Image Reconstruction Applications Research Articles

An inexact variable metric variant of incremental aggregated Forward-Backward method for the large-scale composite optimization problem

ABSTRACT This paper focuses on the large-scale composite optimization problem, which is the sum of a finite number of smooth (possibly nonconvex) functions and a convex (possibly nonsmooth) function. A common method to solve this problem is proximal incremental aggregated gradient (PIAG) method (N. D. Vanli, M. Gurbuzbalaban and A. Ozdaglar, SIAM Journal on Optimization, 2018, 28(2): 1282–1300), which exploits the gradient information from previous iterations to approximate the gradient at the current point. However, as many first-order algorithms, it may suffer from slow convergence. To address this issue, this paper proposes an inexact variable metric variant of incremental aggregated Forward-Backward (iVMFB-IAG) method. This methods incorporates a variable metric strategy to accelerate the PIAG method and introduces an inexact criterion to enhance its practicality. Under the Kurdyka-Łojasiewicz (KL) property and the uniformly bounded positive definiteness of the scaling matrix, the sequence generated by the iVMFB-IAG method converges globally to the critical point of the problem. Additionally, the convergence rate is established under the KL framework. Moreover, under certain restrictive conditions, the method exhibits local superlinear convergence. Through the numerical experiments on image reconstruction applications, we demonstrate the effectiveness of the iVMFB-IAG method by restoring two distinct images.

Detection of respiration-induced field modulations in fMRI: A concurrent and navigator-free approach

Abstract Functional Magnetic Resonance Imaging (fMRI) is typically acquired using gradient-echo sequences with a long echo time at high temporal resolution. Gradient-echo sequences inherently encode information about the magnetic field in the often discarded image phase. We demonstrate a method for processing the phase of reconstructed fMRI data to isolate temporal fluctuations in the harmonic fields associated with respiration by solving a blind source separation problem. The fMRI-derived field fluctuations are shown to be in strong agreement with breathing belt data acquired during the same scan. This work presents a concurrent, hardware-free measurement of respiration-induced field fluctuations, providing a respiratory regressor for fMRI analysis which is independent of local contrast changes, and with potential applications in image reconstruction and fMRI analysis.

Photoacoustic imaging plus X: a review.

Photoacoustic (PA) imaging (PAI) represents an emerging modality within the realm of biomedical imaging technology. It seamlessly blends the wealth of optical contrast with the remarkable depth of penetration offered by ultrasound. These distinctive features of PAI hold tremendous potential for various applications, including early cancer detection, functional imaging, hybrid imaging, monitoring ablation therapy, and providing guidance during surgical procedures. The synergy between PAI and other cutting-edge technologies not only enhances its capabilities but also propels it toward broader clinical applicability. The integration of PAI with advanced technology for PA signal detection, signal processing, image reconstruction, hybrid imaging, and clinical applications has significantly bolstered the capabilities of PAI. This review endeavor contributes to a deeper comprehension of how the synergy between PAI and other advanced technologies can lead to improved applications. An examination of the evolving research frontiers in PAI, integrated with other advanced technologies, reveals six key categories named "PAI plus X." These categories encompass a range of topics, including but not limited to PAI plus treatment, PAI plus circuits design, PAI plus accurate positioning system, PAI plus fast scanning systems, PAI plus ultrasound sensors, PAI plus advanced laser sources, PAI plus deep learning, and PAI plus other imaging modalities. After conducting a comprehensive review of the existing literature and research on PAI integrated with other technologies, various proposals have emerged to advance the development of PAI plus X. These proposals aim to enhance system hardware, improve imaging quality, and address clinical challenges effectively. The progression of innovative and sophisticated approaches within each category of PAI plus X is positioned to drive significant advancements in both the development of PAI technology and its clinical applications. Furthermore, PAI not only has the potential to integrate with the above-mentioned technologies but also to broaden its applications even further.

