LEARN: Learned Experts' Assessment-Based Reconstruction Network for Sparse-Data CT

Low dose computed tomography reconstruction with momentum-based frequency adjustment network

BACKGROUND AND OBJECTIVE

Recent investigations into Low-Dose Computed Tomography (LDCT) reconstruction methods have brought Model-Based Data-Driven (MBDD) approaches to the forefront. One prominent architecture within MBDD entails the integration of Model-Based Iterative Reconstruction (MBIR) with Deep Learning (DL). While this approach offers the advantage of harnessing information from sinogram and image domains, it also reveals several deficiencies. First and foremost, the efficacy of DL methods within the realm of MBDD necessitates meticulous enhancement, as it directly impacts the computational cost and the quality of reconstructed images. Next, high computational costs and a high number of iterations limit the development of MBDD methods. Last but not least, CT reconstruction is sensitive to pixel accuracy, and the role of loss functions within DL methods is crucial for meeting this requirement.

METHODS

This paper advances MBDD methods through three principal contributions. Firstly, we introduce an innovative Frequency Adjustment Network (FAN) that effectively adjusts both high and low-frequency components during the inference phase, resulting in substantial enhancements in reconstruction performance. Second, we develop the Momentum-based Frequency Adjustment Network (MFAN), which leverages momentum terms as an extrapolation strategy to facilitate the amplification of changes throughout successive iterations, culminating in a rapid convergence framework. Lastly, we delve into the visual properties of CT images and present a unique loss function named Focal Detail Loss (FDL). The FDL function preserves fine details throughout the training phase, significantly improving reconstruction quality.

RESULTS

Through a series of experiments validation on the AAPM-Mayo public dataset and real-world piglet datasets, the aforementioned three contributions demonstrated superior performance. MFAN achieved convergence in 10 iterations as an iteration method, faster than other methods. Ablation studies further highlight the advanced performance of each contribution.

CONCLUSIONS

This paper presents an MBDD-based LDCT reconstruction method using a momentum-based frequency adjustment network with a focal detail loss function. This approach significantly reduces the number of iterations required for convergence while achieving superior reconstruction results in visual and numerical analyses.

Deep Radon Prior: A fully unsupervised framework for sparse-view CT reconstruction

BACKGROUND

Sparse-view computed tomography (CT) substantially reduces radiation exposure but often introduces severe artifacts that compromise image fidelity. Recent advances in deep learning for solving inverse problems have shown considerable promise in enhancing CT reconstruction; however, most approaches heavily rely on high-quality training datasets and lack interpretability.

METHOD

To address these challenges, this paper introduces a novel, fully unsupervised deep learning framework that mitigates the dependency on extensive labeled data and improves the interpretability of the reconstruction process. Specifically, we propose the Deep Radon Prior (DRP) framework, inspired by the Deep Image Prior (DIP), which integrates a neural network as an implicit prior into the iterative reconstruction process. This integration facilitates the image domain and the Radon domain gradient feedback and progressively optimizes the neural network through multiple stages, effectively narrowing the solution space in the Radon domain for under-constrained imaging protocols.

RESULTS

We discuss the convergence properties of DRP and validate our approach experimentally, demonstrating its ability to produce high-fidelity images while significantly reducing artifacts. Results indicate that DRP achieves comparable or superior performance to supervised methods, thereby addressing the inherent challenges of sparse-view CT and substantially enhancing image quality.

CONCLUSIONS

The introduction of DRP represents a significant advancement in sparse-view CT imaging by leveraging the inherent deep self-correlation of the Radon domain, enabling effective cooperation with neural network manifolds for image reconstruction. This paradigm shift toward fully unsupervised learning offers a scalable and insightful approach to medical imaging, potentially redefining the landscape of CT reconstruction.

Ultra-sparse view lung CT image reconstruction using generative adversarial networks and compressed sensing

X-ray ionizing radiation from Computed Tomography (CT) scanning increases cancer risk for patients, thus making sparse view CT, which diminishes X-ray exposure by lowering the number of projections, highly significant in diagnostic imaging. However, reducing the number of projections inherently degrades image quality, negatively impacting clinical diagnosis. Consequently, attaining reconstructed images that meet diagnostic imaging criteria for sparse view CT is challenging. This paper presents a novel network (CSUF), specifically designed for ultra-sparse view lung CT image reconstruction. The CSUF network consists of three cohesive components including (1) a compressed sensing-based CT image reconstruction module (VdCS module), (2) a U-shaped end-to-end network, CT-RDNet, enhanced with a self-attention mechanism, acting as the generator in a Generative Adversarial Network (GAN) for CT image restoration and denoising, and (3) a feedback loop. The VdCS module enriches CT-RDNet with enhanced features, while CT-RDNet supplies the VdCS module with prior images infused with rich details and minimized artifacts, facilitated by the feedback loop. Engineering simulation experimental results demonstrate the robustness of the CSUF network and its potential to deliver lung CT images with diagnostic imaging quality even under ultra-sparse view conditions.

DECT sparse reconstruction based on hybrid spectrum data generative diffusion model

PURPOSE

Dual-energy computed tomography (DECT) enables the differentiation of different materials. Additionally, DECT images consist of multiple scans of the same sample, revealing information similarity within the energy domain. To leverage this information similarity and address safety concerns related to excessive radiation exposure in DECT imaging, sparse view DECT imaging is proposed as a solution. However, this imaging method can impact image quality. Therefore, this paper presents a hybrid spectrum data generative diffusion reconstruction model (HSGDM) to improve imaging quality.

METHOD

To exploit the spectral similarity of DECT, we use interleaved angles for sparse scanning to obtain low- and high-energy CT images with complementary incomplete views. Furthermore, we organize low- and high-energy CT image views into multichannel forms for training and inference and promote information exchange between low-energy features and high-energy features, thus improving the reconstruction quality while reducing the radiation dose. In the HSGDM, we build two types of diffusion model constraint terms trained by the image space and wavelet space. The wavelet space diffusion model exploits mainly the orientation and scale features of artifacts. By integrating the image space diffusion model, we establish a hybrid constraint for the iterative reconstruction framework. Ultimately, we transform the iterative approach into a cohesive sampling process guided by the measurement data, which collaboratively produces high-quality and consistent reconstructions of sparse view DECT.

RESULTS

Compared with the comparison methods, this approach is competitive in terms of the precision of the CT values, the preservation of details, and the elimination of artifacts. In the reconstruction of 30 sparse views, with increases of 3.51 dB for the peak signal-to-noise ratio (PSNR), 0.03 for the structural similarity index measure (SSIM), and a reduction of 74.47 for the Fréchet inception distance (FID) score on the test dataset. In the ablation study, we determined the effectiveness of our proposed hybrid prior, consisting of the wavelet prior module and the image prior module, by comparing the visual effects and quantitative results of the methods using an image space model, a wavelet space model, and our hybrid model approach. Both qualitative and quantitative analyses of the results indicate that the proposed method performs well in sparse DECT reconstruction tasks.

CONCLUSION

We have developed a unified optimized mathematical model that integrates the image space and wavelet space prior knowledge into an iterative model. This model is more practical and interpretable than existing approaches are. The experimental results demonstrate the competitive performance of the proposed model.

A Beam Hardening Artifact Correction Method for CT Images Based on VGG Feature Extraction Networks

In X-ray industrial computed tomography (ICT) imaging, beam hardening artifacts significantly degrade the quality of reconstructed images, leading to cupping effects, ring artifacts, and reduced contrast resolution. These issues are particularly severe in high-density and irregularly shaped aerospace components, where accurate defect detection is critical. To mitigate beam hardening artifacts, this paper proposes a correction method based on the VGG16 feature extraction network. Continuous convolutional layers automatically extract relevant features of beam hardening artifacts, establish a nonlinear mapping between artifact-affected and artifact-free images, and progressively enhance the model's ability to understand and represent complex image features through stacked layers. Then, a dataset of ICT images with beam hardening artifacts is constructed, and VGG16 is employed to extract deep features from both artifact-affected and reference images. By incorporating perceptual loss into a convolutional neural network and optimizing through iterative training, the proposed method effectively suppresses cupping artifacts and reduces edge blurring. Experimental results demonstrated that the method significantly enhanced image contrast, reduced image noise, and restored structural details, thereby improving the reliability of ICT imaging for aerospace applications.

