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Session: CAD, Radiomics, and Quantitative Applications of X-Ray Imaging and CT [Return to Session]

Quantifying COPD Disease Severity with CT-Derived Perfusion Imaging

A Nowacki1*, G Nair2, Y Liu1, C J Galban3, C Stevens4, E Castillo1, (1) University of Texas at Austin, Austin, TX, (2) Beaumont Heath System, (3) University of Michigan, Ann Arbor, MI, (4) William Beaumont Hospital, Royal Oak, MI


TH-B-207-4 (Thursday, 7/14/2022) 8:30 AM - 9:30 AM [Eastern Time (GMT-4)]

Room 207

Purpose: CT-derived perfusion (CTP) is a novel image processing modality that quantifies pulmonary perfusion from non-contrast inhale/exhale CT image pairs. While existing imaging markers for COPD primarily depend on morphological features or HU thresholding, CTP provides a quantitative marker of blood flow and has been shown to identify disease progression prior to the appearance structural abnormalities. In this study, we assess the potential utility of CTP as a disease severity marker for COPD.

Methods: CTP was computed from high-resolution inhale-exhale CT scans for 785 patients with a Global Initiative for Obstructive Lung Disease (GOLD) score ranging from 0 to 4 participating in the COPDGene® multi-center trial. Mean CTP (mCTP) was computed for each patient and the Spearman correlations with GOLD score and Forced Expiratory Volume after 1 second (FEV1) were computed.

Results: The Spearman correlations, across all patients, between mCTP and GOLD score and mCTP and FEV1 were -0.61 and 0.59, respectively. Both correlations achieved statistical significance (p-value < 0.05).

Conclusion: mCTP correlation with FEV1 is higher but comparable to correlations generated by competing state-of-the art imaging markers. Moreover, higher mCTP is associated with higher lung function, which implies that its negative correlation with GOLD score is indicative of CTP’s potential as a tool for characterizing disease severity. When combined with CT-derived ventilation methods, CTP has the potential to provide VQ mismatch scoring, in addition to perfusion quantification, that could be used to identify and diagnose COPD with higher fidelity. Future work includes incorporating the spatial distribution of function, therefore using CTP for each voxel rather than whole-lung mCTP, to characterize disease progression in different regions of the lung and monitor more minute changes in perfusion and, therefore, lung function.


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