Improved Image Reconstruction Research Articles

Image quality evaluation of a new high-performance ring-gantry cone-beam computed tomography imager

Objective. Newer cone-beam computed tomography (CBCT) imaging systems offer reconstruction algorithms including metal artifact reduction (MAR) and extended field-of-view (eFoV) techniques to improve image quality. In this study a new CBCT imager, the new Varian HyperSight CBCT, is compared to fan-beam CT and two CBCT imagers installed in a ring-gantry and C-arm linear accelerator, respectively. Approach. The image quality was assessed for HyperSight CBCT which uses new hardware, including a large-size flat panel detector, and improved image reconstruction algorithms. The decrease of metal artifacts was quantified (structural similarity index measure (SSIM) and root-mean-squared error (RMSE)) when applying MAR reconstruction and iterative reconstruction for a dental and spine region using a head-and-neck phantom. The geometry and CT number accuracy of the eFoV reconstruction was evaluated outside the standard field-of-view (sFoV) on a large 3D-printed chest phantom. Phantom size dependency of CT numbers was evaluated on three cylindrical phantoms of increasing diameter. Signal-to-noise and contrast-to-noise were quantified on an abdominal phantom. Main results. In phantoms with streak artifacts, MAR showed comparable results for HyperSight CBCT and CT, with MAR increasing the SSIM (0.97–0.99) and decreasing the RMSE (62–55 HU) compared to iterative reconstruction without MAR. In addition, HyperSight CBCT showed better geometrical accuracy in the eFoV than CT (Jaccard Conformity Index increase of 0.02–0.03). However, the CT number accuracy outside the sFoV was lower than for CT. The maximum CT number variation between different phantom sizes was lower for the HyperSight CBCT imager (∼100 HU) compared to the two other CBCT imagers (∼200 HU), but not fully comparable to CT (∼50 HU). Significance. This study demonstrated the imaging performance of the new HyperSight CBCT imager and the potential of applying this CBCT system in more advanced scenarios by comparing the quality against fan-beam CT.

Electrode Placement Optimization for Electrical Impedance Tomography Using Active Learning

Electrical impedance tomography (EIT) offers a versatile imaging modality with a multitude of applications, although it encounters accuracy limitations. Herein, a novel systematic framework is presented that integrates a neural network (NN), active learning, and transfer learning to optimize electrode placement, improving image reconstruction performance based on user‐defined metrics. Given the many‐to‐one mapping between electrode configuration and the performance metric, the approach utilizes a NN that predicts performance metrics from electrode placement input. To maintain NN's prediction accuracy for unseen electrode configurations, performance metrics are maximized while iteratively updating the NN via active learning during the optimization process. Transfer learning is employed to expedite optimization of electrode placements for time‐consuming iterative reconstruction techniques by fine‐tuning a NN initially trained on one‐step reconstruction data. The method is validated using two representative reconstruction methods: one‐step reconstruction with Newton's one‐step error reconstructor prior and the iterative total variation method. This research underscores the potential of the proposed framework in addressing EIT's inherent limitations and augmenting its performance across diverse applications and reconstruction methods. The framework could potentially contribute to the advancement of noninvasive medical imaging, structural health monitoring, strain sensing, robotics, and other fields that depend on EIT.

High-Fidelity Diffusion-Based Image Editing

Diffusion models have attained remarkable success in the domains of image generation and editing. It is widely recognized that employing larger inversion and denoising steps in diffusion model leads to improved image reconstruction quality. However, the editing performance of diffusion models tends to be no more satisfactory even with increasing denoising steps. The deficiency in editing could be attributed to the conditional Markovian property of the editing process, where errors accumulate throughout denoising steps. To tackle this challenge, we first propose an innovative framework where a rectifier module is incorporated to modulate diffusion model weights with residual features from the original images, thereby providing compensatory information to bridge the fidelity gap. Furthermore, we introduce a novel learning paradigm aimed at minimizing error propagation during the editing process, which trains the editing procedure in a manner similar to denoising score-matching. Extensive experiments demonstrate that our proposed framework and training strategy achieve high-fidelity reconstruction and editing results across various levels of denoising steps, meanwhile exhibits exceptional performance in terms of both quantitative metric and qualitative assessments. Lastly, we explore our model's generalization though several applications like image-to-image translation and out-of-domain image editing.

