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Technical Note: Estimation of lung tumor thickness from planar dual‐energy kV images
Author(s) -
Jung Frederick,
Patel Rakesh,
Campana Maria,
Panfil Joshua,
Pankuch Mark,
Roeske John C.
Publication year - 2015
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4927787
Subject(s) - imaging phantom , contrast (vision) , image contrast , materials science , linear regression , biomedical engineering , dual energy , planar , medical imaging , nuclear medicine , computer science , mathematics , optics , medicine , radiology , physics , pathology , statistics , bone mineral , computer graphics (images) , osteoporosis
Purpose: Dual‐energy (DE) imaging is a method that suppresses bony anatomy on planar kV images while enhancing soft tissue contrast. This technique has been used specifically in the chest to improve the visualization of small lung tumors. However, DE imaging may also provide quantitative information that has not been previously investigated. In this study, the aim was to establish a theoretical relationship between DE image contrast and tumor thickness and to observe this trend in phantom experiments. Methods: A phantom consisting cork (used to simulate lung), tissue‐equivalent material, and pork ribs was constructed to test for a relationship between DE image contrast and simulated tumor thickness. Fifteen phantom setups were used with various thicknesses of cork and tissue‐equivalent material. For each setup, high (120 kVp) and low (60 kVp) energy planar images were acquired and DE images were produced. The image contrast between the simulated tumor and surrounding tissue was then plotted against the known thicknesses and a linear regression was performed. Results: A linear regression of the contrast data vs simulated tumor thickness resulted in a slope of −0.0454 with an R 2 = 0.9904. The expected uncertainty in the thickness measurements using the regression parameters and DE contrast standard deviation was 0.13 cm. Conclusions: Phantom data exhibited a linear relationship between DE image contrast and simulated tumor thickness. Future studies will investigate patient‐specific parameters so that this method can be used clinically to evaluate tumor thickness from planar kV images. Such an approach may have benefits for both adaptive and heavy ion therapies.