Identifying the extent of glioma tumor margins for radiation treatment planning remains a challenging task due to the infiltrative nature of these tumors and limitations in current standard imaging methods. Multiple studies including our own have demonstrated that MR spectroscopic imaging, or spectroscopic MRI (sMRI), can detect areas of infiltrating tumor with a high degree of sensitivity, and could be an essential tool in treating areas that lead to early recurrence. sMRI enables the identification of infiltrating cells that are marked by increased Choline/N-Acetylaspartate ratios, including regions that are not detectable by standard MRI and normally left untreated. Therefore, sMRI shows considerable promise for improving the efficacy of radiation treatment and significantly delay recurrence. However, technological improvements are needed before sMRI can be broadly adopted for clinical use. This study will achieve this goal by leveraging diverse areas of expertise at four research sites to engineer and validate technological improvements needed to improve sMRI acquisition, analysis, and clinical integration. These improvements will include new magnet shimming technology to increase the spatial coverage of sMRI to whole-brain volumes; updated rapid and motion-robust sMRI method that incorporates compressed sensing to both increase spatial resolution and decrease acquisition times; and new processing, display, and analysis methods that will present metabolite maps in an efficient manner with a clinician-friendly web-based interface that enables integration with radiation treatment planning software systems. The value and efficacy of the technological developments will be validated in a clinical study at the participating sites that will incorporate sMRI into RT planning for glioblastoma, using an escalated dose for regions of significantly increased Cho/NAA. The completion of this study will provide robust sMRI acquisition methods and software tools that will be suitable for clinical use.
|Effective start/end date||9/22/18 → 7/31/21|
- National Institutes of Health: $813,062.00