Anomaly‐based Alzheimer's disease detection using entropy‐based probability Positron Emission Tomography images

AbstractDeep neural networks trained on labeled medical data face major challenges owing to the economic costs of data acquisition through expensive medical imaging devices, expert labor for data annotation, and large datasets to achieve optimal model performance. The heterogeneity of diseases, such as Alzheimer's disease, further complicates deep learning because the test cases may substantially differ from the training data, possibly increasing the rate of false positives. We propose a reconstruction‐based self‐supervised anomaly detection model to overcome these challenges. It has a dual‐subnetwork encoder that enhances feature encoding augmented by skip connections to the decoder for improving the gradient flow. The novel encoder captures local and global features to improve image reconstruction. In addition, we introduce an entropy‐based image conversion method. Extensive evaluations show that the proposed model outperforms benchmark models in anomaly detection and classification using an encoder. The supervised and unsupervised models show improved performances when trained with data preprocessed using the proposed image conversion method.

On constrained Kaczmarz algorithm with momentum for image reconstruction

SummaryThe Kaczmarz algorithm is widely used in image reconstruction. In this paper, we propose an enhanced version of the Kaczmarz algorithm by integrating Nesterov momentum into the Kaczmarz algorithm, denoted as the Kaczmarz algorithm with momentum. Our innovation offers a novel approach to accelerate the convergence rate of the Kaczmarz algorithm significantly. Based on greedy rules for selecting work row, we prove that the Kaczmarz method with momentum can achieve global linear convergence rates. Furthermore, we extend a constrained version of the Kaczmarz algorithm with Nesterov momentum for improved image reconstruction and prove its convergence. Numerical experiments show that the proposed algorithm outperforms the original Kaczmarz algorithm in terms of efficiency for image reconstruction.

A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging.

Magnetic resonance imaging (MRI) is an essential diagnostic tool that suffers from prolonged scan time. To alleviate this limitation, advanced fast MRI technology attracts extensive research interests. Recent deep learning has shown its great potential in improving image quality and reconstruction speed. Faithful coil sensitivity estimation is vital for MRI reconstruction. However, most deep learning methods still rely on pre-estimated sensitivity maps and ignore their inaccuracy, resulting in the significant quality degradation of reconstructed images. In this work, we propose a Joint Deep Sensitivity estimation and Image reconstruction network, called JDSI. During the image artifacts removal, it gradually provides more faithful sensitivity maps with high-frequency information, leading to improved image reconstructions. To understand the behavior of the network, the mutual promotion of sensitivity estimation and image reconstruction is revealed through the visualization of network intermediate results. Results on in vivo datasets and radiologist reader study demonstrate that, for both calibration-based and calibrationless reconstruction, the proposed JDSI achieves the state-of-the-art performance visually and quantitatively, especially when the acceleration factor is high. Additionally, JDSI owns nice robustness to patients and autocalibration signals.

Image Reconstruction by VAE

Variational Autoencoders (VAEs) have become a recognized tool for learning data distributions in various applications. Building on previous research, this paper investigates image reconstruction by incorporating Planar Flows into VAEs. As a form of Normalizing Flows, Planar Flows potentially aid VAEs in modeling more intricate distributions, which may enhance the quality of reconstructed images. Our study employs a well-established architecture that integrates Planar Flows with VAEs. Experiments on MNIST are provided to compare our replication efforts with traditional VAEs. The results suggest that utilizing Planar Flows can offer certain improvements in image reconstruction. This work further solidifies the understanding of the combined use of VAEs and Planar Flows in image reconstruction tasks.