TD-STrans: Tri-domain sparse-view CT reconstruction based on sparse transformer

BACKGROUND AND OBJECTIVE

Sparse-view computed tomography (CT) speeds up scanning and reduces radiation exposure in medical diagnosis. However, when the projection views are severely under-sampled, deep learning-based reconstruction methods often suffer from over-smoothing of the reconstructed images due to the lack of high-frequency information. To address this issue, we introduce frequency domain information into the popular projection-image domain reconstruction, proposing a Tri-Domain sparse-view CT reconstruction model based on Sparse Transformer (TD-STrans).

METHODS

TD-STrans integrates three essential modules: the projection recovery module completes the sparse-view projection, the Fourier domain filling module mitigates artifacts and over-smoothing by filling in missing high-frequency details; the image refinement module further enhances and preserves image details. Additionally, a multi-domain joint loss function is designed to simultaneously enhance the reconstruction quality in the projection domain, image domain, and frequency domain, thereby further improving the preservation of image details.

RESULTS

The results of simulation experiments on the lymph node dataset and real experiments on the walnut dataset consistently demonstrate the effectiveness of TD-STrans in artifact removal, suppression of over-smoothing, and preservation of structural fidelity.

CONCLUSION

The reconstruction results of TD-STrans indicate that sparse transformer across multiple domains can alleviate over-smoothing and detail loss caused by reduced views, offering a novel solution for ultra-sparse-view CT imaging.

ADMM-TransNet: ADMM-Based Sparse-View CT Reconstruction Method Combining Convolution and Transformer Network

BACKGROUND

X-ray computed tomography (CT) imaging technology provides high-precision anatomical visualization of patients and has become a standard modality in clinical diagnostics. A widely adopted strategy to mitigate radiation exposure is sparse-view scanning. However, traditional iterative approaches require manual design of regularization priors and laborious parameter tuning, while deep learning methods either heavily depend on large datasets or fail to capture global image correlations.

METHODS

Therefore, this paper proposes a combination of model-driven and data-driven methods, using the ADMM iterative algorithm framework to constrain the network to reduce its dependence on data samples and introducing the CNN and Transformer model to increase the ability to learn the global and local representation of images, further improving the accuracy of the reconstructed image.

RESULTS

The quantitative and qualitative results show the effectiveness of our method for sparse-view reconstruction compared with the current most advanced reconstruction algorithms, achieving a PSNR of 42.036 dB, SSIM of 0.979, and MAE of 0.011 at 32 views.

CONCLUSIONS

The proposed algorithm has effective capability in sparse-view CT reconstruction. Compared with other deep learning algorithms, the proposed algorithm has better generalization and higher reconstruction accuracy.

Artificial Intelligence in Computed Tomography Image Reconstruction: A Review of Recent Advances

The development of novel image reconstruction algorithms has been pivotal in enhancing image quality and reducing radiation dose in computed tomography (CT) imaging. Traditional techniques like filtered back projection perform well under ideal conditions but fail to generate high-quality images under low-dose, sparse-view, and limited-angle conditions. Iterative reconstruction methods improve upon filtered back projection by incorporating system models and assumptions about the patient, yet they can suffer from patchy image textures. The emergence of artificial intelligence (AI), particularly deep learning, has further advanced CT reconstruction. AI techniques have demonstrated great potential in reducing radiation dose while preserving image quality and noise texture. Moreover, AI has exhibited unprecedented performance in addressing challenging CT reconstruction problems, including low-dose CT, sparse-view CT, limited-angle CT, and interior tomography. This review focuses on the latest advances in AI-based CT reconstruction under these challenging conditions.

Trustworthy Limited Data CT Reconstruction Using Progressive Artifact Image Learning

The reconstruction of limited data computed tomography (CT) aims to obtain high-quality images from a reduced set of projection views acquired from sparse views or limited angles. This approach is utilized to reduce radiation exposure or expedite the scanning process. Deep Learning (DL) techniques have been incorporated into limited data CT reconstruction tasks and achieve remarkable performance. However, these DL methods suffer from various limitations. Firstly, the distribution inconsistency between the simulation data and the real data hinders the generalization of these DL-based methods. Secondly, these DL-based methods could be unstable due to lack of kernel awareness. This paper addresses these issues by proposing an unrolling framework called Progressive Artifact Image Learning (PAIL) for limited data CT reconstruction. The proposed PAIL primarily consists of three key modules, i.e., a residual domain module (RDM), an image domain module (IDM), and a wavelet domain module (WDM). The RDM is designed to refine features from residual images and suppress the observable artifacts from the reconstructed images. This module could effectively alleviate the effects of distribution inconsistency among different data sets by transferring the optimization space from the original data domain to the residual data domain. The IDM is designed to suppress the unobservable artifacts in the image space. The RDM and IDM collaborate with each other during the iterative optimization process, progressively removing artifacts and reconstructing the underlying CT image. Furthermore, in order to void the potential hallucinations generated by the RDM and IDM, an additional WDM is incorporated into the network to enhance its stability. This is achieved by making the network become kernel-aware via integrating wavelet-based compressed sensing. The effectiveness of the proposed PAIL method has been consistently verified on two simulated CT data sets, a clinical cardiac data set and a sheep lung data set. Compared to other state-of-the-art methods, the proposed PAIL method achieves superior performance in various limited data CT reconstruction tasks, demonstrating its promising generalization and stability.

Hypernetwork-Based Physics-Driven Personalized Federated Learning for CT Imaging

In clinical practice, computed tomography (CT) is an important noninvasive inspection technology to provide patients' anatomical information. However, its potential radiation risk is an unavoidable problem that raises people's concerns. Recently, deep learning (DL)-based methods have achieved promising results in CT reconstruction, but these methods usually require the centralized collection of large amounts of data for training from specific scanning protocols, which leads to serious domain shift and privacy concerns. To relieve these problems, in this article, we propose a hypernetwork-based physics-driven personalized federated learning method (HyperFed) for CT imaging. The basic assumption of the proposed HyperFed is that the optimization problem for each domain can be divided into two subproblems: local data adaption and global CT imaging problems, which are implemented by an institution-specific physics-driven hypernetwork and a global-sharing imaging network, respectively. Learning stable and effective invariant features from different data distributions is the main purpose of global-sharing imaging network. Inspired by the physical process of CT imaging, we carefully design physics-driven hypernetwork for each domain to obtain hyperparameters from specific physical scanning protocol to condition the global-sharing imaging network, so that we can achieve personalized local CT reconstruction. Experiments show that HyperFed achieves competitive performance in comparison with several other state-of-the-art methods. It is believed as a promising direction to improve CT imaging quality and personalize the needs of different institutions or scanners without data sharing. Related codes have been released at https://github.com/Zi-YuanYang/HyperFed.

An efficient deep unrolling network for sparse-view CT reconstruction via alternating optimization of dense-view sinograms and images

Objective. Recently, there have been many advancements in deep unrolling methods for sparse-view computed tomography (SVCT) reconstruction. These methods combine model-based and deep learning-based reconstruction techniques, improving the interpretability and achieving significant results. However, they are often computationally expensive, particularly for clinical raw projection data with large sizes. This study aims to address this issue while maintaining the quality of the reconstructed image.Approach. The SVCT reconstruction task is decomposed into two subproblems using the proximal gradient method: optimizing dense-view sinograms and optimizing images. Then dense-view sinogram inpainting, image-residual learning, and image-refinement modules are performed at each iteration stage using deep neural networks. Unlike previous unrolling methods, the proposed method focuses on optimizing dense-view sinograms instead of full-view sinograms. This approach not only reduces computational resources and runtime but also minimizes the challenge for the network to perform sinogram inpainting when the sparse ratio is extremely small, thereby decreasing the propagation of estimation error from the sinogram domain to the image domain.Main results. The proposed method successfully reconstructs an image (512 × 512 pixels) from real-size (2304 × 736) projection data, with 3.39 M training parameters and an inference time of 0.09 s per slice on a GPU. The proposed method also achieves superior quantitative and qualitative results compared with state-of-the-art deep unrolling methods on datasets with sparse ratios of 1/12 and 1/18, especially in suppressing artifacts and preserving structural details. Additionally, results show that using dense-view sinogram inpainting not only accelerates the computational speed but also leads to faster network convergence and further improvements in reconstruction results.Significance. This research presents an efficient dual-domain deep unrolling technique that produces excellent results in SVCT reconstruction while requiring small computational resources. These findings have important implications for speeding up deep unrolling CT reconstruction methods and making them more practical for processing clinical CT projection data.

