Quantification of Spatial Ventilation Defect Sparsity in Hyperpolarized Gas Magnetic Resonance Imaging of Lungs...

Area of Science:

• Pulmonary Medicine
• Medical Imaging
• Quantitative MRI

Background:

• Hyperpolarized gas (HPG) magnetic resonance (MR) imaging quantifies pulmonary defects using ventilation defect percentage (VDP).
• VDP provides overall defect quantification but lacks spatial distribution information.
• Understanding defect spatial patterns is crucial for diagnosing and managing lung diseases.

Purpose of the Study:

• To develop and validate a novel method for quantifying the focality/sparseness of ventilation defects in HPG lung MR images.
• To introduce a "cluster index" (CI) algorithm for assessing the spatial distribution of lung defects.
• To evaluate the correlation of CI with VDP and FEV1pp across different respiratory diseases.

Main Methods:

• Developed a "cluster index" (CI) algorithm to quantify ventilation defect spatial distribution.
• Assessed the CI algorithm using synthesized defect clusters and real 3D lung volume defects.
• Calculated CI and VDP for 56 subjects (asthma, COPD, CF) across four different MR imaging studies/sequences.
• Correlated CI with VDP and FEV1% predicted (FEV1pp) using Pearson correlation and linear regression.

Main Results:

• The CI algorithm visually represented defect focality accurately.
• Significant correlations were found between CI and VDP for asthma (r=0.60), COPD (r=0.79), and CF (r=0.84).
• A significant negative correlation was observed between CI and FEV1pp (r=-0.47).
• The CI/VDP ratio differed significantly between asthma/CF and CF/COPD, indicating distinct spatial defect patterns.

Conclusions:

• The developed cluster index (CI) effectively quantifies the spatial distribution of ventilation defects in HPG MR imaging.
• CI provides complementary information to VDP, enhancing the characterization of lung disease.
• This volumetric quantification method offers insights into defect cluster size, which VDP alone cannot provide.