Program Information
Enhancing Tumor Contrast and Contrast-To-Noise Ratio for Improved Tumor Targeting During Liver SBRT Using Mono-Energetic Decompositions of Dual Energy CT
G Noid*, J Robbins , D Schott , A Tai , Y Liu , X Li , Medical College of Wisconsin, Milwaukee, WI
Presentations
WE-DE-605-5 (Wednesday, August 2, 2017) 10:15 AM - 12:15 PM Room: 605
Purpose: It is desirable to increase CT contrast and contrast to noise ratio (CNR) to improve delineation of hypo-dense liver tumors for SBRT planning and delivery guidance. This can be accomplished by the application of mono-energetic images (MEI) obtained from dual energy (DE) CT.
Methods: CT data were acquired with an in-room CT (Definition AS Open, Siemens Healthcare) during SBRT delivery for liver cancer and a phantom (CTED/Gammex) using both sequential DE protocols and standard 120 kVp protocols. For the DE protocols, the scanner rapidly performs two acquisitions, the first at a tube voltage of 80 kV and the second at a tube voltage of 140 kV. MEI across a range of energies (40-120 keV) were reconstructed using an image-based material decomposition. An iterative reconstruction was applied to reduce the noise in the MEI and subtracted images were generated to maximize tumor visibility. Contrast, defined as the difference in the mean CT number of the target and the surrounding liver tissue, was measured and compared between the DE and standard protocols. The contrast to noise ratio was also evaluated.
Results: Phantom scans confirm the expected contrast enhancement as a function of x-ray energy. For the patient data, the lowest energy decompositions featured the highest contrast between target and surrounding tissue, allowing improved target delineation. For a representative patient, the increase in contrast relative to a 120 kVp scan at 40 keV was 13.8 HU. By applying an iterative reconstruction the CNR ratio increased monotonically from 0.3 to 0.8 as energy decreased.
Conclusion: Low energy MEI from dual-energy CT substantially increase liver tumor contrast and CNR, resulting in improved tumor targeting particularly important for SBRT.
Funding Support, Disclosures, and Conflict of Interest: Partially supported by Siemens
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