Anthropomorphic breast phantoms for radiology imaging: a review

Phantoms are used to validate diagnostic imaging methods or improve the skills of medical professionals. For instance, they allow conducting an unlimited number of imaging studies during medical training, assessing image quality, optimizing radiation dose, and testing novel techniques and equipment. Researchers in breast imaging use anthropomorphic models to validate, assess, and optimize new methods for diagnosing breast diseases. Such models also facilitate control over the quality of diagnostic systems, help optimize clinical protocols, and improve image reconstruction algorithms. Realistic simulation of the breast tissue is essential to address the challenges of advancing X-ray mammary gland studies. The review aimed to describe phantoms currently available for diagnostic imaging and the way they were fabricated. In this literature review, PubMed, eLIBRARY, and Google Scholar databases were screened for relevant articles. Thus, 72 articles and 13 conference papers were included. The study two major types of breast phantoms: computational and physical. Specifically, computational phantoms are classified into subgroups depending on the data they use. These include mathematical models, tissue samples, and medical images of the breast. The classification of the physical phantoms is based on their manufacturing process: casting silicone-like substances, 3D printing with resins and plastics, or printing on paper using X-ray contrast ink. Computational phantoms are generally advantageous with respect to versatility, efficiency, precision, and safety and allow the generation of large amounts of virtual data. Physical phantoms provide the most realistic diagnostic images without the need for a patient and allow performing an unlimited number of radiological studies.

Subspace Model-Assisted Deep Learning for Improved Image Reconstruction.

Image reconstruction from limited and/or sparse data is known to be an ill-posed problem and a priori information/constraints have played an important role in solving the problem. Early constrained image reconstruction methods utilize image priors based on general image properties such as sparsity, low-rank structures, spatial support bound, etc. Recent deep learning-based reconstruction methods promise to produce even higher quality reconstructions by utilizing more specific image priors learned from training data. However, learning high-dimensional image priors requires huge amounts of training data that are currently not available in medical imaging applications. As a result, deep learning-based reconstructions often suffer from two known practical issues: a) sensitivity to data perturbations (e.g., changes in data sampling scheme), and b) limited generalization capability (e.g., biased reconstruction of lesions). This paper proposes a new method to address these issues. The proposed method synergistically integrates model-based and data-driven learning in three key components. The first component uses the linear vector space framework to capture global dependence of image features; the second exploits a deep network to learn the mapping from a linear vector space to a nonlinear manifold; the third is an unrolling-based deep network that captures local residual features with the aid of a sparsity model. The proposed method has been evaluated with magnetic resonance imaging data, demonstrating improved reconstruction in the presence of data perturbation and/or novel image features. The method may enhance the practical utility of deep learning-based image reconstruction.

CFA-GAN: Cross fusion attention and frequency loss for image style transfer

Image style transfer is a significant challenge in computer vision, where the goal is to transfer the style of a reference image to a source image while preserving its background and feature information. Despite previous progress in this area, many studies have overlooked the issues of local details and complexity, leading to unrealistic generated images and slow model inference speeds. This paper proposes an efficient image style transfer method based on cross-fusion attention (CFA), inspired by generative adversarial networks (GAN) and a new joint loss. Our novel CFA encodes local contextual information between low-level and high-level features, models remote dependencies of features, and embeds CFA into the encoder of our proposed model to increase the diversity of small features and focus on more prominent feature parts. Furthermore, we use frequency domain loss to improve image reconstruction quality and a joint loss to train the network. Our experiments on multiple datasets demonstrate that our proposed method offers significant advantages in terms of inference speed, background information preservation, and local details, making it a promising approach to image style transfer. The code for our method is available at https://github.com/berylxzhang/CFA-GAN.

