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The cone-beam CT (CBCT) imaging systems that based on flat panel detectors have been widely implemented in image-guided intervention and radiation therapy applications. However, the imaging performance of CBCT is strongly limited. One of such limitations is the lack of quantitative imaging capability, which is important for material recognition, image contrast enhancement, and dose reduction. Over the past decade, dual-energy computed tomography (DECT) has become a promising imaging technique in generating quantitative material information, whereas, multiple (>2) basis images with high quality and accuracy are hard to be obtained from the conventional DECT image reconstruction algorithms. In this work, an innovative deep learning technique is presented to realize three materials decomposition from the dual-energy CBCT scans. In this strategy, a dedicated end-to-end convolutional neural network (CNN) is developed. It accepts the low and high energy CBCT projections, and automatically outputs three different basis image volumes (water basis, iodine basis, CaCl2 basis) with high accuracy. Training data was synthesized numerically from the photos downloaded from ImageNet. Dual-energy projections of the Iodine/CaCl2 phantom with ground truth were acquired from our in-house benchtop CBCT system to validate the proposed method. Results demonstrate that this novel network is able to generate three different material bases with high accuracy (decomposition errors less than 5%). In conclusion, the proposed CNN based multi-material (≥ 3) decomposition approach shows promising benefits in high quality dual-energy CBCT imaging applications.
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