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Interior tomography of X-ray computed tomography (CT) has many advantages, such as a lower radiation dose and lower detector hardware cost compared to traditional CT. However, this imaging technique only uses the projection data passing through the region of interest (ROI) for imaging; accordingly, the projection data are truncated at both ends of the detector, so the traditional analytical reconstruction algorithm cannot satisfy the demand of clinical diagnosis. To solve the above limitations, in this paper we propose a high-quality statistical iterative reconstruction algorithm that uses the zeroth-order image moment as novel prior knowledge; the zeroth-order image moment can be estimated in the projection domain using the Helgason–Ludwig consistency condition. Then, the L1norm of sparse representation, in terms of dictionary learning, and the zeroth-order image moment constraints are incorporated into the statistical iterative reconstruction framework to construct an objective function. Finally, the objective function is minimized using an alternating minimization iterative algorithm. The chest CT image simulated and CT real data experimental results demonstrate that the proposed approach can remove shift artifacts effectively and has superior performance in removing noise and persevering fine structures than the total variation (TV)-based approach. [ABSTRACT FROM AUTHOR]

Purpose: Four‐dimensional cone‐beam computed tomography (4D CBCT) is developed to reconstruct a sequence of phase‐resolved images, which could assist in verifying the patient's position and offering information for cancer treatment planning. However, 4D CBCT images suffer from severe streaking artifacts and noise due to the extreme sparse‐view CT reconstruction problem for each phase. As a result, it would cause inaccuracy of treatment estimation. The purpose of this paper was to develop a new 4D CBCT reconstruction method to generate a series of high spatiotemporal 4D CBCT images. Methods: Considering the advantage of (DL) on representing structural features and correlation between neighboring pixels effectively, we construct a novel DL‐based method for the 4D CBCT reconstruction. In this study, both a motion‐aware dictionary and a spatially structural 2D dictionary are trained for 4D CBCT by excavating the spatiotemporal correlation among ten phase‐resolved images and the spatial information in each image, respectively. Specifically, two reconstruction models are produced in this study. The first one is the motion‐aware dictionary learning‐based 4D CBCT algorithm, called motion‐aware DL based 4D CBCT (MaDL). The second one is the MaDL equipped with a prior knowledge constraint, called pMaDL. Qualitative and quantitative evaluations are performed using a 4D extended cardiac torso (XCAT) phantom, simulated patient data, and two sets of patient data sets. Several state‐of‐the‐art 4D CBCT algorithms, such as the McKinnon–Bates (MKB) algorithm, prior image constrained compressed sensing (PICCS), and the high‐quality initial image‐guided 4D CBCT reconstruction method (HQI‐4DCBCT) are applied for comparison to validate the performance of the proposed MaDL and prior constraint MaDL (pMaDL) pmadl reconstruction frameworks. Results: Experimental results validate that the proposed MaDL can output the reconstructions with few streaking artifacts but some structural information such as tumors and blood vessels, may still be missed. Meanwhile, the results of the proposed pMaDL demonstrate an improved spatiotemporal resolution of the reconstructed 4D CBCT images. In these improved 4D CBCT reconstructions, streaking artifacts are suppressed primarily and detailed structures are also restored. Regarding the XCAT phantom, quantitative evaluations indicate that an average of 58.70%, 45.25%, and 40.10% decrease in terms of root‐mean‐square error (RMSE) and an average of 2.10, 1.37, and 1.37 times in terms of structural similarity index (SSIM) are achieved by the proposed pMaDL method when compared with piccs, PICCS, MaDL(2D), and MaDL(2D), respectively. Moreover the proposed pMaDL achieves a comparable performance with HQI‐4DCBCT algorithm in terms of RMSE and SSIM metrics. However, pMaDL has a better ability to suppress streaking artifacts than HQI‐4DCBCT. Conclusions: The proposed algorithm could reconstruct a set of 4D CBCT images with both high spatiotemporal resolution and detailed features preservation. Moreover the proposed pMaDL can effectively suppress the streaking artifacts in the resultant reconstructions, while achieving an overall improved spatiotemporal resolution by incorporating the motion‐aware dictionary with a prior constraint into the proposed 4D CBCT iterative framework. [ABSTRACT FROM AUTHOR]