MAFA-Uformer: Multi-attention and dual-branch feature aggregation U-shaped transformer for sparse-view CT reconstruction

BACKGROUND

Although computed tomography (CT) is widely employed in disease detection, X-ray radiation may pose a risk to the health of patients. Reducing the projection views is a common method, however, the reconstructed images often suffer from streak artifacts.

PURPOSE

In previous related works, it can be found that the convolutional neural network (CNN) is proficient in extracting local features, while the Transformer is adept at capturing global information. To suppress streak artifacts for sparse-view CT, this study aims to develop a method that combines the advantages of CNN and Transformer.

METHODS

In this paper, we propose a Multi-Attention and Dual-Branch Feature Aggregation U-shaped Transformer network (MAFA-Uformer), which consists of two branches: CNN and Transformer. Firstly, with a coordinate attention mechanism, the Transformer branch can capture the overall structure and orientation information to provide a global context understanding of the image under reconstruction. Secondly, the CNN branch focuses on extracting crucial local features of images through channel spatial attention, thus enhancing detail recognition capabilities. Finally, through a feature fusion module, the global information from the Transformer and the local features from the CNN are integrated effectively.

RESULTS

Experimental results demonstrate that our method achieves outstanding performance in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE). Compared with Restormer, our model achieves significant improvements: PSNR increases by 0.76 dB, SSIM improves by 0.44%, and RMSE decreases by 8.55%.

CONCLUSION

Our method not only effectively suppresses artifacts but also better preserves details and features, thereby providing robust support for accurate diagnosis of CT images.

Reconstruction of Optical Coherence Tomography Images from Wavelength Space Using Deep Learning

Conventional Fourier domain Optical Coherence Tomography (FD-OCT) systems depend on resampling into a wavenumber (k) domain to extract the depth profile. This either necessitates additional hardware resources or amplifies the existing computational complexity. Moreover, the OCT images also suffer from speckle noise, due to systemic reliance on low-coherence interferometry. We propose a streamlined and computationally efficient approach based on Deep Learning (DL) which enables reconstructing speckle-reduced OCT images directly from the wavelength (λ) domain. For reconstruction, two encoder-decoder styled networks, namely Spatial Domain Convolution Neural Network (SD-CNN) and Fourier Domain CNN (FD-CNN), are used sequentially. The SD-CNN exploits the highly degraded images obtained by Fourier transforming the (λ) domain fringes to reconstruct the deteriorated morphological structures along with suppression of unwanted noise. The FD-CNN leverages this output to enhance the image quality further by optimization in the Fourier domain (FD). We quantitatively and visually demonstrate the efficacy of the method in obtaining high-quality OCT images. Furthermore, we illustrate the computational complexity reduction by harnessing the power of DL models. We believe that this work lays the framework for further innovations in the realm of OCT image reconstruction.

SOUL-Net: A Sparse and Low-Rank Unrolling Network for Spectral CT Image Reconstruction

Spectral computed tomography (CT) is an emerging technology, that generates a multienergy attenuation map for the interior of an object and extends the traditional image volume into a 4-D form. Compared with traditional CT based on energy-integrating detectors, spectral CT can make full use of spectral information, resulting in high resolution and providing accurate material quantification. Numerous model-based iterative reconstruction methods have been proposed for spectral CT reconstruction. However, these methods usually suffer from difficulties such as laborious parameter selection and expensive computational costs. In addition, due to the image similarity of different energy bins, spectral CT usually implies a strong low-rank prior, which has been widely adopted in current iterative reconstruction models. Singular value thresholding (SVT) is an effective algorithm to solve the low-rank constrained model. However, the SVT method requires a manual selection of thresholds, which may lead to suboptimal results. To relieve these problems, in this article, we propose a sparse and low-rank unrolling network (SOUL-Net) for spectral CT image reconstruction, that learns the parameters and thresholds in a data-driven manner. Furthermore, a Taylor expansion-based neural network backpropagation method is introduced to improve the numerical stability. The qualitative and quantitative results demonstrate that the proposed method outperforms several representative state-of-the-art algorithms in terms of detail preservation and artifact reduction.

TMAA-net: tensor-domain multi-planal anti-aliasing network for sparse-view CT image reconstruction

Objective.Among various deep-network-based sparse-view CT image reconstruction studies, the sinogram upscaling network has been predominantly employed to synthesize additional view information. However, the performance of the sinogram-based network is limited in terms of removing aliasing streak artifacts and recovering low-contrast small structures. In this study, we used a view-by-view back-projection (VVBP) tensor-domain network to overcome such limitations of the sinogram-based approaches.Approach.The proposed method offers advantages of addressing the aliasing artifacts directly in the 3D tensor domain over the 2D sinogram. In the tensor-domain network, the multi-planal anti-aliasing modules were used to remove artifacts within the coronal and sagittal tensor planes. In addition, the data-fidelity-based refinement module was also implemented to successively process output images of the tensor network to recover image sharpness and textures.Main result.The proposed method showed outperformance in terms of removing aliasing artifacts and recovering low-contrast details compared to other state-of-the-art sinogram-based networks. The performance was validated for both numerical and clinical projection data in a circular fan-beam CT configuration.Significance.We observed that view-by-view aliasing artifacts in sparse-view CT exhibit distinct patterns within the tensor planes, making them effectively removable in high-dimensional representations. Additionally, we demonstrated that the co-domain characteristics of tensor space processing offer higher generalization performance for aliasing artifact removal compared to conventional sinogram-domain processing.

Artificial intelligence application in the diagnosis and treatment of bladder cancer: advance, challenges, and opportunities

Bladder cancer (BC) is a serious and common malignant tumor of the urinary system. Accurate and convenient diagnosis and treatment of BC is a major challenge for the medical community. Due to the limited medical resources, the existing diagnosis and treatment protocols for BC without the assistance of artificial intelligence (AI) still have certain shortcomings. In recent years, with the development of AI technologies such as deep learning and machine learning, the maturity of AI has made it more and more applied to the medical field, including improving the speed and accuracy of BC diagnosis and providing more powerful treatment options and recommendations related to prognosis. Advances in medical imaging technology and molecular-level research have also contributed to the further development of such AI applications. However, due to differences in the sources of training information and algorithm design issues, there is still room for improvement in terms of accuracy and transparency for the broader use of AI in clinical practice. With the popularization of digitization of clinical information and the proposal of new algorithms, artificial intelligence is expected to learn more effectively and analyze similar cases more accurately and reliably, promoting the development of precision medicine, reducing resource consumption, and speeding up diagnosis and treatment. This review focuses on the application of artificial intelligence in the diagnosis and treatment of BC, points out some of the challenges it faces, and looks forward to its future development.

Learned Tensor Neural Network Texture Prior for Photon-Counting CT Reconstruction

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.

Self-supervised learning for CT image denoising and reconstruction: a review

This article reviews the self-supervised learning methods for CT image denoising and reconstruction. Currently, deep learning has become a dominant tool in medical imaging as well as computer vision. In particular, self-supervised learning approaches have attracted great attention as a technique for learning CT images without clean/noisy references. After briefly reviewing the fundamentals of CT image denoising and reconstruction, we examine the progress of deep learning in CT image denoising and reconstruction. Finally, we focus on the theoretical and methodological evolution of self-supervised learning for image denoising and reconstruction.

Cross-Domain Denoising for Low-Dose Multi-Frame Spiral Computed Tomography

Computed tomography (CT) has been used worldwide as a non-invasive test to assist in diagnosis. However, the ionizing nature of X-ray exposure raises concerns about potential health risks such as cancer. The desire for lower radiation doses has driven researchers to improve reconstruction quality. Although previous studies on low-dose computed tomography (LDCT) denoising have demonstrated the effectiveness of learning-based methods, most were developed on the simulated data. However, the real-world scenario differs significantly from the simulation domain, especially when using the multi-slice spiral scanner geometry. This paper proposes a two-stage method for the commercially available multi-slice spiral CT scanners that better exploits the complete reconstruction pipeline for LDCT denoising across different domains. Our approach makes good use of the high redundancy of multi-slice projections and the volumetric reconstructions while leveraging the over-smoothing issue in conventional cascaded frameworks caused by aggressive denoising. The dedicated design also provides a more explicit interpretation of the data flow. Extensive experiments on various datasets showed that the proposed method could remove up to 70% of noise without compromised spatial resolution, while subjective evaluations by two experienced radiologists further supported its superior performance against state-of-the-art methods in clinical practice. Code is available at https://github.com/YCL92/TMD-LDCT.

