KEYWORDS: Computed tomography, Medical imaging, Denoising, Image sharpness, Education and training, Image quality, Tunable filters, Image restoration, Image filtering, Signal to noise ratio
Self-supervised learning for CT image denoising is a promising technique because it does not require clean target data that are usually unavailable in the clinic. Noise2void (N2V) is one of the famous methods to denoise the image without paired target data and it has been used to denoise optical images and also medical images such as MRI, and CT. However, the performance of the N2V is still limited due to the restricted receptive field of the network and it decreases the prediction performance for CT images that have complex image context and non-uniform Poisson random noise. Thus, we proposed enhanced N2V that utilizes penalty-driven network optimization to further denoise the images while preserving the important details. We used the total variation term to further denoise the image and also the laplacian pyramids term to preserve the important edges of the image. The degree of the influence of each penalty term is controlled by the hyperparameter value and they are optimized to achieve the best image quality in terms of noise level and structure sharpness. For the experiment, the real dental CBCT projection data were used to train the network in the projection domain. After the network training, the test results were reconstructed and compared at each different dose level. Meanwhile, PSNR, SNR, and a line profile were also evaluated to quantitatively compare the original FDK images, and proposed method. In conclusion, the proposed method achieved further denoises the image than N2V even preserving the details. By penalty-driven optimization, the network was able to learn the spectral features of the image while still the receptive field is limited to avoid identity mapping. We hope that our method would increase the practical utility of network-based CT images denoising that usually the target data are unavailable.
Often, the artifacts caused by high-density objects degrade the quality of the image with streaks and information loss in CT imaging. In recent years, machine learning has proven itself a powerful tool to resolve some of the challenges faced in reducing metal artifacts. In this work, a novel method of metal artifact reduction (MAR) without metal segmentation by using a CNN network is proposed. The approach focuses on removing the need for the sensitive metal segmentation step to improve robustness and aims to tackle beam hardening directly in the sinogram domain. In the proposed method, we trained the network with sinogram pairs that include metal objects and those that include virtual non-metal (VNM) replacement objects. A VNM object is designed to be less dense than metal but more dense than soft tissue. The novelty of this method lies in the sinogram-to-sinogram training without the need for metal segmentation by replacing the metal object to a virtual non-metal object in the sinogram to reduce beam hardening and successfully compensate for the information loss.
In the production of display screen modules, multi-faceted quality control is performed. One of the processes is detection of defects on and between module components such as particles, scratches and air bubbles using a 3D optical microscope. Technicians view a stack of images of potential defect areas and make a qualitative assessment of the sample. However, this is made difficult by the artifacts in the unfocused image layers. Moreover, there is a large discrepancy in the detection tendencies of the technicians. In order to standardize and automate the classification of major and minor defects in products, we propose a convolutional neural network based binary classification that makes use of the normal angle and oblique angle images. The decision factors affecting the classification of the sample include defect position, size, and shape. In order to reflect these factors, the microscopic images of the sample are taken in varying focal depths from normal and oblique angles. Then, the maximum intensity projection (MaxIP) and minimum intensity projection (MinIP) in the xy, yz, xz plane are created. The set of MaxIP and MinIP are used to train a modified VGG-network. Each plane differs in size, so MaxIP and MinIP of every plane was independently added as input to the network and were concatenated in the fully connected layer. Being that the dataset used for this work composed of 185 major defect samples and 2036 minor defect samples, augmentation was essential. In order to even out the major and minor defect sample ratio, random affine transformation was performed on the major defect sample images. The proposed method of binary classification performs with a total accuracy of 98.6%.
A single-scan dual-energy low-dose cone-beamCT (CBCT) imaging technique that exploits a filter-strip array is summarized in this paper. The filter-strip array installed between the x-ray source and the scanned object is reciprocated during a scan. The x-ray beams through the slits would generate relatively low-energy x-ray projection data, while the filtered beams would make high-energy projection data. An iterative image reconstruction algorithm that uses an adaptive-steepest-descent method to minimize image total-variation under the constraint of data fidelity was applied to reconstructing the image from the low-energy projection data. Since the high-energy projection data suffer from a substantially high noise level due to the beam filtration, the algorithm exploits the joint sparsity between the low- and high-energy CT images for image reconstruction of the high-energy CT image. The feasibility of the proposed technique has been earlier demonstrated by the use of various phantoms in the experimental CBCT setup. Based on the proposed dual-energy imaging, a material differentiation was also performed and its potential utility has been shown. In this work, we summarize the technique emphasizing task-specific optimization nature of the imaging in medical applications. A choice of beam-filtering material and its thickness, filter-strip array design, scanning configurations, and image reconstruction algorithm have been systematically investigated therefore.
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