# Spatial identification of lung abnormalities in CF via a probabilistic library of MRI measures of lung water density

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2020 · $664,412

## Abstract

Project Summary
 Goal: The long-term goal of our research is to establish, evaluate and translate a non-invasive MRI
technique to spatially quantify and monitor lung abnormalities in patients with cystic fibrosis (CF).
 Background: Our group has recently used a fast gradient echo magnetic resonance imaging (MRI)
technique to quantify the regional distribution of lung water content in adults with cystic fibrosis (CF). We have
shown that CF subjects have a somewhat lower lung water content at functional residual capacity (FRC) and a
significantly higher lung water density at total lung capacity (TLC) when compared with normal controls. We
interpret these findings to reflect the presence of excess fluid from excess secretions and other pathology
leading to increased water content at TLC and also of air trapping in the lung periphery resulting in decreased
water content at FRC. Our preliminary data shows that the ratio of lung water content at FRC to that at TLC,
the fractional lung water density (FLD) ratio, was significantly smaller in CF patients than in controls. Such
information is important as it may provide insights into the disease pathophysiology and may also serve as a
biomarker to evaluate disease severity and progression.
 Hypothesis: We therefore hypothesize that when compared to a probabilistic library of the spatial
distribution of the FLD ratio in healthy controls, the changes in the FLD ratio will spatially identify lung
abnormalities in CF and will be in statistical agreement with measures obtained by CT. We further hypothesize
that FLD ratio will have the sensitivity to follow dynamic changes in CF, and thus providing a means to monitor
response to therapy.
 Aims: 1. Establish a probabilistic library of the spatial distribution of the FLD ratio in healthy subjects aged
18-50. 2. Evaluate our probabilistic library in stable CF patients over a wide range of disease severity and
correlate with clinical measures. 3. Translate our approach to the clinic by evaluating CF subjects at the onset
of a severe exacerbation and post exacerbation after therapy. We predict that CF abnormalities will be
identified by a significantly reduced FLD ratio when compared to the library and that these values will indicates
changes in lung health.
 Impact: The proposed radiation dose-free imaging and analysis tools have been designed for application
on any clinical1.5T scanner without the addition of special hardware or trained personnel. We believe the
quantitative density MRI method described may fill a critical need for an objective measure of CF disease
severity that can be used repeatedly to spatially assess disease severity and response to therapy. Because of
the absence of any ionizing radiation, MRI is a very attractive modality for frequent longitudinal follow-up.

## Key facts

- **NIH application ID:** 9930129
- **Project number:** 5R01HL135496-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Rebecca J Theilmann
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $664,412
- **Award type:** 5
- **Project period:** 2017-08-04 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9930129

## Citation

> US National Institutes of Health, RePORTER application 9930129, Spatial identification of lung abnormalities in CF via a probabilistic library of MRI measures of lung water density (5R01HL135496-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9930129. Licensed CC0.

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