We developed a region-of-interest (ROI) image reconstruction method that effectively reduces truncation artifacts in CBCT. By using U-Net-based deep learning (DL) methods, we devised a method to reduce truncation artifacts for ROI imaging. A total of 16294 image slices from 49 patient cases were used to generate projection data. The center of the projected image was cropped to a width of 150 mm. Then, the outer part of the truncation image was filled with each outermost pixel value for the initial correction. After the filtering process, the truncation area was cut off and used as input data in the DL model. Finally, inference images were reconstructed by use of the FDK algorithm. SSIM values for the test set of 14 patients were calculated as 0.541, 0.709 and 0.979 for FBP, Extension and the proposed ROI method, respectively. We have achieved promising results and believe that the proposed ROI image reconstruction method can help reduce radiation dose while preserving image quality
In computed tomography (CT) imaging, radiat ion dose delivered to the patient is one of the major concerns. Many CT
developers and researchers have been making efforts to reduce radiat ion dose. Spars e-view CT takes project ions at
sparser view-angles and provides a viable option to reducing radiation dose. Sparse-view CT inspired by a compressive
sensing (CS) theory, which acquires sparsely sampled data in projection angles to reconstruct volumetric images of the
scanned object, is under active research for low-dose imaging. Also, region of interest (ROI) imaging method is one of
the reasonable approaches to reducing the integral dose to the patient and the risk of overdose. In this study, we
combined the two approaches together to achieve an ultra-low-dose imaging: a sparse-view imaging and the intensityweighted
region-of-interest (IWROI) imaging. IWROI imaging technique is particularly interesting because it can reduce
the imaging radiation dose substantially to the structures away from the imaging target, while allowing a stable solution
of the reconstruction problem in comparison with the interior problem. We used a total-variation (TV) minimization
algorithm that exploits the sparseness of the image derivative magnitude and can reconstruct images from sparse-view
data. In this study, we implemented an imaging mode that combines a sparse-view imaging and an ROI imaging. We
obtained promising results and believe that the proposed scanning approach can help reduce radiation dose to the patients
while preserving good quality images for applications such as image-guided radiation therapy. We are in progress of
applying the method to the real data.
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