# Real-time MRI-guided adaptive radiotherapy of unresectable pancreatic cancer

> **NIH NIH R01** · SLOAN-KETTERING INST CAN RESEARCH · 2021 · $640,178

## Abstract

PROJECT SUMMARY
Pancreatic cancer has the highest mortality rate of all cancers, with a 5-year survival rate of only 9%. Surgery
still represents the only curative treatment option, though less than 20% of patients are candidates for resection.
Approximately 30-40% of patients present with locally advanced unresectable tumors with no significant chance
of long-term survival through standard treatments. The use of ablative radiation doses (biologically equivalent
doses of 100Gy) produces results that are comparable to surgical resection in patients with inferior prognostic
features. However, organ motion, due to respiratory motion, must be managed to minimize toxicity in the
gastrointestinal tract. In this project, we will develop novel real-time volumetric MRI technology that can guide
radiotherapy to enable the use of ablative doses with minimal risk. Our technique, called MR SIGnature Matching
(MRSIGMA), pre-learns 3D motion states and assigns unique motion signatures during an offline learning phase
and performs fast signature acquisition and matching during an online matching phase. We have demonstrated
real-time tracking of liver tumors with an imaging latency (acquisition plus reconstruction) of about 250 ms using
MRSIGMA. We will collaborate with Elekta to implement MRSIGMA on the Unity MR-Linac system and to link
the output of MRSIGMA with the multileaf collimator (MLC) system to enable the radiation beam to track the 3D
position and shape of the moving tumor in real-time. Specific Aims are as follows:
1. Develop deep learning reconstruction of undersampled dynamic MRI data for rapid motion database
 generation during offline learning and adaptation during online matching
 a. Develop a convolutional neural network for rapid reconstruction of motion-resolved data (< 10 seconds)
 b. Detect anatomical changes, such as motion baseline drifts, and adapt the motion database accordingly
 c. Perform initial validation on a dynamic MRI phantom and ten volunteers
2. Validate the potential of MRSIGMA for real-time volumetric tumor motion imaging on fifty patients with locally
 advanced unresectable pancreatic cancer
 a. Accuracy hypothesis: real-time MRSIGMA is noninferior to a non-real-time XDGRASP reference
 b. Reproducibility hypothesis: two MRSIGMA scans present equivalent real-time imaging performance
3. Develop and validate on dynamic phantoms the proposed MRSIGMA-guided MLC tracking in collaboration
 with Elekta
 a. Develop software to control the MLC with the output of MRSIGMA
 b. Evaluate tracking latency, geometric error, reproducibility and dosimetric accuracy

## Key facts

- **NIH application ID:** 10299267
- **Project number:** 1R01CA255661-01A1
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** Ricardo Otazo
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $640,178
- **Award type:** 1
- **Project period:** 2021-08-13 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10299267

## Citation

> US National Institutes of Health, RePORTER application 10299267, Real-time MRI-guided adaptive radiotherapy of unresectable pancreatic cancer (1R01CA255661-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10299267. Licensed CC0.

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