Multi-Channel Optimization Generative Model for Stable Ultra-Sparse-View CT Reconstruction

Score-based generative model (SGM) has risen to prominence in sparse-view CT reconstruction due to its impressive generation capability. The consistency of data is crucial in guiding the reconstruction process in SGM-based reconstruction methods. However, the existing data consistency policy exhibits certain limitations. Firstly, it employs partial data from the reconstructed image of the iteration process for image updates, which leads to secondary artifacts with compromising image quality. Moreover, the updates to the SGM and data consistency are considered as distinct stages, disregarding their interdependent relationship. Additionally, the reference image used to compute gradients in the reconstruction process is derived from the intermediate result rather than ground truth. Motivated by the fact that a typical SGM yields distinct outcomes with different random noise inputs, we propose a Multi-channel Optimization Generative Model (MOGM) for stable ultra-sparse-view CT reconstruction by integrating a novel data consistency term into the stochastic differential equation model. Notably, the unique aspect of this data consistency component is its exclusive reliance on original data for effectively confining generation outcomes. Furthermore, we pioneer an inference strategy that traces back from the current iteration result to ground truth, enhancing reconstruction stability through foundational theoretical support. We also establish a multi-channel optimization reconstruction framework, where conventional iterative techniques are employed to seek the reconstruction solution. Quantitative and qualitative assessments on 23 views datasets from numerical simulation, clinical cardiac and sheep's lung underscore the superiority of MOGM over alternative methods. Reconstructing from just 10 and 7 views, our method consistently demonstrates exceptional performance.

Dual-Domain Collaborative Diffusion Sampling for Multi-Source Stationary Computed Tomography Reconstruction

The multi-source stationary CT, where both the detector and X-ray source are fixed, represents a novel imaging system with high temporal resolution that has garnered significant interest. Limited space within the system restricts the number of X-ray sources, leading to sparse-view CT imaging challenges. Recent diffusion models for reconstructing sparse-view CT have generally focused separately on sinogram or image domains. Sinogram-centric models effectively estimate missing projections but may introduce artifacts, lacking mechanisms to ensure image correctness. Conversely, image-domain models, while capturing detailed image features, often struggle with complex data distribution, leading to inaccuracies in projections. Addressing these issues, the Dual-domain Collaborative Diffusion Sampling (DCDS) model integrates sinogram and image domain diffusion processes for enhanced sparse-view reconstruction. This model combines the strengths of both domains in an optimized mathematical framework. A collaborative diffusion mechanism underpins this model, improving sinogram recovery and image generative capabilities. This mechanism facilitates feedback-driven image generation from the sinogram domain and uses image domain results to complete missing projections. Optimization of the DCDS model is further achieved through the alternative direction iteration method, focusing on data consistency updates. Extensive testing, including numerical simulations, real phantoms, and clinical cardiac datasets, demonstrates the DCDS model's effectiveness. It consistently outperforms various state-of-the-art benchmarks, delivering exceptional reconstruction quality and precise sinogram.

Wavelet-Inspired Multi-Channel Score-Based Model for Limited-Angle CT Reconstruction

Score-based generative model (SGM) has demonstrated great potential in the challenging limited-angle CT (LA-CT) reconstruction. SGM essentially models the probability density of the ground truth data and generates reconstruction results by sampling from it. Nevertheless, direct application of the existing SGM methods to LA-CT suffers multiple limitations. Firstly, the directional distribution of the artifacts attributing to the missing angles is ignored. Secondly, the different distribution properties of the artifacts in different frequency components have not been fully explored. These drawbacks would inevitably degrade the estimation of the probability density and the reconstruction results. After an in-depth analysis of these factors, this paper proposes a Wavelet-Inspired Score-based Model (WISM) for LA-CT reconstruction. Specifically, besides training a typical SGM with the original images, the proposed method additionally performs the wavelet transform and models the probability density in each wavelet component with an extra SGM. The wavelet components preserve the spatial correspondence with the original image while performing frequency decomposition, thereby keeping the directional property of the artifacts for further analysis. On the other hand, different wavelet components possess more specific contents of the original image in different frequency ranges, simplifying the probability density modeling by decomposing the overall density into component-wise ones. The resulting two SGMs in the image-domain and wavelet-domain are integrated into a unified sampling process under the guidance of the observation data, jointly generating high-quality and consistent LA-CT reconstructions. The experimental evaluation on various datasets consistently verifies the superior performance of the proposed method over the competing method.

Sparse-View Spectral CT Reconstruction and Material Decomposition Based on Multi-Channel SGM

In medical applications, the diffusion of contrast agents in tissue can reflect the physiological function of organisms, so it is valuable to quantify the distribution and content of contrast agents in the body over a period. Spectral CT has the advantages of multi-energy projection acquisition and material decomposition, which can quantify K-edge contrast agents. However, multiple repetitive spectral CT scans can cause excessive radiation doses. Sparse-view scanning is commonly used to reduce dose and scan time, but its reconstructed images are usually accompanied by streaking artifacts, which leads to inaccurate quantification of the contrast agents. To solve this problem, an unsupervised sparse-view spectral CT reconstruction and material decomposition algorithm based on the multi-channel score-based generative model (SGM) is proposed in this paper. First, multi-energy images and tissue images are used as multi-channel input data for SGM training. Secondly, the organism is multiply scanned in sparse views, and the trained SGM is utilized to generate multi-energy images and tissue images driven by sparse-view projections. After that, a material decomposition algorithm using tissue images generated by SGM as prior images for solving contrast agent images is established. Finally, the distribution and content of the contrast agents are obtained. The comparison and evaluation of this method are given in this paper, and a series of mouse scanning experiments are carried out to verify the effectiveness of the method.

Systematic Review on Learning-based Spectral CT

Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.

DuDoCFNet: Dual-Domain Coarse-to-Fine Progressive Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECT

Single-Photon Emission Computed Tomography (SPECT) is widely applied for the diagnosis of coronary artery diseases. Low-dose (LD) SPECT aims to minimize radiation exposure but leads to increased image noise. Limited-view (LV) SPECT, such as the latest GE MyoSPECT ES system, enables accelerated scanning and reduces hardware expenses but degrades reconstruction accuracy. Additionally, Computed Tomography (CT) is commonly used to derive attenuation maps ( μ -maps) for attenuation correction (AC) of cardiac SPECT, but it will introduce additional radiation exposure and SPECT-CT misalignments. Although various methods have been developed to solely focus on LD denoising, LV reconstruction, or CT-free AC in SPECT, the solution for simultaneously addressing these tasks remains challenging and under-explored. Furthermore, it is essential to explore the potential of fusing cross-domain and cross-modality information across these interrelated tasks to further enhance the accuracy of each task. Thus, we propose a Dual-Domain Coarse-to-Fine Progressive Network (DuDoCFNet), a multi-task learning method for simultaneous LD denoising, LV reconstruction, and CT-free μ -map generation of cardiac SPECT. Paired dual-domain networks in DuDoCFNet are cascaded using a multi-layer fusion mechanism for cross-domain and cross-modality feature fusion. Two-stage progressive learning strategies are applied in both projection and image domains to achieve coarse-to-fine estimations of SPECT projections and CT-derived μ -maps. Our experiments demonstrate DuDoCFNet's superior accuracy in estimating projections, generating μ -maps, and AC reconstructions compared to existing single- or multi-task learning methods, under various iterations and LD levels. The source code of this work is available at https://github.com/XiongchaoChen/DuDoCFNet-MultiTask.

Linear diffusion noise boosted deep image prior for unsupervised sparse-view CT reconstruction

Objective.Deep learning has markedly enhanced the performance of sparse-view computed tomography reconstruction. However, the dependence of these methods on supervised training using high-quality paired datasets, and the necessity for retraining under varied physical acquisition conditions, constrain their generalizability across new imaging contexts and settings.Approach.To overcome these limitations, we propose an unsupervised approach grounded in the deep image prior framework. Our approach advances beyond the conventional single noise level input by incorporating multi-level linear diffusion noise, significantly mitigating the risk of overfitting. Furthermore, we embed non-local self-similarity as a deep implicit prior within a self-attention network structure, improving the model's capability to identify and utilize repetitive patterns throughout the image. Additionally, leveraging imaging physics, gradient backpropagation is performed between the image domain and projection data space to optimize network weights.Main Results.Evaluations with both simulated and clinical cases demonstrate our method's effective zero-shot adaptability across various projection views, highlighting its robustness and flexibility. Additionally, our approach effectively eliminates noise and streak artifacts while significantly restoring intricate image details.Significance. Our method aims to overcome the limitations in current supervised deep learning-based sparse-view CT reconstruction, offering improved generalizability and adaptability without the need for extensive paired training data.

