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.

Public Health Relevance

? relevance to public health A fast magnetic resonance imaging technique to measure the regional distribution of lung water content has been used to demonstrate that cystic fibrosis subjects have a somewhat lower lung water content at functional residual capacity and a significantly higher lung water content at total lung capacity when compared with normal controls. The ratio of the lung water content at functional residual capacity to that at total lung capacity, which is significantly reduced in CF when compared to controls, may be a useful biomarker reflecting both the presence of excess fluid and air trapping. The goal of this study is develop an imaging and software package that spatially identifies statistically meaningful changes in lung disease based on the lung water content ratio when compared to a database on healthy controls and use this information to monitor lung health on an individual-by-individual basis.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL135496-04
Application #
9930129
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Lachowicz-Scroggins, Marrah Elizabeth
Project Start
2017-08-04
Project End
2021-05-31
Budget Start
2020-06-01
Budget End
2021-05-31
Support Year
4
Fiscal Year
2020
Total Cost
Indirect Cost
Name
University of California, San Diego
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
804355790
City
La Jolla
State
CA
Country
United States
Zip Code
92093