# Towards the automation of MR spectroscopic imaging in patients with glioblashoma

> **NIH NIH F30** · EMORY UNIVERSITY · 2020 · $47,795

## Abstract

Glioblastoma is the most common adult primary brain tumor and is highly aggressive in its disease course.
Despite advances in neurosurgical resection, radiation targeting, and chemotherapy, the prognosis remains
grim with a median survival of just 15 months. The effectiveness of current radiation therapy strategies is
severely limited by shortcomings in the imaging modalities used to develop treatment plans. Current radiation
therapy planning is mainly based on contrast-enhanced T1-weighted MRI, which identifies high grade tumors
that are immediately associated with leaky neovasculature. Although it is an excellent diagnostic tool to identify
high grade from low grade tumors, it is unable to signal occult infiltration beyond the core of the tumor. Though
many believe GBM to be an incurable disease, we believe we have identified a method for optimizing tumor
targeting that will increase the effectiveness of radiation therapy. A significant component of the current
problem in GBM therapy is the lack of treatment for non-enhancing regions that are significantly infiltrated by
neoplastic glioma cells without neovascularization. This untreated population undoubtedly leads to early
recurrence. The proposed study addresses an important step toward translating an advanced quantitative
imaging modality that complements the conventional imaging that is capable of reliably revealing glioma-
infiltrated regions for precise, personalized treatment targeting. Proton spectroscopic magnetic resonance
imaging (sMRI) is an alternative modality able to identify endogenous metabolism within tissue without the
need for exogenous contrast, and has been shown to identify the metabolic abnormalities associated with
tumor beyond the regions identified by T1-weighted MRI. The clinical integration of sMRI in patient
management has been limited due to the computational challenges of analysis of sMRI data. Two key hurdles
to be overcome are the insufficiency of filters to remove image artifacts and the necessity of quantification of
metabolic levels relative to a patient's baseline metabolism. As a result, sMRI processing requires skilled user
intervention and many hours of computational and user time. To automate this pipeline and provide clinically
useful information to oncologists, we seek to develop a software framework for the automated and expedient
processing of sMRI for use in radiation therapy planning. We will use novel advances in the fields of high
performance computing and deep learning, an approach to computation that has shattered benchmarks in
many medical and non-medical problems. Specifically, we will develop filters for removing artifacts, algorithms
for personalized diagnosis of tumor infiltration, and explore deep learning as a method to synthesize sMRI data
with anatomical and clinical metrics in a fully automated fashion. Success in the proposed work will produce a
“scanner-to-clinician” platform for quantitative, expedient, and objective analysis meth...

## Key facts

- **NIH application ID:** 9926827
- **Project number:** 5F30CA206291-05
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Saumya Gurbani
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $47,795
- **Award type:** 5
- **Project period:** 2016-06-24 → 2021-05-15

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9926827

## Citation

> US National Institutes of Health, RePORTER application 9926827, Towards the automation of MR spectroscopic imaging in patients with glioblashoma (5F30CA206291-05). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/9926827. Licensed CC0.

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