Learning a stable approximation of an existing but unknown inverse mapping: application to the half-time circular Radon transform

Supervised deep learning-based methods have inspired a new wave of image reconstruction methods that implicitly learn effective regularization strategies from a set of training data. While they hold potential for improving image quality, they have also raised concerns regarding their robustness. Instabilities can manifest when learned methods are applied to find approximate solutions to ill-posed image reconstruction problems for which a unique and stable inverse mapping does not exist, which is a typical use case. In this study, we investigate the performance of supervised deep learning-based image reconstruction in an alternate use case in which a stable inverse mapping is known to exist but is not yet analytically available in closed form. For such problems, a deep learning-based method can learn a stable approximation of the unknown inverse mapping that generalizes well to data that differ significantly from the training set. The learned approximation of the inverse mapping eliminates the need to employ an implicit (optimization-based) reconstruction method and can potentially yield insights into the unknown analytic inverse formula. The specific problem addressed is image reconstruction from a particular case of radially truncated circular Radon transform (CRT) data, referred to as 'half-time' measurement data. For the half-time image reconstruction problem, we develop and investigate a learned filtered backprojection method that employs a convolutional neural network to approximate the unknown filtering operation. We demonstrate that this method behaves stably and readily generalizes to data that differ significantly from training data. The developed method may find application to wave-based imaging modalities that include photoacoustic computed tomography.

Compressed-sensing-inspired reconstruction algorithms in low-dose computed tomography: A review

BACKGROUND

Optimization of the dose the patient receives during scanning is an important problem in modern medical X-ray computed tomography (CT). One of the basic ways to its solution is to reduce the number of views. Compressed sensing theory helped promote the development of a new class of effective reconstruction algorithms for limited data CT. These compressed-sensing-inspired (CSI) algorithms optimize the Lp (0 ≤ p ≤ 1) norm of images and can accurately reconstruct CT tomograms from a very few views. The paper presents a review of the CSI algorithms and discusses prospects for their further use in commercial low-dose CT.

METHODS

Many literature references with the CSI algorithms have been were searched. To structure the material collected the author gives a classification framework within which he describes Lp regularization methods, the basic CSI algorithms that are used most often in few-view CT, and some of their derivatives. Lots of examples are provided to illustrate the use of the CSI algorithms in few-view and low-dose CT.

RESULTS

A list of the CSI algorithms is compiled from the literature search. For better demonstrativeness they are summarized in a table. The inference is done that already today some of the algorithms are capable of reconstruction from 20 to 30 views with acceptable quality and dose reduction by a factor of 10.

DISCUSSION

In conclusion the author discusses how soon the CSI reconstruction algorithms can be introduced in the practice of medical diagnosis and used in commercial CT scanners.

HEAL: High-Frequency Enhanced and Attention-Guided Learning Network for Sparse-View CT Reconstruction

X-ray computed tomography (CT) imaging technology has become an indispensable diagnostic tool in clinical examination. However, it poses a risk of ionizing radiation, making the reduction of radiation dose one of the current research hotspots in CT imaging. Sparse-view imaging, as one of the main methods for reducing radiation dose, has made significant progress in recent years. In particular, sparse-view reconstruction methods based on deep learning have shown promising results. Nevertheless, efficiently recovering image details under ultra-sparse conditions remains a challenge. To address this challenge, this paper proposes a high-frequency enhanced and attention-guided learning Network (HEAL). HEAL includes three optimization strategies to achieve detail enhancement: Firstly, we introduce a dual-domain progressive enhancement module, which leverages fidelity constraints within each domain and consistency constraints across domains to effectively narrow the solution space. Secondly, we incorporate both channel and spatial attention mechanisms to improve the network's feature-scaling process. Finally, we propose a high-frequency component enhancement regularization term that integrates residual learning with direction-weighted total variation, utilizing directional cues to effectively distinguish between noise and textures. The HEAL network is trained, validated and tested under different ultra-sparse configurations of 60 views and 30 views, demonstrating its advantages in reconstruction accuracy and detail enhancement.

Directional TV algorithm for image reconstruction from sparse-view projections in EPR imaging

Objective.Electron paramagnetic resonance (EPR) imaging is an advanced in vivo oxygen imaging modality. The main drawback of EPR imaging is the long scanning time. Sparse-view projections collection is an effective fast scanning pattern. However, the commonly-used filtered back projection (FBP) algorithm is not competent to accurately reconstruct images from sparse-view projections because of the severe streak artifacts. The aim of this work is to develop an advanced algorithm for sparse reconstruction of 3D EPR imaging.Methods.The optimization based algorithms including the total variation (TV) algorithm have proven to be effective in sparse reconstruction in EPR imaging. To further improve the reconstruction accuracy, we propose the directional TV (DTV) model and derive its Chambolle-Pock solving algorithm.Results.After the algorithm correctness validation on simulation data, we explore the sparse reconstruction capability of the DTV algorithm via a simulated six-sphere phantom and two real bottle phantoms filled with OX063 trityl solution and scanned by an EPR imager with a magnetic field strength of 250 G.Conclusion.Both the simulated and real data experiments show that the DTV algorithm is superior to the existing FBP and TV-type algorithms and a deep learning based method according to visual inspection and quantitative evaluations in sparse reconstruction of EPR imaging.Significance.These insights gained in this work may be used in the development of fast EPR imaging workflow of practical significance.

Noise-Generating and Imaging Mechanism Inspired Implicit Regularization Learning Network for Low Dose CT Reconstrution

Low-dose computed tomography (LDCT) helps to reduce radiation risks in CT scanning while maintaining image quality, which involves a consistent pursuit of lower incident rays and higher reconstruction performance. Although deep learning approaches have achieved encouraging success in LDCT reconstruction, most of them treat the task as a general inverse problem in either the image domain or the dual (sinogram and image) domains. Such frameworks have not considered the original noise generation of the projection data and suffer from limited performance improvement for the LDCT task. In this paper, we propose a novel reconstruction model based on noise-generating and imaging mechanism in full-domain, which fully considers the statistical properties of intrinsic noises in LDCT and prior information in sinogram and image domains. To solve the model, we propose an optimization algorithm based on the proximal gradient technique. Specifically, we derive the approximate solutions of the integer programming problem on the projection data theoretically. Instead of hand-crafting the sinogram and image regularizers, we propose to unroll the optimization algorithm to be a deep network. The network implicitly learns the proximal operators of sinogram and image regularizers with two deep neural networks, providing a more interpretable and effective reconstruction procedure. Numerical results demonstrate our proposed method improvements of > 2.9 dB in peak signal to noise ratio, > 1.4% promotion in structural similarity metric, and > 9 HU decrements in root mean square error over current state-of-the-art LDCT methods.

Hybrid-supervised deep learning for domain transfer 3D protoacoustic image reconstruction

Objective. Protoacoustic imaging showed great promise in providing real-time 3D dose verification of proton therapy. However, the limited acquisition angle in protoacoustic imaging induces severe artifacts, which impairs its accuracy for dose verification. In this study, we developed a hybrid-supervised deep learning method for protoacoustic imaging to address the limited view issue.Approach. We proposed a Recon-Enhance two-stage deep learning method. In the Recon-stage, a transformer-based network was developed to reconstruct initial pressure maps from raw acoustic signals. The network is trained in a hybrid-supervised approach, where it is first trained using supervision by the iteratively reconstructed pressure map and then fine-tuned using transfer learning and self-supervision based on the data fidelity constraint. In the enhance-stage, a 3D U-net is applied to further enhance the image quality with supervision from the ground truth pressure map. The final protoacoustic images are then converted to dose for proton verification.Main results. The results evaluated on a dataset of 126 prostate cancer patients achieved an average root mean squared errors (RMSE) of 0.0292, and an average structural similarity index measure (SSIM) of 0.9618, out-performing related start-of-the-art methods. Qualitative results also demonstrated that our approach addressed the limit-view issue with more details reconstructed. Dose verification achieved an average RMSE of 0.018, and an average SSIM of 0.9891. Gamma index evaluation demonstrated a high agreement (94.7% and 95.7% for 1%/3 mm and 1%/5 mm) between the predicted and the ground truth dose maps. Notably, the processing time was reduced to 6 s, demonstrating its feasibility for online 3D dose verification for prostate proton therapy.Significance. Our study achieved start-of-the-art performance in the challenging task of direct reconstruction from radiofrequency signals, demonstrating the great promise of PA imaging as a highly efficient and accurate tool forinvivo3D proton dose verification to minimize the range uncertainties of proton therapy to improve its precision and outcomes.

