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Photon-counting computed tomography (PCCT) reconstructs multiple energy-channel images to describe the same object, where there exists a strong correlation among different channel images. In addition, reconstruction of each channel image suffers photon count starving problem. To make full use of the correlation among different channel images to suppress the data noise and enhance the texture details in reconstructing each channel image, this paper proposes a tensor neural network (TNN) architecture to learn a multi-channel texture prior for PCCT reconstruction. Specifically, we first learn a spatial texture prior in each individual channel image by modeling the relationship between the center pixels and its corresponding neighbor pixels using a neural network. Then, we merge the single channel spatial texture prior into multi-channel neural network to learn the spectral local correlation information among different channel images. Since our proposed TNN is trained on a series of unpaired small spatial-spectral cubes which are extracted from one single reference multi-channel image, the local correlation in the spatial-spectral cubes is considered by TNN. To boost the TNN performance, a low-rank representation is also employed to consider the global correlation among different channel images. Finally, we integrate the learned TNN and the low-rank representation as priors into Bayesian reconstruction framework. To evaluate the performance of the proposed method, four references are considered. One is simulated images from ultra-high-resolution CT. One is spectral images from dual-energy CT. The other two are animal tissue and preclinical mouse images from a custom-made PCCT systems. Our TNN prior Bayesian reconstruction demonstrated better performance than other state-of-the-art competing algorithms, in terms of not only preserving texture feature but also suppressing image noise in each channel image.

Lowering radiation dose per view and utilizing sparse views per scan are two common CT scan modes, albeit often leading to distorted images characterized by noise and streak artifacts. Blind image quality assessment (BIQA) strives to evaluate perceptual quality in alignment with what radiologists perceive, which plays an important role in advancing low-dose CT reconstruction techniques. An intriguing direction involves developing BIQA methods that mimic the operational characteristic of the human visual system (HVS). The internal generative mechanism (IGM) theory reveals that the HVS actively deduces primary content to enhance comprehension. In this study, we introduce an innovative BIQA metric that emulates the active inference process of IGM. Initially, an active inference module, implemented as a denoising diffusion probabilistic model (DDPM), is constructed to anticipate the primary content. Then, the dissimilarity map is derived by assessing the interrelation between the distorted image and its primary content. Subsequently, the distorted image and dissimilarity map are combined into a multi-channel image, which is inputted into a transformer-based image quality evaluator. By leveraging the DDPM-derived primary content, our approach achieves competitive performance on a low-dose CT dataset.

X-ray spectrum plays an essential role in CT applications. Since it is difficult to measure X-ray spectrum directly in practice, X-ray spectrum is always indirectly obtained by using transmission measurements through a calibration phantom of known thickness and materials. These methods are independent of CT image reconstruction and bring extra cost. To solve this problem, we propose a parametric physical model-based X-ray spectrum estimation algorithm for CT imaging. First, an X-ray spectrum model with six parameters is proposed, which is derived from the X-ray imaging physics. Second, a template image containing different material components can be obtained by segmenting CT reconstructed images with a simple method. And the estimated projections can be calculated by reprojecting the template image with the proposed spectrum model. Finally, the six model parameters can be solved by iteratively minimizing the error between the estimated projection and real measurements. The effectiveness of the proposed method has been validated on both simulated and real data. Experimental results demonstrate that the proposed method can estimate the accurate spectra at different energies and provide a good reconstruction of characteristic radiations without extra cost.

Ring artifacts degrade the quality of reconstructed images in cone-beam computed tomography (CBCT). In this article, we propose a hybrid ring artifact reduction algorithm in computed tomography (CT) images based on a convolutional neural network (CNN), which fuses the information from the image domain and sinogram domain corrected images. The proposed method consists of three steps. First, the database for CNN training is established, which consists of artifact-free, ring artifact, and sinogram domain corrected images. Second, the original and sinogram domain corrected images are input to the trained CNN to generate an image with less artifacts. Finally, we use image mutual correlation to generate a hybrid corrected image by fusing the information from ring artifacts reduction in the sinogram domain and output by CNN. Both simulated and real experiments were performed to verify the proposed method. The experimental results show that the proposed method can suppress the ring artifacts effectively without the introduction of structure distortion.

AbstractHyperspectral classification with limited training samples is challenging. The current work lies in two aspects: first, we change the statistical distribution of samples by iterative filtering based on the guide images. The filter is called a Simplified Bilateral Filter (SBF), which is a modified bilateral filter for clustering samples. Secondly, new structural convolution kernels are used to generate new hyperspectral data. Finally, the class label of the test sample after dimension reduction is determined by OMP classification or SVM classification. Experimental results on two hyperspectral datasets demonstrate the effectiveness of the proposed feature extraction method in improving classification accuracy with limited training samples.

X-ray scatter leads to erroneous calculations of dual-energy digital mammography (DEDM). The purpose of this work is to design an algorithmic method for scatter correction in DEDM without extra exposures or lead sheet. The method was developed based on the knowledge that scatter radiation in mammograms varies slowly spatially and most pixels in mammograms are non-calcification pixels, and implemented on a commercial full-field digital mammography system with a phantom of breast tissue equivalent material. The pinhole-array interpolation scatter correction method was also implemented on the system. We compared the background dual-energy (DE) calcification signals in the DE calcification images. Results show that the background signal in the DE calcification image can be reduced. The rms of background DE calcification image signal of 1105m with scatter-uncorrected data was reduced to 187m and 253m after scatter correction, using our algorithmic method and pinhole-array interpolation method, respectively. The range of background DE calcification signals using scatter-uncorrected data was reduced by ~80% with scatter-corrected data using algorithmic method. The proposed algorithmic scatter correction method is effective; it has similar or even better performance than pinhole-array interpolation method in scatter correction for DEDM.