Dual states based reinforcement learning for fast MR scan and image reconstruction

Incomplete phase encoding with few phases is an effective under-sampling manner of fast Magnetic Resonance (MR) scan. The key is how to choose important slice-specific phases. Reinforcement Learning (RL) is powerful for sequential decision-making and therefore is feasible for slice-specific phase selection. Existing RL-based methods employ time-consuming reconstruction-oriented Deep Neural Networks (DNN) to generate/transit states from which phase selection and reward computation are performed. The advantage is that the selected phases and the corresponding partial k-space data match the DNN for image reconstruction. The disadvantage lies in the impossibility of deciding/selecting a phase in as short as several milliseconds required in the timings of a typical pulse sequence. To simultaneously keep the matching advantage and avoid the inefficiency disadvantage, we propose a dual-state-based RL framework. A visible Parameter-Free (PF) state obtained by inverse Fast Fourier transform and a hidden DNN state obtained by applying a time-consuming reconstruction-oriented DNN on the visible state are called dual states. Visible states are used as input of phase decision networks and hidden states are used for computing reward to evaluate the decision networks. Because the time-consuming hidden states are merely involved in training process and only efficient visible states are computed in inference process, the proposed method is very efficient. Moreover, we demonstrate that incorporating the phase-indicator vector (containing sequentially selected phases) as an additional input to the transformer used for reconstructing from undersampled MR images can significantly improve image reconstruction accuracy. Experiments on fastMRI dataset demonstrate effectiveness and efficiency of the proposed method.

Umjetna inteligencija u oslikavanju koljena magnetnom rezonancijom

Introduction: Technological progress leads to an increasing use of radiological imaging, and an increase in the number of imaging results in an increased workload for radiologists. The driver of the application of AI in radiology is considered to be the reduction of the workload of radiologists and the need for faster and more accurate diagnosis. Aim: The aim of this paper is to bring the reader closer to the implementation of AI in radiology, especially in the MRI modality, and how deep learning algorithms improve image reconstruction. Discussion: Numerous studies have confirmed the importance of implementing machine learning, a subset of artificial intelligence, in the radiology system. In this review paper, numerous researches on the application of deep learning in magnetic resonance imaging are highlighted, and the emphasis is on models for automatic segmentation. Automatic segmentation has shown excellent results in the early detection of osteoarthritis, then in anterior cruciate ligament and meniscus tears, the most common knee injuries, and more recently, the deep learning model has excelled in automatic bone age estimation. Automatic segmentation has achieved, above all, high accuracy and precision, objectivity and time saving. Conclusion: Previous research has already highlighted the significant advantage of using machine learning in radiology and the exceptional compatibility between the work of radiologists and machine learning, which achieves precise and quick diagnoses. All this is a great incentive for further research, and technological progress will certainly speed up its integration into clinical practice.

Comparison of Different Radial Basis Function Networks for the Electrical Impedance Tomography (EIT) Inverse Problem

This paper aims to determine whether regularization improves image reconstruction in electrical impedance tomography (EIT) using a radial basis network. The primary purpose is to investigate the effect of regularization to estimate the network parameters of the radial basis function network to solve the inverse problem in EIT. Our approach to studying the efficacy of the radial basis network with regularization is to compare the performance among several different regularizations, mainly Tikhonov, Lasso, and Elastic Net regularization. We vary the network parameters, including the fixed and variable widths for the Gaussian used for the network. We also perform a robustness study for comparison of the different regularizations used. Our results include (1) determining the optimal number of radial basis functions in the network to avoid overfitting; (2) comparison of fixed versus variable Gaussian width with or without regularization; (3) comparison of image reconstruction with or without regularization, in particular, no regularization, Tikhonov, Lasso, and Elastic Net; (4) comparison of both mean square and mean absolute error and the corresponding variance; and (5) comparison of robustness, in particular, the performance of the different methods concerning noise level. We conclude that by looking at the R2 score, one can determine the optimal number of radial basis functions. The fixed-width radial basis function network with regularization results in improved performance. The fixed-width Gaussian with Tikhonov regularization performs very well. The regularization helps reconstruct the images outside of the training data set. The regularization may cause the quality of the reconstruction to deteriorate; however, the stability is much improved. In terms of robustness, the RBF with Lasso and Elastic Net seem very robust compared to Tikhonov.

A correction algorithm for improved magnetic field monitoring with distal field probes.

