# Personalized Motion Management for Truly 4D Lung Radiotherapy

> **NIH NIH R01** · UNIVERSITY OF MARYLAND BALTIMORE · 2021 · $658,497

## Abstract

It is well-recognized that unanticipated respiration-induced motion can result in significant errors in
planned vs delivered dose in thoracic radiotherapy (RT), resulting in local regional failure and/or increased
radiation-induced toxicity. In this proposal, we build upon our previous motion management research and aim
to overcome the limitations of current motion management strategies, which tend to underrepresent both the
extent and the spatiotemporal complexity of respiratory motion. Our overall premise is that, as our field adopts
increasingly more potent forms of RT, real-time single-point monitoring needs to be replaced by real-time
volumetric monitoring to capture complex motion. Recently available integrated magnetic resonance imaging
(MRI)+Linac systems aim to address the limitations of current conventional solutions. However, the high cost
and complexity of these systems, as well as engineering and technological challenges, have proven to be
substantial barriers to their widespread clinical adoption (less than 1% of the total US install base for linacs).
 To address this unmet clinical need, we form an academic-industrial partnership to investigate and
develop a novel in-room real-time motion management solution for lung RT that combines 4DMRI and 4DCT
(4D=3D+time). In Aim 1, we develop and investigate rapid 4DMRI techniques. In Aim 2, we merge the
volumetric motion information derived from 4DMRI and 4DCT to create a patient-specific, multi-cycle motion
model that incorporates the geometric fidelity and electron density information from CT with the soft-tissue
contrast and dose-free, long-term monitoring from MRI. This model is parameterized by the spatial positions of
MRI-compatible electromagnetic (EM) sensors placed on the thoracoabdominal surface of the patient. By
knowing the position of these sensors at any given time point, we can estimate the corresponding position of
each voxel within the irradiated volume. At each treatment fraction, the model is rebuilt using in-room kV
fluoroscopy prior to delivery to account for inter-fraction (day-to-day) changes in external-internal
correspondence and updated using kV fluoro during dose delivery to account for intra-fraction changes. In Aim
3, we develop two identical preclinical prototype systems (EndoScoutRT) and form end-user teams tasked with
formulating clinical workflows, quality assurance guidelines, and strategies for clinical translation. In Aim 4, we
perform end-user evaluation of the prototype systems by conducting a prospective non-interventional clinical
study in 44 lung cancer patients at two institutions. We compare the performance of our model-based motion
management to current standard-of-care and MRI+Linac based real-time motion management. Our team has
extensive expertise in clinical study design, image-guided RT, rapid MRI, and real-time motion management.
We anticipate that the successful clinical translation of this approach (beyond the current scope) will enable
sa...

## Key facts

- **NIH application ID:** 10274050
- **Project number:** 1R01CA262017-01
- **Recipient organization:** UNIVERSITY OF MARYLAND BALTIMORE
- **Principal Investigator:** RAO P GULLAPALLI
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $658,497
- **Award type:** 1
- **Project period:** 2021-07-16 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10274050

## Citation

> US National Institutes of Health, RePORTER application 10274050, Personalized Motion Management for Truly 4D Lung Radiotherapy (1R01CA262017-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10274050. Licensed CC0.

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