Semi-supervised iterative adaptive network for low-dose CT sinogram recovery

Background.Concern has been expressed regarding the risk of carcinogenesis from medical computed tomography (CT) radiation. Lowering radiation in CT without appropriate modifications often leads to severe noise-induced artifacts in the images. The utilization of deep learning (DL) techniques has achieved promising reconstruction performance in low-dose CT (LDCT) imaging. However, most DL-based algorithms require the pre-collection of a large set of image pairs (low-dose/standard-dose) and the training of networks in an end-to-end supervised manner. Meanwhile, securing such a large volume of paired, well-registered training data in clinical practice is challenging. Moreover, these algorithms often overlook the potential to utilize the abundant information in a large collection of LDCT-only images/sinograms.Methods.In this paper, we introduce a semi-supervised iterative adaptive network (SIA-Net) for LDCT imaging, utilizing both labeled and unlabeled sinograms in a cohesive network framework, integrating supervised and unsupervised learning processes. Specifically, the supervised process captures critical features (i.e. noise distribution and tissue characteristics) latent in the paired sinograms, while the unsupervised process effectively learns these features in the unlabeled low-dose sinograms, employing a conventional weighted least-squares model with a regularization term. Furthermore, the SIA-Net method is designed to adaptively transfer the learned feature distribution from the supervised to the unsupervised process, thereby obtaining a high-fidelity sinogram through iterative adaptive learning. Finally, high-quality CT images can be reconstructed from the refined sinogram using the filtered back-projection algorithm.Results.Experimental results on two clinical datasets indicate that the proposed SIA-Net method achieves competitive performance in terms of noise reduction and structure preservation in LDCT imaging, when compared to traditional supervised learning methods.

SWISTA-Nets: Subband-adaptive wavelet iterative shrinkage thresholding networks for image reconstruction

Robust and interpretable image reconstruction is central to imageology applications in clinical practice. Prevalent deep networks, with strong learning ability to extract implicit information from data manifold, are still lack of prior knowledge introduced from mathematics or physics, leading to instability, poor structure interpretability and high computation cost. As to this issue, we propose two prior knowledge-driven networks to combine the good interpretability of mathematical methods and the powerful learnability of deep learning methods. Incorporating different kinds of prior knowledge, we propose subband-adaptive wavelet iterative shrinkage thresholding networks (SWISTA-Nets), where almost every network module is in one-to-one correspondence with each step involved in the iterative algorithm. By end-to-end training of proposed SWISTA-Nets, implicit information can be extracted from training data and guide the tuning process of key parameters that possess mathematical definition. The inverse problems associated with two medical imaging modalities, i.e., electromagnetic tomography and X-ray computational tomography are applied to validate the proposed networks. Both visual and quantitative results indicate that the SWISTA-Nets outperform mathematical methods and state-of-the-art prior knowledge-driven networks, especially with fewer training parameters, interpretable network structures and well robustness. We assume that our analysis will support further investigation of prior knowledge-driven networks in the field of ill-posed image reconstruction.

AIRPORT: A Data Consistency Constrained Deep Temporal Extrapolation Method To Improve Temporal Resolution In Contrast Enhanced CT Imaging

Typical tomographic image reconstruction methods require that the imaged object is static and stationary during the time window to acquire a minimally complete data set. The violation of this requirement leads to temporal-averaging errors in the reconstructed images. For a fixed gantry rotation speed, to reduce the errors, it is desired to reconstruct images using data acquired over a narrower angular range, i.e., with a higher temporal resolution. However, image reconstruction with a narrower angular range violates the data sufficiency condition, resulting in severe data-insufficiency-induced errors. The purpose of this work is to decouple the trade-off between these two types of errors in contrast-enhanced computed tomography (CT) imaging. We demonstrated that using the developed data consistency constrained deep temporal extrapolation method (AIRPORT), the entire time-varying imaged object can be accurately reconstructed with 40 frames-per-second temporal resolution, the time window needed to acquire a single projection view data using a typical C-arm cone-beam CT system. AIRPORT is applicable to general non-sparse imaging tasks using a single short-scan data acquisition.

Deep Few-view High-resolution Photon-counting Extremity CT at Halved Dose for a Clinical Trial

The latest X-ray photon-counting computed tomography (PCCT) for extremity allows multi-energy high-resolution (HR) imaging for tissue characterization and material decomposition. However, both radiation dose and imaging speed need improvement for contrast-enhanced and other studies. Despite the success of deep learning methods for 2D few-view reconstruction, applying them to HR volumetric reconstruction of extremity scans for clinical diagnosis has been limited due to GPU memory constraints, training data scarcity, and domain gap issues. In this paper, we propose a deep learning-based approach for PCCT image reconstruction at halved dose and doubled speed in a New Zealand clinical trial. Particularly, we present a patch-based volumetric refinement network to alleviate the GPU memory limitation, train network with synthetic data, and use model-based iterative refinement to bridge the gap between synthetic and real-world data. The simulation and phantom experiments demonstrate consistently improved results under different acquisition conditions on both in- and off-domain structures using a fixed network. The image quality of 8 patients from the clinical trial are evaluated by three radiologists in comparison with the standard image reconstruction with a full-view dataset. It is shown that our proposed approach is essentially identical to or better than the clinical benchmark in terms of diagnostic image quality scores. Our approach has a great potential to improve the safety and efficiency of PCCT without compromising image quality.

A model-based direct inversion network (MDIN) for dual spectral computed tomography

Objective. Dual spectral computed tomography (DSCT) is a very challenging problem in the field of imaging. Due to the nonlinearity of its mathematical model, the images reconstructed by the conventional CT usually suffer from the beam hardening artifacts. Additionally, several existing DSCT methods rely heavily on the information of the spectra, which is often not readily available in applications. To address this problem, in this study, we aim to develop a novel approach to improve the DSCT reconstruction performance.Approach. A model-based direct inversion network (MDIN) is proposed for DSCT, which can directly predict the basis material images from the collected polychromatic projections. The all operations are performed in the network, requiring neither the conventional algorithms nor the information of the spectra. It can be viewed as an approximation to the inverse procedure of DSCT imaging model. The MDIN is composed of projection pre-decomposition module (PD-module), domain transformation layer (DT-layer), and image post-decomposition module (ID-module). The PD-module first performs the pre-decomposition on the polychromatic projections that consists of a series of stacked one-dimensional convolution layers. The DT-layer is designed to obtain the preliminary decomposed results, which has the characteristics of sparsely connected and learnable parameters. And the ID-module uses a deep neural network to further decompose the reconstructed results of the DT-layer so as to achieve higher-quality basis material images.Main results. Numerical experiments demonstrate that the proposed MDIN has significant advantages in substance decomposition, artifact reduction and noise suppression compared to other methods in the DSCT reconstruction.Significance. The proposed method has a flexible applicability, which can be extended to other CT problems, such as multi-spectral CT and low dose CT.

