# High-Precision Lung Radiotherapy by Intra-treatment Dynamic Cone-beam CT imaging and Dosimetry-guided Plan Adaptation

> **NIH NIH R01** · UT SOUTHWESTERN MEDICAL CENTER · 2024 · $497,982

## Abstract

PROJECT SUMMARY
 The recent advancement of stereotactic body radiation therapy (SBRT) enables highly focused dose
delivery to tumors while sparing surrounding normal tissues. Both past and emerging clinical evidence has
strengthened the role of radiotherapy in lung cancer management, and SBRT is considered the standard of
care for many diagnoses. However, radiation-induced toxicity, especially for cases with centrally located lung
tumors, poses a lingering challenge to lung SBRT. The respiration-induced motion of lung tumors and
surrounding organs-at-risk (OARs) introduces substantial uncertainties to the delivery accuracy of lung SBRT,
causing under-dosing to tumors and over-irradiation to surrounding OARs. The current motion management
techniques, including internal-target-volume (ITV)-based treatment, respiratory phase gating, breath hold, and
motion tracking, all suffer from various sources of uncertainty and inaccuracy, potentially resulting in large dose
deviations that miss the tumor and damage normal tissues. Obtaining intra-treatment tumor and OAR
motion/deformation, and using such information to derive the actually delivered doses to optimize remaining
radiotherapy treatments, will systematically address the intra-treatment motion challenge. However,
reconstructing intra-treatment dynamic and volumetric images for motion/deformation tracking remains an
unmet clinical need, mostly due to the challenging spatiotemporal inverse problem of reconstructing volumetric
images from extremely under-sampled signals. In addition, currently, there are no existing techniques and
workflows that use the actually delivered dose to optimize future SBRT plans and deliveries. The overarching
goal of this project is to develop an intra-treatment dynamic imaging and plan adaptation (IDIPA) system,
which is composed of two sub-systems, spatial and temporal implicit neural representation (STINR) and
dosimetry-guided plan adaptation (DGPA). STINR solves dynamic cone-beam CT (CBCT) images and intra-
treatment deformation vector fields (DVFs) from x-ray projections with each x-ray projection corresponding to a
dynamic CBCT volume and a DVF. The solved dynamic CBCTs and DVFs will then be used by DGPA for
treatment dose calculation, dose accumulation, and dosimetry-guided plan adaptation. The STINR sub-system
uses an Artificial Intelligence-driven method to address the substantial challenge of dynamic CBCT
reconstruction. The DGPA sub-system features the first closed-loop, dosimetry-guided optimization framework
that uses delivered doses to adapt the following plans to ensure the treatment doses will be delivered to where
it is intended. We have three Specific Aims for this project: 1) Develop and optimize the dynamic CBCT
imaging sub-system (STINR), 2) Develop and optimize the dosimetry-guided plan adaptation sub-system
(DGPA), and 3). Evaluate the overall IDIPA system via a clinical study. The success of the project will result in
the first end-to-end system to i...

## Key facts

- **NIH application ID:** 10880487
- **Project number:** 1R01EB034691-01A1
- **Recipient organization:** UT SOUTHWESTERN MEDICAL CENTER
- **Principal Investigator:** Weiguo Lu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $497,982
- **Award type:** 1
- **Project period:** 2024-06-01 → 2028-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10880487

## Citation

> US National Institutes of Health, RePORTER application 10880487, High-Precision Lung Radiotherapy by Intra-treatment Dynamic Cone-beam CT imaging and Dosimetry-guided Plan Adaptation (1R01EB034691-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10880487. Licensed CC0.

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