Generation of Dual-Energy CT Image Dataset From Single-Energy CT Image Dataset
Y Dong1*, J Huo2, X Wang3, P Wang4, X Zhu5, Y Feng6, (1) Tianjin Medical University Cancer Institute and Hospital, Tianjin, (2) Tianjin University, Tianjin, (3) Tianjin University, Tianjin, (4) Tianjin Medical University Cancer Institute and Hospital, Tianjin, (5) Tianjin University, Tianjin, (6) Tianjin Medical University Cancer Institute and Hospital, Tianjin University, East Carolina University, Greenville, NCSU-E-J-13 Sunday 3:00PM - 6:00PM Room: Exhibit Hall
Purpose: The capability of obtaining tissue-specific information from dual-energy imaging has the potential to be used in radiotherapy for gross-tumor-volume (GTV) definition and localization with enhanced accuracy in radiotherapy treatment planning and image guided delivery. However, there are not dedicated dual energy CT (DECT) simulators in radiotherapy clinics yet. This study is to develop a method to create a set of DECT data with the data from a conventional single-energy CT simulator, i.e., to generate a set of CT images at energy B from a set of CT images obtained at energy A, and to explore the feasibility of utilizing the two CT data sets to create anatomical images with higher soft-tissue-contrast.
Methods: CT images from 5 lung cancer patients were used in this feasibility study. The images were acquired with a Brilliance Big Bore CT simulator at energy A (CT_A, 120kVp). For each patient, one set of CT images at energy B (CT_B, 30keV) was generated from CT_A with a bilinear scaling method, two pairs of digitally reconstructed radiographs (DRRs) were created from CT_B and CT_A respectively and then a soft-tissue-only DRR pair (DRR_dualE) was obtained using an in-house software of dual-energy subtraction with DRR_CTA and DRR_CTB. The image quality of DRRs was quantitatively analyzed using signal-difference-to-noise-ratio (SDNR).
Results: The results indicate that the generated CT images showed improved contrast and the DRRdualE showed that bony structure was eliminated, soft tissue contrast was improved. SDNR for GTV was improved from 1.94 ± 0.09 (in DRR_CTA) to 4.31 ± 0.12 (in DRR_dualE).
Conclusion: The feasibility study has shown the potential of utilizing a conventional single-energy CT dataset to get another set of CT images at a different energy to achieve improved soft tissue contrast for GTV delineation or target localization when a DECT machine is not available. Corresponding:firstname.lastname@example.org
Funding Support, Disclosures, and Conflict of Interest: National Science Foundation of China (NSFC-81171342,81041107)
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