A significant source of artifacts in MRI are field fluctuations. Field monitoring is a new technology that allows measurement of field dynamics during a scan via "field probes," which can be used to improve image reconstruction. Ideally, probes are located within the volume where gradients produce nominally linear field patterns. However, in some situations probes must be located far from isocenter where rapid field variation can arise, leading to erroneous field-monitoring characterizations and images. This work aimed to develop an algorithm that improves the robustness of field dynamics in these situations. The algorithm is split into three components. Component 1 calculates field dynamics one spatial order at a time, whereas the second implements a weighted least squares solution based on probe distance. Component 3 then calculates phase residuals and removes the residual phase for distant probes before recalculation. Two volunteers and a phantom were scanned on a 7T MRI using diffusion-weighted sequences, and field monitoring was performed. Image reconstructions were informed with field dynamics calculated conventionally, and with the correction algorithm, after which in vivo images were compared qualitatively and phantom image error was quantitatively assessed. The algorithm was able to correct corrupted field dynamics, resulting in image-quality improvements. Significant artifact reduction was observed when correcting higher-order fits. Stepwise fitting provided the most correction benefit, which was marginally improved when adding the other correction strategies. The proposed algorithm can mitigate effects of phase errors in field monitoring, providing improved characterization of field dynamics.

Progressive Feedback Residual Attention Network for Cardiac Magnetic Resonance Imaging Super-Resolution.

Atrial fibrillation (AF) is an increasing medical burden worldwide, and its pathological manifestations are atrial tissue remodeling and low-pressure atrial tissue fibrosis. Due to the inherent defects of medical image data acquisition systems, the acquisition of high-resolution cardiac magnetic resonance imaging (CMRI) faces many problems. In response to these problems, we propose the Progressive Feedback Residual Attention Network (PFRN) for CMRI super-resolution. Specifically, we directly perform feature extraction on low-resolution images, retain feature information to a large extent, and then build multiple independent progressive feedback modules to extract high-frequency details. To accelerate network convergence and improve image reconstruction quality, we implement the MS-SSIM-L1 loss function. Furthermore, we utilize the residual attention stack module to explore the image's internal relevance and extract the low-resolution image's detailed features. Extensive benchmark evaluation shows that PFRN can improve the detailed information of the image SR reconstruction results, and the reconstructed CMRI has a better visual effect.

Advanced Image Post-Processing Methods for Photoacoustic Tomography: A Review

Photoacoustic tomography (PAT) is a promising imaging technique that utilizes the detection of light-induced acoustic waves for both morphological and functional biomedical imaging. However, producing high-quality images using PAT is still challenging and requires further research. Besides improving image reconstruction, which turns the raw photoacoustic signal into a PAT image, an alternative way to address this issue is through image post-processing, which can enhance and optimize the reconstructed PAT image. Image post-processing methods have rapidly emerged in PAT and are proven to be essential in improving image quality in recent research. In this review, we investigate the need for image post-processing in PAT imaging. We conduct a thorough literature review on the latest PAT image post-processing articles, including both general and PAT-specific post-processing techniques. In contrast to previous reviews, our analysis focuses specifically on advanced image post-processing rather than image reconstruction methods. By highlighting their potential applications, we hope to encourage further research and development in PAT image post-processing technology.

Image reconstruction for ECT based on high-order approximate sensitivity matrix

Electrical capacitance tomography (ECT), widely used for industrial measurements, reflects the permittivity distribution of the imaging region by measuring capacitance. The image reconstruction method is a crucial factor in determining image quality. Normally, the ECT image is reconstructed by sensitivity matrix. To alleviate the effects of the soft field property of the traditional sensitivity matrix, we propose an ECT reconstruction method based on the high-order approximate sensitivity matrix. The high-order approximate sensitivity matrices with four different nonlinearities, including a traditional sensitivity matrix, are derived through electromagnetic field analysis. Both numerical simulation and experiment are implemented for four sensitivity matrices using the Tikhonov regularisation algorithm and Landweber algorithm. Sensitivity evaluation indicators, image evaluation metricises, and capacitance matching correlation coefficients are used to compare the performance of sensitivity matrices. The results show that the high-order approximate sensitivity matrix with suitable nonlinearities improves image reconstruction quality. In different flow regimes, the sensitivity matrix with proper linearity has obvious advantages for regimes with high nonlinearity. The high-order approximate sensitivity matrix leads to shorter calculation time and can be used by various algorithms to enhance image reconstruction quality.