Retracted: Application of High-Resolution Face Recognition and EDF Image Reconstruction in English Classroom Teaching

Retracted: Application of High-Resolution Face Recognition and EDF Image Reconstruction in English Classroom Teaching

High-resolution face recognition and EDF image reconstruction application in English classroom teaching

High-resolution face recognition and EDF image reconstruction application in English classroom teaching

Adaptive super-resolution image reconstruction based on fractal theory

Image super-resolution (SR) reconstruction has been a significant research field in image processing, although the current approaches to dealing with complicated image reconstruction still have some issues. In order to overcome these problems, traditional approaches apply fractal geometry to image super-resolution reconstruction, either in textured or non-textured regions, treating the image as a single fractal set. However, it won't be possible to classify the image specifically, such as edge, common texture, and complex texture, unless identifying the texture complexity of all regions in the image. For the texture region, this paper considers the fractal as a perturbation function of rational interpolation and constructs a new interpolation model with scale factor parameter. The parameter is related to the local fractal dimension (LFD). This paper defines a new function between scale factor and local fractal dimension so as to select the optimal value adaptively. Considering the image edge quality, this paper combines the non-texture regions and edge regions, applies an improved edge interpolation algorithm and sets a new pixel mapping method to meet the needs of different zoom factors. Based on the above, this paper proposes an adaptive super-resolution reconstruction algorithm. Particularly, region segmentation with different texture complexity is a foundational step of our algorithm, this paper proposes an image segmentation algorithm based on the research of local fractal dimension which combines the multi-scale analysis capability of wavelets with the multi-scale self-similarity feature of fractals. The experimental results show that our algorithm can obtain accurate texture details, smooth edges and low noise subjectively, and achieve the best evaluation objectively. This paper lays the theoretical foundation for fractal applications in super-resolution image reconstruction.

Transformers and their application to medical image processing: A review

Transformers perform well in natural language processing tasks and have made many breakthroughs in computer vision. In medical image processing, transformers are successfully used in image segmentation, classification, reconstruction, and diagnosis. In this paper, we mainly expound on the transformer principle and its application in medical imaging. Specifically, we first introduce the basic principles and model structure of transformers. Then, we summarize the improvement mechanism of the transformer's network including combining the transformer with the Unet network, creating a transformer lightweight variant network, strengthening the cross-fast link mechanism, and building a large model with the transformer as the skeleton. Second, extensive discussion is given to medical image segmentation, reconstruction, classification, and other applications. Finally, the main challenges transformers face in the medical image processing field and future development prospects. Furthermore, we systematically summarize the latest research progress of transformers and their application in medical image processing, which has significant reference value for transformer research in the medical field.

Investigation of Optimal Image Inpainting Techniques for Image Reconstruction and Image Restoration Applications

People in today's society take a lot of pictures with their smartphones and also make an effort to keep their old photographs safe, but with time, those photographs deteriorate. Image inpainting is the art of reconstructing damaged or missing parts of an image. Repairing scratches in photographs or film negatives, or adding or removing elements like stamped dates or "red-eye," are all possible through inpainting. In order to restore the image many techniques have been developed, significant techniques include exemplar based inpainting, coherent based inpainting and method for correction of non-uniform illumination. The four main applications of these image inpainting techniques are scratch removal, text removal, object removal and image restoration. However, all the four image inpainting applications cannot be implemented using a single technique. According to the literature, there has been relatively less work done in the field of image inpainting applications. Investigation has been carried out to find the suitability of these three techniques for the four above mentioned image inpainting applications based on two performance metrics.