Deep convolutional dictionary learning network for sparse view CT reconstruction with a group sparse prior

Purpose Numerous techniques based on deep learning have been utilized in sparse view computed tomography (CT) imaging. Nevertheless, the majority of techniques are instinctively constructed utilizing state-of-the-art opaque convolutional neural networks (CNNs) and lack interpretability. Moreover, CNNs tend to focus on local receptive fields and neglect nonlocal self-similarity prior information. Obtaining diagnostically valuable images from sparsely sampled projections is a challenging and ill-posed task. Method To address this issue, we propose a unique and understandable model named DCDL-GS for sparse view CT imaging. This model relies on a network comprised of convolutional dictionary learning and a nonlocal group sparse prior. To enhance the quality of image reconstruction, we utilize a neural network in conjunction with a statistical iterative reconstruction framework and perform a set number of iterations. Inspired by group sparsity priors, we adopt a novel group thresholding operation to improve the feature representation and constraint ability and obtain a theoretical interpretation. Furthermore, our DCDL-GS model incorporates filtered backprojection (FBP) reconstruction, fast sliding window nonlocal self-similarity operations, and a lightweight and interpretable convolutional dictionary learning network to enhance the applicability of the model. Results The efficiency of our proposed DCDL-GS model in preserving edges and recovering features is demonstrated by the visual results obtained on the LDCT-P and UIH datasets. Compared to the results of the most advanced techniques, the quantitative results are enhanced, with increases of 0.6-0.8 dB for the peak signal-to-noise ratio (PSNR), 0.005-0.01 for the structural similarity index measure (SSIM), and 1-1.3 for the regulated Fréchet inception distance (rFID) on the test dataset. The quantitative results also show the effectiveness of our proposed deep convolution iterative reconstruction module and nonlocal group sparse prior. Conclusion In this paper, we create a consolidated and enhanced mathematical model by integrating projection data and prior knowledge of images into a deep iterative model. The model is more practical and interpretable than existing approaches. The results from the experiment show that the proposed model performs well in comparison to the others.

Systematic Review on Learning-based Spectral CT

Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.

Pediatric evaluations for deep learning CT denoising

BACKGROUND

Deep learning (DL) CT denoising models have the potential to improve image quality for lower radiation dose exams. These models are generally trained with large quantities of adult patient image data. However, CT, and increasingly DL denoising methods, are used in both adult and pediatric populations. Pediatric body habitus and size can differ significantly from adults and vary dramatically from newborns to adolescents. Ensuring that pediatric subgroups of different body sizes are not disadvantaged by DL methods requires evaluations capable of assessing performance in each subgroup.

PURPOSE

To assess DL CT denoising in pediatric and adult-sized patients, we built a framework of computer simulated image quality (IQ) control phantoms and evaluation methodology.

METHODS

The computer simulated IQ phantoms in the framework featured pediatric-sized versions of standard CatPhan 600 and MITA-LCD phantoms with a range of diameters matching the mean effective diameters of pediatric patients ranging from newborns to 18 years old. These phantoms were used in simulating CT images that were then inputs for a DL denoiser to evaluate performance in different sized patients. Adult CT test images were simulated using standard-sized phantoms scanned with adult scan protocols. Pediatric CT test images were simulated with pediatric-sized phantoms and adjusted pediatric protocols. The framework's evaluation methodology consisted of denoising both adult and pediatric test images then assessing changes in image quality, including noise, image sharpness, CT number accuracy, and low contrast detectability. To demonstrate the use of the framework, a REDCNN denoising model trained on adult patient images was evaluated. To validate that the DL model performance measured with the proposed pediatric IQ phantoms was representative of performance in more realistic patient anatomy, anthropomorphic pediatric XCAT phantoms of the same age range were also used to compare noise reduction performance.

RESULTS

Using the proposed pediatric-sized IQ phantom framework, size differences between adult and pediatric-sized phantoms were observed to substantially influence the adult trained DL denoising model's performance. When applied to adult images, the DL model achieved a 60% reduction in noise standard deviation without substantial loss in sharpness in mid or high spatial frequencies. However, in smaller phantoms the denoising performance dropped due to different image noise textures resulting from the smaller field of view (FOV) between adult and pediatric protocols. In the validation study, noise reduction trends in the pediatric-sized IQ phantoms were found to be consistent with those found in anthropomorphic phantoms.

CONCLUSION

We developed a framework of using pediatric-sized IQ phantoms for pediatric subgroup evaluation of DL denoising models. Using the framework, we found the performance of an adult trained DL denoiser did not generalize well in the smaller diameter phantoms corresponding to younger pediatric patient sizes. Our work suggests noise texture differences from FOV changes between adult and pediatric protocols can contribute to poor generalizability in DL denoising and that the proposed framework is an effective means to identify these performance disparities for a given model.

Fourier Diffusion for Sparse CT Reconstruction

Sparse CT reconstruction continues to be an area of interest in a number of novel imaging systems. Many different approaches have been tried including model-based methods, compressed sensing approaches, and most recently deep-learning-based processing. Diffusion models, in particular, have become extremely popular due to their ability to effectively encode rich information about images and to allow for posterior sampling to generate many possible outputs. One drawback of diffusion models is that their recurrent structure tends to be computationally expensive. In this work we apply a new Fourier diffusion approach that permits processing with many fewer time steps than the standard scalar diffusion model. We present an extension of the Fourier diffusion technique and evaluate it in a simulated breast cone-beam CT system with a sparse view acquisition.

Dual-Domain Reconstruction Network Incorporating Multi-Level Wavelet Transform and Recurrent Convolution for Sparse View Computed Tomography Imaging

Sparse view computed tomography (SVCT) aims to reduce the number of X-ray projection views required for reconstructing the cross-sectional image of an object. While SVCT significantly reduces X-ray radiation dose and speeds up scanning, insufficient projection data give rise to issues such as severe streak artifacts and blurring in reconstructed images, thereby impacting the diagnostic accuracy of CT detection. To address this challenge, a dual-domain reconstruction network incorporating multi-level wavelet transform and recurrent convolution is proposed in this paper. The dual-domain network is composed of a sinogram domain network (SDN) and an image domain network (IDN). Multi-level wavelet transform is employed in both IDN and SDN to decompose sinograms and CT images into distinct frequency components, which are then processed through separate network branches to recover detailed information within their respective frequency bands. To capture global textures, artifacts, and shallow features in sinograms and CT images, a recurrent convolution unit (RCU) based on convolutional long and short-term memory (Conv-LSTM) is designed, which can model their long-range dependencies through recurrent calculation. Additionally, a self-attention-based multi-level frequency feature normalization fusion (MFNF) block is proposed to assist in recovering high-frequency components by aggregating low-frequency components. Finally, an edge loss function based on the Laplacian of Gaussian (LoG) is designed as the regularization term for enhancing the recovery of high-frequency edge structures. The experimental results demonstrate the effectiveness of our approach in reducing artifacts and enhancing the reconstruction of intricate structural details across various sparse views and noise levels. Our method excels in both performance and robustness, as evidenced by its superior outcomes in numerous qualitative and quantitative assessments, surpassing contemporary state-of-the-art CNNs or Transformer-based reconstruction methods.

A dense and U-shaped transformer with dual-domain multi-loss function for sparse-view CT reconstruction

OBJECTIVE

CT image reconstruction from sparse-view projections is an important imaging configuration for low-dose CT, as it can reduce radiation dose. However, the CT images reconstructed from sparse-view projections by traditional analytic algorithms suffer from severe sparse artifacts. Therefore, it is of great value to develop advanced methods to suppress these artifacts. In this work, we aim to use a deep learning (DL)-based method to suppress sparse artifacts.

METHODS

Inspired by the good performance of DenseNet and Transformer architecture in computer vision tasks, we propose a Dense U-shaped Transformer (D-U-Transformer) to suppress sparse artifacts. This architecture exploits the advantages of densely connected convolutions in capturing local context and Transformer in modelling long-range dependencies, and applies channel attention to fusion features. Moreover, we design a dual-domain multi-loss function with learned weights for the optimization of the model to further improve image quality.

RESULTS

Experimental results of our proposed D-U-Transformer yield performance improvements on the well-known Mayo Clinic LDCT dataset over several representative DL-based models in terms of artifact suppression and image feature preservation. Extensive internal ablation experiments demonstrate the effectiveness of the components in the proposed model for sparse-view computed tomography (SVCT) reconstruction.

SIGNIFICANCE

The proposed method can effectively suppress sparse artifacts and achieve high-precision SVCT reconstruction, thus promoting clinical CT scanning towards low-dose radiation and high-quality imaging. The findings of this work can be applied to denoising and artifact removal tasks in CT and other medical images.

Deep Generalized Learning Model for PET Image Reconstruction

Low-count positron emission tomography (PET) imaging is challenging because of the ill-posedness of this inverse problem. Previous studies have demonstrated that deep learning (DL) holds promise for achieving improved low-count PET image quality. However, almost all data-driven DL methods suffer from fine structure degradation and blurring effects after denoising. Incorporating DL into the traditional iterative optimization model can effectively improve its image quality and recover fine structures, but little research has considered the full relaxation of the model, resulting in the performance of this hybrid model not being sufficiently exploited. In this paper, we propose a learning framework that deeply integrates DL and an alternating direction of multipliers method (ADMM)-based iterative optimization model. The innovative feature of this method is that we break the inherent forms of the fidelity operators and use neural networks to process them. The regularization term is deeply generalized. The proposed method is evaluated on simulated data and real data. Both the qualitative and quantitative results show that our proposed neural network method can outperform partial operator expansion-based neural network methods, neural network denoising methods and traditional methods.