ADAS: A High Computational Utilization D ynamic Reconfigurable Hardware A ccelerator for S uper Resolution

Super-resolution (SR) based on deep learning has obtained superior performance in image reconstruction. Recently, various algorithm efforts have been committed to improving image reconstruction quality and speed. However, the inference of SR contains huge amounts of computation and data access, leading to low hardware implementation efficiency. For instance, the up-sampling with the deconvolution process requires considerable computation resources. In addition, the sizes of output feature maps of several middle layers are extraordinarily large, which is challenging to optimize, causing serious data access issues. In this work, we present an all-on-chip hardware architecture based on the deconvolution scheme and feature map segmentation strategy, namely ADAS, where all the generated data by the middle layers are buffered on-chip to avoid large data movements between on- and off-chip. In ADAS, we develop a hardware-friendly and efficient deconvolution scheme to accelerate the computation. Also, the dynamic reconfigurable process element (PE) combined with efficient mapping is proposed to enhance PE utilization up to nearly 100% and support multiple scaling factors. Based on our experimental results, ADAS demonstrates real-time image SR and better image reconstruction quality with PSNR (37.15 dB ) and SSIM (0.9587). Compared to baseline and validated with the FPGA platform, ADAS can support scaling factors of 2, 3, and 4, achieving 2.68 ×, 5.02 ×, and 8.28 × speedup.

Dual-branch fusion model for lensless imaging.

A lensless camera is an imaging system that replaces the lens with a mask to reduce thickness, weight, and cost compared to a lensed camera. The improvement of image reconstruction is an important topic in lensless imaging. Model-based approach and pure data-driven deep neural network (DNN) are regarded as two mainstream reconstruction schemes. In this paper, the advantages and disadvantages of these two methods are investigated to propose a parallel dual-branch fusion model. The model-based method and the data-driven method serve as two independent input branches, and the fusion model is used to extract features from the two branches and merge them for better reconstruction. Two types of fusion model named Merger-Fusion-Model and Separate-Fusion-Model are designed for different scenarios, where Separate-Fusion-Model is able to adaptively allocate the weights of the two branches by the attention module. Additionally, we introduce a novel network architecture named UNet-FC into the data-driven branch, which enhances reconstruction by making full use of the multiplexing property of lensless optics. The superiority of the dual-branch fusion model is verified by drawing comparison with other state-of-the-art methods on public dataset (+2.95dB peak signal-to-noise (PSNR), +0.036 structural similarity index (SSIM), -0.0172 Learned Perceptual Image Patch Similarity (LPIPS)). Finally, a lensless camera prototype is constructed to further validate the effectiveness of our method in a real lensless imaging system.

Structured Illumination Microscopy of Mitochondrial in Mouse Hepatocytes with an Improved Image Reconstruction Algorithm.

In this paper, a structured illumination microscopy (SIM) image reconstruction algorithm combined with notch function (N-SIM) is proposed. This method suppresses the defocus signal in the imaging process by processing the low-frequency signal of the image. The existing super-resolution image reconstruction algorithm produces streak artifacts caused by defocus signal. The experimental results show that the algorithm proposed in our study can well suppress the streak artifacts caused by defocused signals during the imaging process without losing the effective information of the image. The image reconstruction algorithm is used to analyze the mouse hepatocytes, and the image processing tool developed by MATLAB is applied to identify, detect and count the reconstructed images of mitochondria and lipid droplets, respectively. It is found that the mitochondrial activity in oxidative stress induced growth inhibitor 1 (OSGIN1) overexpressed mouse hepatocytes is higher than that in normal cells, and the interaction with lipid droplets is more obvious. This paper provides a reliable subcellular observation platform, which is very meaningful for biomedical work.