patient, configuring the radiofrequency coils, and setting the DW-MRI sequence parameters. To find the optimal parameter configuration, the image signal-to-noise ratio (SNR) and the directional variability (DV) of the principal eigenvector of the diffusion tensor were calculated.

RESULTS

The mean SNR across all trials and 12 thigh muscles was 41, with a range of 12 to 72. The mean DV was 13° and ranged from 11° to 23°. The longest scan time was 22 minutes and 58 seconds, and the shortest was 11 minutes and 46 seconds. For the high-resolution image with a voxel volume of 1.3 × 1.3 × 6 mm and 38 slices, the optimal parameters were found to be a repetition time of 8000 ms, 12 signal averages, and 6 gradient directions. This configuration resulted in a scan time of 11 minutes and 46 seconds, an SNR of 34, and a DV of 13°.

CONCLUSIONS

A DW-MRI scan duration acceptable for imaging cancer patients was achieved with an image quality suitable for reproducible modeling of tumor infiltration. The developed protocol can be used for preclinical evaluation in STS patients.