Abstract
X-ray scattering significantly limits image quality. Conventional strategies for scatter reduction based on physical equipment or measurements inevitably increase the dose to improve the image quality. In addition, scatter reduction based on a computational algorithm could take a large amount of time. We propose a deep learning-based scatter correction method, which adopts a convolutional neural network (CNN) for restoration of degraded images. Because it is hard to obtain real data from an X-ray imaging system for training the network, Monte Carlo (MC) simulation was performed to generate the training data. For simulating X-ray images of a human chest, a cone beam CT (CBCT) was designed and modeled as an example. Then, pairs of simulated images, which correspond to scattered and scatter-free images, respectively, were obtained from the model with different doses. The scatter components, calculated by taking the differences of the pairs, were used as targets to train the weight parameters of the CNN. Compared with the MC-based iterative method, the proposed one shows better results in projected images, with as much as 58.5% reduction in root-mean-square error (RMSE), and 18.1% and 3.4% increases in peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), on average, respectively.
Highlights
IntroductionWhen X-rays penetrate a patient’s body, the scattered radiation significantly limits image quality, resulting in contrast reduction, image artifacts, and lack of computed tomography (CT) number
When X-rays penetrate a patient’s body, the scattered radiation significantly limits image quality, resulting in contrast reduction, image artifacts, and lack of computed tomography (CT) number.In addition, in digital radiography (DR) images, scattered photons due to large illumination fields seriously degrade image quality
Once the data are obtained, they can be used for training the convolutional neural network (CNN) for image restoration, after which the CNN can be used for scatter correction of X-ray images
Summary
When X-rays penetrate a patient’s body, the scattered radiation significantly limits image quality, resulting in contrast reduction, image artifacts, and lack of computed tomography (CT) number. In digital radiography (DR) images, scattered photons due to large illumination fields seriously degrade image quality. Scatter correction is important to enhance the quality of. X-ray images and eventually of 3D reconstructed volume data. Conventional strategies for scatter management can be classified into two groups according to whether a method is based on hardware or software. Scatter control can be achieved by scatter suppression and scatter estimation. Hardware-based scatter suppression techniques, such as anti-scatter grid [1,2], attempt to reduce the number of scattered photons that reach the detector array
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