Fifty-eighth annual meeting of the american association of physicists in medicine
WE-DE-BRA-07: Megavoltage Spectral Imaging with a Layered Detector
The aim of the current work is to investigate the feasibility of megavoltage spectral imaging using a multiple layered detector for enhancement of low contrast detectability through material segmentation and discrimination (such as bone, markers and metal implants). Potentially the technique can be applied to improve detection and reduce dose in Megavoltage Cone Beam Computed Tomography (MV-CBCT).
Experiments were performed with a prototype multi-layer imager (MLI) which has higher detective efficiency and lower noise characteristics than conventional Electronic Portal Imaging Devices (EPIDs). Images of a solid water phantom were acquired at 2.5 MV, 6MV and 6MV without flattening filter (FFF). The following materials were placed within a stack of solid water: aluminum, copper and gold. Material separation was assessed based on Contrast-to-Noise Ratio (CNR) of the weighted image, formed by a weighted subtraction of the images from two layers of the MLI. A range of weighting factors were investigated for material separation.
CNR can be minimized for each material by appropriate selection of the subtraction weighting factor. This is equivalent to a selective subtraction of specific materials from the image. Using multiple layers simultaneously also decreases the dose requirement and removes any registration errors. The minimum CNR for aluminum, copper and gold at the weighted image formed with 2.5MV was obtained at weighting factors equal to 0.92, 0.76 and 0.64 respectively. The corresponding values at 6MVFFF were 0.99, 0.92 and 0.78 respectively.
In the current work, an MV spectral imaging feasibility study was attempted using a novel multi-layer prototype EPID imager. Initial results suggest that material separation based on spectral differences between different layers is possible. This spectral imaging technique has potential advantages in MV-CBCT for real-time target tracking, patient set-up imaging and adaptive radiotherapy.
The project was supported, partially, by a grant from Varian Medical Systems, Inc., and Award No. R01CA188446-01 from the National Cancer Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health