A Review of Image Reconstruction Algorithms for Diffuse Optical Tomography

Diffuse optical tomography (DOT) is a biomedical imaging modality that can reconstruct hemoglobin concentration and associated oxygen saturation by using detected light passing through a biological medium. Various clinical applications of DOT such as the diagnosis of breast cancer and functional brain imaging are expected. However, it has been difficult to obtain high spatial resolution and quantification accuracy with DOT because of diffusive light propagation in biological tissues with strong scattering and absorption. In recent years, various image reconstruction algorithms have been proposed to overcome these technical problems. Moreover, with progress in related technologies, such as artificial intelligence and supercomputers, the circumstances surrounding DOT image reconstruction have changed. To support the applications of DOT image reconstruction in clinics and new entries of related technologies in DOT, we review the recent efforts in image reconstruction of DOT from the viewpoint of (i) the forward calculation process, including the radiative transfer equation and its approximations to simulate light propagation with high precision, and (ii) the optimization process, including the use of sparsity regularization and prior information to improve the spatial resolution and quantification.

TextureWGAN: texture preserving WGAN with multitask regularizer for computed tomography inverse problems.

This paper presents a deep learning (DL) based method called TextureWGAN. It is designed to preserve image texture while maintaining high pixel fidelity for computed tomography (CT) inverse problems. Over-smoothed images by postprocessing algorithms have been a well-known problem in the medical imaging industry. Therefore, our method tries to solve the over-smoothing problem without compromising pixel fidelity. The TextureWGAN extends from Wasserstein GAN (WGAN). The WGAN can create an image that looks like a genuine image. This aspect of the WGAN helps preserve image texture. However, an output image from the WGAN is not correlated to the corresponding ground truth image. To solve this problem, we introduce the multitask regularizer (MTR) to the WGAN framework to make a generated image highly correlated to the corresponding ground truth image so that the TextureWGAN can achieve high-level pixel fidelity. The MTR is capable of using multiple objective functions. In this research, we adopt a mean squared error (MSE) loss to maintain pixel fidelity. We also use a perception loss to improve the look and feel of result images. Furthermore, the regularization parameters in the MTR are trained along with generator network weights to maximize the performance of the TextureWGAN generator. The proposed method was evaluated in CT image reconstruction applications in addition to super-resolution and image-denoising applications. We conducted extensive qualitative and quantitative evaluations. We used PSNR and SSIM for pixel fidelity analysis and the first-order and the second-order statistical texture analysis for image texture. The results show that the TextureWGAN is more effective in preserving image texture compared with other well-known methods such as the conventional CNN and nonlocal mean filter (NLM). In addition, we demonstrate that TextureWGAN can achieve competitive pixel fidelity performance compared with CNN and NLM. The CNN with MSE loss can attain high-level pixel fidelity, but it often damages image texture. TextureWGAN can preserve image texture while maintaining pixel fidelity. The MTR is not only helpful to stabilize the TextureWGAN's generator training but also maximizes the generator performance.

Visualizing the mind's eye: a future perspective on applications of image reconstruction from brain signals to psychiatry.

Abstract In an era where neuroscience dances with computational advances, the power to “visualize” one's thoughts at image-level is no longer confined to the realm of science fiction. This groundbreaking image reconstruction from brain signals (IRBS) techniques, riding the wave of deep learning and large-scale neuroimaging datasets, offer an unprecedented perspective for not only neurocognitive research but also psychiatry. In this short commentary, I provide a concise introduction to the development of IRBS technology and offer a perspective on its potential applications, current limitations, and future directions in psychiatry.

Inversion of the Gravity Gradiometry Data by ResUnet Network: An Application in Nordkapp Basin, Barents Sea

The study and assessment of the subsurface density distribution are vital for mining and oil & gas exploration. This can be achieved by the three-dimensional (3D) inversion of the observed gravity and gravity gradiometry (GG) data. Due to the ill-posedness of the geophysical inverse problem, the nonuniqueness and instability of solutions represent the main difficulties in inversion. In recent years, convolutional neural networks, especially U-net technology, have found wide applications in image processing, recognition, and reconstruction. This paper proposes using this method for fast reconstruction of the subsurface density models based on the ResUnet technology. The developed new method was examined on two 3D synthetic gravity and gravity gradiometry datasets inversion. The results show that the ResUnet network can reconstruct the density anomaly with sharp boundaries and is robust to the noise, making the solution stable.