In computed tomography (CT) imaging, optimizing the balance between radiation dose and image quality is crucial due to the potentially harmful effects of radiation on patients. Although subjective assessments by radiologists are considered the gold standard in medical imaging, these evaluations can be time-consuming and costly. Thus, objective methods, such as the peak signal-to-noise ratio and structural similarity index measure, are often employed as alternatives. However, these metrics, initially developed for natural images, may not fully encapsulate the radiologists' assessment process. Consequently, interest in developing deep learning-based image quality assessment (IQA) methods that more closely align with radiologists' perceptions is growing. A significant barrier to this development has been the absence of open-source datasets and benchmark models specific to CT IQA. Addressing these challenges, we organized the Low-dose Computed Tomography Perceptual Image Quality Assessment Challenge in conjunction with the Medical Image Computing and Computer Assisted Intervention 2023. This event introduced the first open-source CT IQA dataset, consisting of 1,000 CT images of various quality, annotated with radiologists' assessment scores. As a benchmark, this challenge offers a comprehensive analysis of six submitted methods, providing valuable insight into their performance. This paper presents a summary of these methods and insights. This challenge underscores the potential for developing no-reference IQA methods that could exceed the capabilities of full-reference IQA methods, making a significant contribution to the research community with this novel dataset. The dataset is accessible at https://zenodo.org/records/7833096., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Jang-Hwan Choi reports financial support was provided by the National Research Foundation of Korea. Jang-Hwan Choi reports was provided by the Korea Medical Device Development Fund grant funded by the Korea government. Adrian Galdran reports financial support was provided by the Marie Sklodowska-Curie Fellowship. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

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.

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.

Image restoration (IR) via deep learning has been vigorously studied in recent years. However, due to the ill-posed nature of the problem, it is challenging to recover the high-quality image details from a single distorted input especially when images are corrupted by multiple distortions. In this paper, we propose a multi-stage IR approach for progressive restoration of multi-degraded images via transferring similar edges/textures from the reference image. Our method, called a Reference-based Image Restoration Transformer (Ref-IRT), operates via three main stages. In the first stage, a cascaded U-Transformer network is employed to perform the preliminary recovery of the image. The proposed network consists of two U-Transformer architectures connected by feature fusion of the encoders and decoders, and the residual image is estimated by each U-Transformer in an easy-to-hard and coarse-to-fine fashion to gradually recover the high-quality image. The second and third stages perform texture transfer from a reference image to the preliminarily-recovered target image to further enhance the restoration performance. To this end, a quality-degradation-restoration method is proposed for more accurate content/texture matching between the reference and target images, and a texture transfer/reconstruction network is employed to map the transferred features to the high-quality image. Experimental results tested on three benchmark datasets demonstrate the effectiveness of our model as compared with other state-of-the-art multi-degraded IR methods. Our code and dataset are available at https://vinelab.jp/refmdir/.

Anti-vascular endothelial growth factor (Anti-VEGF) therapy has become a standard way for choroidal neovascularization (CNV) and cystoid macular edema (CME) treatment. However, anti-VEGF injection is a long-term therapy with expensive cost and may be not effective for some patients. Therefore, predicting the effectiveness of anti-VEGF injection before the therapy is necessary. In this study, a new optical coherence tomography (OCT) images based self-supervised learning (OCT-SSL) model for predicting the effectiveness of anti-VEGF injection is developed. In OCT-SSL, we pre-train a deep encoder-decoder network through self-supervised learning to learn the general features using a public OCT image dataset. Then, model fine-tuning is performed on our own OCT dataset to learn the discriminative features to predict the effectiveness of anti-VEGF. Finally, classifier trained by the features from fine-tuned encoder as a feature extractor is built to predict the response. Experimental results on our private OCT dataset demonstrated that the proposed OCT-SSL can achieve an average accuracy, area under the curve (AUC), sensitivity and specificity of 0.93, 0.98, 0.94 and 0.91, respectively. Meanwhile, it is found that not only the lesion region but also the normal region in OCT image is related to the effectiveness of anti-VEGF.

Interior tomography of X-ray computed tomography (CT) has many advantages, such as a lower radiation dose and lower detector hardware cost compared to traditional CT. However, this imaging technique only uses the projection data passing through the region of interest (ROI) for imaging; accordingly, the projection data are truncated at both ends of the detector, so the traditional analytical reconstruction algorithm cannot satisfy the demand of clinical diagnosis. To solve the above limitations, in this paper we propose a high-quality statistical iterative reconstruction algorithm that uses the zeroth-order image moment as novel prior knowledge; the zeroth-order image moment can be estimated in the projection domain using the Helgason-Ludwig consistency condition. Then, the L 1 norm of sparse representation, in terms of dictionary learning, and the zeroth-order image moment constraints are incorporated into the statistical iterative reconstruction framework to construct an objective function. Finally, the objective function is minimized using an alternating minimization iterative algorithm. The chest CT image simulated and CT real data experimental results demonstrate that the proposed approach can remove shift artifacts effectively and has superior performance in removing noise and persevering fine structures than the total variation (TV)-based approach.