(Wednesday, 7/17/2019) 7:30 AM - 9:30 AM
Room: 221AB
Purpose: Functional lung imaging has shown promise for radiation therapy applications, where single-source CT is the most common imaging modality. The purpose of this study is to evaluate the ability of two single-source perfusion CT techniques to accurately identify functional lung tissue in a novel animal model, the Wisconsin Miniature SwineTM (WMS).
Methods: Dynamic 4DCT and split-filter DECT images were acquired on a Siemens SOMATOM Definition Edge CT scanner for three WMS in twelve imaging sessions. In each session, dynamic 4DCT images were acquired over the central 15 cm of the lung with a 5 ml/sec flowrate. The dynamic 4DCT protocol acquired an average of 36 sequential, helical acquisitions over the course of 51 seconds. A split-filter DECT acquisition was acquired in the distal arterial phase, after contrast bolus-tracking in the pulmonary trunk using a 4 ml/sec flowrate. Image analysis was performed in Siemens Syngo(VB30) software. Blood volume distribution and iodine maps were calculated for dynamic 4DCT and DECT, respectively. The regional-lung functionality was determined for six regions within a 5 cm axial-length of lung centered in the scan length of the dynamic 4DCT image. Regional-lung function was calculated as the average image value in each region normalized to the average image value in the entire 5 cm of segmented lung.
Results: A correlation was found between regional functional lung derived from split-filter DECT versus dynamic 4DCT (r =0.93). Lung function was greatest in the posterior regions of the lungs and decreased in the anterior segments for both CT techniques. Volume of the highest 50% of functioning lung segmented with both techniques agreed well within 6% (1-9%).
Conclusion: Two single-source CT techniques, dynamic 4DCT and a split-filter DECT, show promise in identifying functional lung within a WMS animal model. Future work is required to understand the limitations of each technique.
Keywords
Dual-energy Imaging, Functional Imaging, Perfusion Imaging
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