Applications of ultrasound image reconstruction using deep learning

Deep learning has gained tremendous popularity as a tool for ultrasound beamforming and image reconstruction. In previous work, we trained deep neural networks (DNNs) to estimate the echogenicity of a medium, to improve acoustical and electronic signal-to-noise ratio (SNR) in channel data, and to detect targeted microbubbles nondestructively for real-time ultrasound molecular imaging. Here, we present several advancements to each application. First, we compare the speckle- and noise-reducing performance of DNNs trained with simple linear Field II simulations of photographic images versus that of DNNs trained with full wave finite-difference time domain numerical simulations containing realistic abdominal walls and the resulting image degradation artifacts. We further extend our nondestructive molecular imaging DNN to incorporate spatiotemporal information using an extended simulation study to increase specificity for stationary bound microbubbles and further improve nondestructive molecular imaging.

Image reconstruction by shapefree radial basis function neural networks (RBFNs)

With the growth of artificial intelligence technologies, the research on artificial neural networks (ANNs) has been paid much more attention. Radial basis function neural networks (RBFNs) are a type of ANNs that are referred to as models that replicate the role of biological neural networks. While their applications are growing in a wide range of areas, conventional forms of RBFs contain a highly problem-dependent shape parameter, making it not as convenient as one would expect. This work investigates the numerical effectiveness of RBFs containing no shapes, so they are referred to as ‘shapefree’, under the application of image reconstruction. Nine forms of shapefree RBFs have been gathered and implemented in conjunction with the RBFNs. Two popular images (known as Lena and Plane) are damaged in Salt-and-Pepper manner before being repaired by the networks using these shapefree RBFs. The overall performances are monitored based on error norm, CPU-time and storage, and condition number. This aims to provide useful information regarding choices of RBFs for future uses, to overcome the pain one faces from choosing a suitable value of shape parameter.

Extra Proximal-Gradient Network with Learned Regularization for Image Compressive Sensing Reconstruction.

Learned optimization algorithms are promising approaches to inverse problems by leveraging advanced numerical optimization schemes and deep neural network techniques in machine learning. In this paper, we propose a novel deep neural network architecture imitating an extra proximal gradient algorithm to solve a general class of inverse problems with a focus on applications in image reconstruction. The proposed network features learned regularization that incorporates adaptive sparsification mappings, robust shrinkage selections, and nonlocal operators to improve solution quality. Numerical results demonstrate the improved efficiency and accuracy of the proposed network over several state-of-the-art methods on a variety of test problems.

Application of High-Resolution Face Recognition and EDF Image Reconstruction in English Classroom Teaching

This study examines classroom monitoring in order to increase the effectiveness of English classroom instruction. This article uses high-resolution algorithms and EDF image reconstruction technology in the English classroom education system to improve the high resolution of students’ faces. This work presents a fast pixel capture function to enhance the rapid video capture function. The stack velocity picking of the speed spectrum is the most important aspect of velocity analysis. To create successful changes to the algorithm, this method often employs optimization algorithms as a way of stack velocity choosing speed. Finally, based on algorithm improvements, this study builds the system structure and develops simulation tests to validate system performance. The English classroom teaching system created in this work can properly determine the real-time status of students by facial recognition and has excellent user satisfaction, indicating that it may effectively increase the teaching impact, according to experimental research.

A New Application of Functional Zonal Image Reconstruction in Programming for Parkinson's Disease Treated Using Subthalamic Nucleus-Deep Brain Stimulation.