Recently iterative reconstruction algorithms with total variation (TV) regularization have shown its tremendous power in image reconstruction from few-view projection data, but it is much more demanding in computation. In this paper, we propose an accelerated augmented Lagrangian method (ALM) for few-view CT reconstruction with total variation regularization. Experimental phantom results demonstrate that the proposed method not only reconstruct high quality image from few-view projection data but also converge fast to the optimal solution.

Bit allocation is a key issue in image/video coding. An optimal bit allocation can improve the encoding performance, which means to maximize the image/video quality in the constraint of bit rate, or vice versa, to minimize the bit rate with a restrictive quality. We suggest that the bit allocation for macro blocks (MBs) can be optimized by aiming at the constant perceptual quality (CPQ) inside an image/a frame. Based on the MINMAX criterion, we proposed a multi-pass block-layer bit allocation scheme for intra frame encoding, in which all the local areas in a frame get approximately the same perceptual quality by choosing the quantization parameter (QP) for each MB. The experimental results show that the proposed method can improve the encoding performance obviously.

The reduced reference (RR) image quality assessment (IQA) has been attracting much attention from researchers for its loyalty to human perception and flexibility in practice. A promising RR metric should be able to predict the perceptual quality of an image accurately while using as few features as possible. In this paper, a novel RR metric is presented, whose novelty lies in two aspects. Firstly, it measures the image redundancy by calculating the so-called Sub-image Similarity (SIS), and the image quality is measured by comparing the SIS between the reference image and the test image. Secondly, the SIS is computed by the ratios of NSE (Non-shift Edge)between pairs of sub-images. Experiments on two IQA databases (i.e. LIVE and CSIQ databases) show that by using only 6 features, the proposed metric can work very well with high correlations between the subjective and objective scores. In particular, it works consistently well across all the distortion types.

Recently, research of Objective Image Quality Assessment (IQA) has gained much attention due to its wide application prospect. Among them, the Reduced-Reference (RR) methods estimate perceptual quality of distorted images with partial information from the reference images. This paper proposes a novel universal RR-IQA metric based on the statistics of edge patterns. Firstly, the binary edge maps of the reference and distorted images are created by the LOG operator and zero-crossing detection. Based on them, 15 groups of typical edge patterns are extracted and then their statistical distributions are calculated respectively for the reference and distortion images. The proposed RR-IQA metric is achieved by computing the L-1 Minkowski distance between those two distributions. We have evaluated this metric on six publicly accessible subjective IQA databases. Experiments shows that the proposed metric featured with typical edge patterns outperform other methods in terms of data volume, accuracy and consistency with human perception. In a way, our work provides a new view to the IQA metric design.

This paper proposes a novel method to correct saturated pixels in images. This method is based on the YCbCr color space and separately corrects the chrominance and the luminance of saturated pixels. Dynamic thresholds are adopted to identify saturated pixels, i.e. the thresholds for different images and different color channels are different. So our method can correct not only RAW images but also processed images. Once the saturated pixels are identified, there are three kinds of saturated pixels: 1-channel saturated pixels, 2-channel saturated pixels and 3-channel saturated pixels. They are denoted as 1, 2and 3respectively. Different strategies are implemented to these three kinds of regions. The color of saturated pixels in 1is corrected according to their original color and the color of their neighborhood. And the color of saturated pixels in 2and 3is corrected only according to the color of their neighborhood. The luminance of saturated pixels is corrected using the model proposed in this paper. Experiment results show our method is effective in correcting saturated pixels of RAW images and process images.

We propose a novel method to reduce the noise in fan-beam computed tomography (CT) imaging. First, the inverse Radon transform is induced for a family of differential expression of projection function. Second, the diffusion partial differential equation (PDE) is generalized from image space to projection space in parallel-beam geometry. Third, the diffusion PDE is further induced from parallel-beam geometry to fan-beam geometry. Finally, the projection direction dependent diffusion is developed to reduce CT noise, which arises from the quantum variation in the low dose exposure of a medical x-ray CT (XCT) system. The proposed noise reduction processes projections iteratively and dependently on x-ray path position, followed by a general CT reconstruction. Numerical simulation studies have demonstrated its feasibility in the noise reduction of low dose fan-beam XCT imaging.

For interior tomography based on truncated Hilbert transform (THT), the recently proposed truncated singular value decomposition (TSVD) reconstruction method uses a regularization parameter given directly. In this paper, a method of choosing the regularization parameter is presented based on L-curve to get an optimal regularization parameter in theoretical sense. Furthermore, we develop a Tikhonov regularization method in comparison to TSVD. Simulation results indicate that both of the two regularization methods with the optimal regularization parameters have good performances on the image quality for both cases of noise-free and noisy projections.

The research on image quality assessment (IQA) has been become a hot topic in most area concerning image processing. Seeking for the efficient IQA model with the neurophysiology support is naturally the goal people put the efforts to pursue. In this paper, we argue that comparing the edges position of reference and distorted image can well measure the image structural distortion and become an efficient IQA metric, while the edge is detected from the primitive structures of image convolving with LOG filters. The proposed metric is called NSER that has been designed following a simple logic based on the cosine distance of the primitive structures and two accessible improvements. Validation is taken by comparison of the well-known state-of-the-art IQA metrics: VIF, MS-SSIM, VSNR over the six IQA databases: LIVE, TID2008, MICT, IVC, A57, and CSIQ. Experiments show that NSER works stably across all the six databases and achieves the good performance.