Vicki Huang1, Dana Albon2, Lucy Gettle2, Kun Qing1, Nicholas Tustison1, Yun Shim2, John Mugler1, James Patrie3, and Jaime Mata1
1Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, United States, 2Medicine, University of Virginia, Charlottesville, VA, United States, 3Public Health, University of Virginia, Charlottesville, VA, United States
Synopsis
Results demonstrate sensitivity to early physiologic changes in subjects with cystic fibrosis (CF) using 3D single breath-hold chemical shift imaging (3D-SBCSI). 3D-SBCSI is a technique that uses MRI and hyperpolarized Xenon-129, an inert, non-radioactive gas, to assess lung physiology by detecting Xe-129 in three compartments in the lungs: airspaces, tissue parenchyma and plasma, and red-blood-cells. Results of 3D-SBCSI were compared to pulmonary function test results, Xe-129 ventilation images, blood panels and showed early physiologic trends in mild subjects that progressed on severe CF subjects. 3D-SBCSI could be used to monitor treatment responses and disease progression before symptoms become clinically apparent.
Introduction
In current clinical practice, early stages of
pulmonary pathophysiology are difficult to monitor in cystic fibrosis (CF)
without using invasive procedures or radiation. In contrast, 3D single breath-hold chemical
shift imaging (3D-SBCSI)1,2 which uses MRI and hyperpolarized
Xenon-129, an inert and non-radioactive gas, is sensitive to early CF
progression. Here we present a
preliminary analysis of results from a clinical trial of 3D-SBCSI in cystic
fibrosis.Methods
Twenty-one healthy, 12 mild CF, and 6 severe CF
subjects (n=39) participated in the study, and were imaged using a 1.5T MR
scanner (Avanto, Siemens). For MR
imaging, subjects laid supine on the scanner table, inhaled a volume (1/3 FVC)
of enriched Xe-129 up to 1000mL and the remainder (if any) N2, and
held their breath throughout two ~7-second acquisitions. A vest-shaped RF coil
tuned to the Xe-129 frequency (Clinical MR Solutions) was used. Xe-129 was
polarized to ~30% using a commercial polarizer (Model 9820, Polarean). The resulting Xe-129 ventilation, Xe-129 3D-SBCSI,
and proton images were post-processed3. The percentage of ventilation defects was
calculated from the Xe-129 ventilation and corresponding proton images, and,
from the 3D-SBCSI acquisition, three peaks in the Xe-129 spectrum were
identified, corresponding to alveolar gas, tissue, and red blood1,2,4 These peaks
were analyzed on a voxel-by-voxel basis, and results were compared to those
from pulmonary function tests (PFT) and blood panels obtained in each subject.Results
Severe CF subjects had a larger percentage of
ventilation defects (49.2±3.9% of total lung volume) than mild CF (34.0±14.4%) (p=0.07)
and healthy subjects (10.5±7.4%) (p<0.05)(Fig. 1). A statistically
significant difference (p<0.05) in the percentage of ventilation defects was
also found between mild CF and healthy subjects. The average tissue/RBC peak ratios were 3.36±0.7
for severe CF subjects, 3.1±0.6 for mild CF, and 2.7±0.5 for healthy subjects
(p<0.05) (Figs. 2, 3). Tissue/RBC, RBC/gas, and tissue/gas ratios correlated
well with iron concentration and transferrin saturation in blood (|R|>0.70).Discussion
Trends in the tissue/RBC peak ratio showed good
correlation with ventilation defects and revealed localized areas of
ventilation/perfusion mismatch. Two severe CF subjects (S1 and S2) had similar
FEV1 but different iron levels; with lower iron, tissue/RBC was higher, tissue/gas
was about the same, and RBC/gas was lower (Fig. 4). We observed a larger shift
in the RBC peak relative to the gas peak as the disease became more severe and
iron level decreased, indicating a reduced capacity to bind oxygen.5,6 The RBC T2*
relaxation time was shorter in severe disease which may be linked to acidosis.7Conclusion
3D-SBCSI parameters indicate underlying
impairment in gas-exchange and reveal specific physiologic evidence of disease
progression on a voxel-by-voxel basis. 3D-SBCSI
can detect early pulmonary physiologic changes in CF subjects that could be
used to monitor treatment responses and disease progression before symptoms
become clinically apparent.Acknowledgements
This work was funded by NIH grants
R01-CA172595 and S10-OD018079.References
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