Objective:Programming plays an important role in the outcome of deep brain stimulation (DBS) for Parkinson's disease (PD). This study introduced a new application for functional zonal image reconstruction in programming.MethodsFollow-up outcomes were retrospectively compared, including first programming time, number of discomfort episodes during programming, and total number of programming sessions between patients who underwent image-reconstruction-guided programming and those who underwent conventional programming. Data from 142 PD patients who underwent subthalamic nucleus (STN)-DBS between January 2017 and June 2019 were retrospectively analyzed. There were 75 conventional programs and 67 image reconstruction-guided programs.ResultsAt 1-year follow-up, there was no significant difference in the rate of stimulus improvement or superposition improvement between the two groups. However, patients who underwent image reconstruction-guided programming were significantly better at the first programming time, number of discomfort episodes during programming, and total number of programming sessions than those who underwent conventional programming.ConclusionImaging-guided programming of directional DBS leads was possible and led to reduced programming time and reduced patient side effects compared with conventional programming.

Application of computer-aided image reconstruction and image guide in parasagittal meningioma resection

BackgroundIn recent years, smaller-sized (diameter < 2.5 cm) meningiomas are diagnosed due to increased cranial imaging. Symptomatic meningiomas need to be removed surgically. Therefore, it is extremely important to locate the lesion exactly to tailor the craniotomy especially if the neuro-navigation system is not available. Many hospitals located in the underdeveloped countries cannot afford the high costs of neuro-navigation equipment. Hence, it is relevant to discover low-cost associated and effective methods for lesion localization for surgery.MethodsThe use of localization markers in advance can help to acquire preoperative CT images of the patients to create and calculate a three-dimensional (3D) virtual graph using a computer. With the 3D graph, spatial distance of the tumor from the markers is calculated and the tumor location projected on the scalp by the Triangle Pythagorean theorem. This enables precise localization of intracranial microlesions preoperatively.ResultsThe location of the tumor was consistent with that of the pre-operative virtual image, and the craniotomy was exact. The patient was discharged 3 days later without any neurological deficits.ConclusionsThis method is simple and reliable, inexpensive, and accurate in the location of small-sized lesions, which can partially compensate for the lack of neuro-navigation and is suitable for widespread application in hospitals in developing countries.

Deep learning image reconstruction in pediatric abdominal and chest computed tomography: a comparison of image quality and radiation dose.

Studies on the application of deep learning image reconstruction (DLIR) in pediatric computed tomography (CT) are limited and have so far been mostly based on phantom. The purpose of this study was to compare the image quality and radiation dose of DLIR with that of adaptive statistical iterative reconstruction-Veo (ASiR-V) during abdominal and chest CT for the pediatric population. A pediatric phantom was used for the pilot study, and 20 children were recruited for clinical verification. The preset scan parameter noise index (NI) was 5, 8, 11, 13, 15, and 18 for the phantom study, and 8 and 13 for the clinical pediatric study. We reconstructed CT images with ASiR-V 30%, ASiR-V 70%, DLIR-M (medium) and DLIR-H (high). The regions of interest (ROI) were marked on the organs of the abdomen (liver, kidney, and subcutaneous fat) and the chest (lung, mediastinum, and spine). The CT dose index volume (CTDIvol), CT value, image noise (N), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured and calculated. The subjective image quality was assessed by 3 radiologists blindly using a 5-point scale. The dose reduction efficiency of DLIR was estimated. In the phantom study, the interobserver assessment of the data measurement demonstrated good agreement [intraclass correlation coefficient (ICC) =0.814 for abdomen, 0.801 for chest]. Within the same dose level, the N, SNR, and CNR were statistically different among reconstructions, while the CT value remained the same. The N increased and SNR decreased as the radiation dose decreased. The DLIR-H performed better than ASiR-V when the radiation dose was reduced, without sacrificing image quality. In the patient study, the interobserver assessment of the data measurement demonstrated good agreement (ICC =0.774 for abdomen, 0.751 for chest). DLIR-H had the highest subjective and objective scores in the abdomen. Application of DLIR could help to reduce radiation dose without sacrificing the image quality of pediatric CT scans. Further clinical validation is required.

A Nonmonotone Scaled Fletcher–Reeves Conjugate Gradient Method with Application in Image Reconstruction

A Nonmonotone Scaled Fletcher–Reeves Conjugate Gradient Method with Application in Image Reconstruction