Room: Exhibit Hall | Forum 8
Purpose: To produce a synthetic CT image using MR image, a multi-modality phantom is vitally required to verify the accuracy of the synthesized CT image in MR image-guided therapy. For fabricating the multi-modality phantom, the usefulness of silicone oil as a modifier of signal intensity on MR and CT image was evaluated to verify this novel technique regarding a signal enhancement of various thermosetting resin materials.
Methods: A total 25 polyurethanes and silicones were assessed by 0.35T MRI-guided radiotherapy machine with TRUFI sequence and CT scanner. Then, the seven polyurethanes among them were mixed with a 10, 20, 30 and 40 percent by weight (wt%) silicone oil (3000cps), respectively. These and fresh water and 100% silicone oil were imaged by a 1.5T field strength MRI with a T1, T2-turbo spin echo sequence and CT scanner. A T1 and T2 relaxation time and Hounsfield unit (HU) value were measured to identify a correlation between a volume of silicone oil and the signal intensity.
Results: Smooth cast 45D (SC45D)and 325 with no T2/T1 signal were mixed with the silicone oil and then, those gained a significant T2/T1 (147.07Â±12ms/95.2Â±9ms for SC45D and 192.31Â±3ms/148.83Â±12ms for SC325) signal difference accordance with the silicone oil concentration(20 wt%). Whereas, the CT number of SC45D decreased from 120 to 21(HU) and also, the SC325â€™s HU value reduced from 113 to 32.
Conclusion: Easy of handling, sufficient strength and a low cost are preferred to fabricate the multi-modality phantom. The thermosetting resin materials are quite adequate for the required conditions but it is not straightforward to control the signal intensity on the MR and CT images. In this study, the proposed technique leads to a sufficient signal enhancement and therefore, it is anticipated that the silicone oil will be useful for generating the multi-modality phantom.
Funding Support, Disclosures, and Conflict of Interest: Current work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2017M2A2A7A02020643).