# Quantitative Steady-State and Dynamic Metabolic MRI for Evaluating Patients with Glioma

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $627,567

## Abstract

Project Summary
The UCSF and GE team will address the limitations of current MR metabolic imaging approaches by developing
a package of hardware and software tools to provide improved strategies for automatic prescription of imaging
sequences to reduce operator differences, provide more extensive coverage of the lesion and enable improved
serial comparisons. We will also simplify and streamline the processing and interpretation of the resulting data
by using our open source, DICOM compatible software package, SIVIC. This will include features to facilitate
the assessment of data from serial imaging examinations, as well as expanding the algorithms available to
address the analysis of more complex types of metabolic imaging data. A further critical component of the
proposal will be to refine the imaging and tissue acquisition workflow and integration of collected quantitative
biomarkers in the clinical setting.

## Key facts

- **NIH application ID:** 10798220
- **Project number:** 5R01CA262630-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Susan M Chang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $627,567
- **Award type:** 5
- **Project period:** 2022-03-08 → 2027-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10798220

## Citation

> US National Institutes of Health, RePORTER application 10798220, Quantitative Steady-State and Dynamic Metabolic MRI for Evaluating Patients with Glioma (5R01CA262630-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10798220. Licensed CC0.

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