# Lung Ventilation Mapping Based On Biomechanical Motion Modeling for Radiation Therapy

> **NIH NIH F31** · DUKE UNIVERSITY · 2020 · $26,540

## Abstract

ABSTRACT
Radiation-induced pulmonary toxicity poses a serious challenge and limiting factor in delivering a sufficient
amount of dose to eradicate thoracic tumors without compromising lung function. Functional avoidance radiation
therapy (RT) using lung ventilation mapping techniques would allow for preferential avoidance of functional lung
tissue during radiotherapy and reduce RT-induced lung injuries. Conventional methods for lung ventilation
imaging include gamma camera scintigraphy and positron emission tomography scan after inhaling a gaseous
radionuclide. Recently, a new method has been proposed in which deformable image registration (DIR) is
performed on a pair of anatomical lung images at different respiratory phases to obtain the displacement vector
field (DVF) between both phases and generate a lung ventilation map. The long-term goal of this application is
to develop an accurate DIR-based lung ventilation mapping technique and apply it to advanced radiotherapy of
lung cancer. This new DIR-based method is advantageous in its high image resolutions and robust processing,
making it a more feasible option for implementation into the clinical workflow. However, current DIR-based lung
ventilation methods have been largely hampered due to two major deficiencies: 1) current DIR algorithms are
morphologically based, lacking sufficient physiological realism and thus resulting in erroneous ventilation
measurements; and 2) there is a lack of validation of DIR-based lung ventilation calculations against clinical
ground truth. The objective of the proposed research is to develop and evaluate a highly efficient and robust
biomechanically-based hybrid DIR method for lung ventilation applications. The central hypothesis of this
application is that the incorporation of biomechanical motion information into the DIR algorithm improves the
accuracy of the DIR-based lung ventilation calculation. The central hypothesis will be tested by pursuing two
specific aims: 1) Improve the efficiency and accuracy of a DIR algorithm through automatic vessel tree matching
for lung ventilation mapping; and 2) Evaluate the performance of the improved DIR algorithm for lung ventilation
applications using an existing multi-institutional lung ventilation database. Outcome of the proposed research
will yield an improved DIR algorithm with high efficiency and accuracy for accurate lung ventilation mapping.
Once fully developed and validated, it can be used for functional-based treatment planning and adaptive
radiotherapy to improve the outcome of lung cancer treatments. The co-sponsors will foster an educational and
collaborative environment within their multidisciplinary laboratory and assist in the completion of benchmarks.
The candidate will be responsible for designing and carrying out the methods described, along with her sponsors’
guidance on the feasibility and significance of the approaches. Duke University offers a training environment with
the opportunity to consult a...

## Key facts

- **NIH application ID:** 9811782
- **Project number:** 5F31CA224980-02
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Isabella Duarte
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $26,540
- **Award type:** 5
- **Project period:** 2019-02-01 → 2020-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9811782

## Citation

> US National Institutes of Health, RePORTER application 9811782, Lung Ventilation Mapping Based On Biomechanical Motion Modeling for Radiation Therapy (5F31CA224980-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9811782. Licensed CC0.

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