Deep learning enables time-efficient soft tissue enhancement in CBCT: Proof-of-concept study for dentomaxillofacial applications

PURPOSE

The use of iterative and deep learning reconstruction methods, which would allow effective noise reduction, is limited in cone-beam computed tomography (CBCT). As a consequence, the visibility of soft tissues is limited with CBCT. The study aimed to improve this issue through time-efficient deep learning enhancement (DLE) methods.

METHODS

Two DLE networks, UNIT and U-Net, were trained with simulated CBCT data. The performance of the networks was tested with three different test data sets. The quantitative evaluation measured the structural similarity index measure (SSIM) and the peak signal-to-noise ratio (PSNR) of the DLE reconstructions with respect to the ground truth iterative reconstruction method. In the second assessment, a dentomaxillofacial radiologist assessed the resolution of hard tissue structures, visibility of soft tissues, and overall image quality of real patient data using the Likert scale. Finally, the technical image quality was determined using modulation transfer function, noise power spectrum, and noise magnitude analyses.

RESULTS

The study demonstrated that deep learning CBCT denoising is feasible and time efficient. The DLE methods, trained with simulated CBCT data, generalized well, and DLE provided quantitatively (SSIM/PSNR) and visually similar noise-reduction as conventional IR, but with faster processing time. The DLE methods improved soft tissue visibility compared to the conventional Feldkamp-Davis-Kress (FDK) algorithm through noise reduction. However, in hard tissue quantification tasks, the radiologist preferred the FDK over the DLE methods.

CONCLUSION

Post-reconstruction DLE allowed feasible reconstruction times while yielding improvements in soft tissue visibility in each dataset.

Sparsier2Sparse: Self-supervised convolutional neural network-based streak artifacts reduction in sparse-view CT images

BACKGROUND

Sparse-view computed tomography (CT) has attracted a lot of attention for reducing both scanning time and radiation dose. However, sparsely-sampled projection data generate severe streak artifacts in the reconstructed images. In recent decades, many sparse-view CT reconstruction techniques based on fully-supervised learning have been proposed and have shown promising results. However, it is not feasible to acquire pairs of full-view and sparse-view CT images in real clinical practice.

PURPOSE

In this study, we propose a novel self-supervised convolutional neural network (CNN) method to reduce streak artifacts in sparse-view CT images.

METHODS

We generate the training dataset using only sparse-view CT data and train CNN based on self-supervised learning. Since the streak artifacts can be estimated using prior images under the same CT geometry system, we acquire prior images by iteratively applying the trained network to given sparse-view CT images. We then subtract the estimated steak artifacts from given sparse-view CT images to produce the final results.

RESULTS

We validated the imaging performance of the proposed method using extended cardiac-torso (XCAT) and the 2016 AAPM Low-Dose CT Grand Challenge dataset from Mayo Clinic. From the results of visual inspection and modulation transfer function (MTF), the proposed method preserved the anatomical structures effectively and showed higher image resolution compared to the various streak artifacts reduction methods for all projection views.

CONCLUSIONS

We propose a new framework for streak artifacts reduction when only the sparse-view CT data are given. Although we do not use any information of full-view CT data for CNN training, the proposed method achieved the highest performance in preserving fine details. By overcoming the limitation of dataset requirements on fully-supervised-based methods, we expect that our framework can be utilized in the medical imaging field.

Virtual differential phase-contrast and dark-field imaging of x-ray absorption images via deep learning

Weak absorption contrast in biological tissues has hindered x-ray computed tomography from accessing biological structures. Recently, grating-based imaging has emerged as a promising solution to biological low-contrast imaging, providing complementary and previously unavailable structural information of the specimen. Although it has been successfully applied to work with conventional x-ray sources, grating-based imaging is time-consuming and requires a sophisticated experimental setup. In this work, we demonstrate that a deep convolutional neural network trained with a generative adversarial network can directly convert x-ray absorption images into differential phase-contrast and dark-field images that are comparable to those obtained at both a synchrotron beamline and a laboratory facility. By smearing back all of the virtual projections, high-quality tomographic images of biological test specimens deliver the differential phase-contrast- and dark-field-like contrast and quantitative information, broadening the horizon of x-ray image contrast generation.

Machine Learned Texture Prior From Full-Dose CT Database via Multi-Modality Feature Selection for Bayesian Reconstruction of Low-Dose CT

In our earlier study, we proposed a regional Markov random field type tissue-specific texture prior from previous full-dose computed tomography (FdCT) scan for current low-dose CT (LdCT) imaging, which showed clinical benefits through task-based evaluation. Nevertheless, two assumptions were made for early study. One assumption is that the center pixel has a linear relationship with its nearby neighbors and the other is previous FdCT scans of the same subject are available. To eliminate the two assumptions, we proposed a database assisted end-to-end LdCT reconstruction framework which includes a deep learning texture prior model and a multi-modality feature based candidate selection model. A convolutional neural network-based texture prior is proposed to eliminate the linear relationship assumption. And for scenarios in which the concerned subject has no previous FdCT scans, we propose to select one proper prior candidate from the FdCT database using multi-modality features. Features from three modalities are used including the subjects' physiological factors, the CT scan protocol, and a novel feature named Lung Mark which is deliberately proposed to reflect the z-axial property of human anatomy. Moreover, a majority vote strategy is designed to overcome the noise effect from LdCT scans. Experimental results showed the effectiveness of Lung Mark. The selection model has accuracy of 84% testing on 1,470 images from 49 subjects. The learned texture prior from FdCT database provided reconstruction comparable to the subjects having corresponding FdCT. This study demonstrated the feasibility of bringing clinically relevant textures from available FdCT database to perform Bayesian reconstruction of any current LdCT scan.

A cascade-based dual-domain data correction network for sparse view CT image reconstruction

Computed tomography (CT) provides non-invasive anatomical structures of the human body and is also widely used for clinical diagnosis, but excessive ionizing radiation in X-rays can cause harm to the human body. Therefore, the researchers obtained sparse sinograms reconstructed sparse view CT images (SVCT) by reducing the amount of X-ray projection, thereby reducing the radiological effects caused by radiation. This paper proposes a cascade-based dual-domain data correction network (CDDCN), which can effectively combine the complementary information contained in the sinogram domain and the image domain to reconstruct high-quality CT images from sparse view sinograms. Specifically, several encoder-decoder subnets are cascaded in the sinogram domain to reconstruct artifact-free and noise-free CT images. In the encoder-decoder subnets, spatial-channel domain learning is designed to achieve efficient feature fusion through a group merging structure, providing continuous and elaborate pixel-level features and improving feature extraction efficiency. At the same time, to ensure that the original sinogram data collected can be retained, a sinogram data consistency layer is proposed to ensure the fidelity of the sinogram data. To further maintain the consistency between the reconstructed image and the reference image, a multi-level composite loss function is designed for regularization to compensate for excessive smoothing and distortion of the image caused by pixel loss and preserve image details and texture. Quantitative and qualitative analysis shows that CDDCN achieves competitive results in artifact removal, edge preservation, detail restoration, and visual improvement for sparsely sampled data under different views.

CAIR: Combining integrated attention with iterative optimization learning for sparse-view CT reconstruction

Sparse-view CT is an efficient way for low dose scanning but degrades image quality. Inspired by the successful use of non-local attention in natural image denoising and compression artifact removal, we proposed a network combining integrated attention and iterative optimization learning for sparse-view CT reconstruction (CAIR). Specifically, we first unrolled the proximal gradient descent into a deep network and added an enhanced initializer between the gradient term and the approximation term. It can enhance the information flow between different layers, fully preserve the image details, and improve the network convergence speed. Secondly, the integrated attention module was introduced into the reconstruction process as a regularization term. It adaptively fuses the local and non-local features of the image which are used to reconstruct the complex texture and repetitive details of the image, respectively. Note that we innovatively designed a one-shot iteration strategy to simplify the network structure and reduce the reconstruction time while maintaining image quality. Experiments showed that the proposed method is very robust and outperforms state-of-the-art methods in terms of both quantitative and qualitative, greatly improving the preservation of structures and the removal of artifacts.