PSNR Research Articles

Deep Learning-Based Synthetic Computed Tomography for Low-Field Brain Magnetic Resonance-Guided Radiation Therapy

PurposeMR-guided Radiation Therapy (MRgRT) enables online adaptation to address intra- and inter-fractional changes. To address the need of high-fidelity synthetic CT (synCT) required for dose calculation, we developed a conditional generative adversarial network (cGAN) for synCT generation from low-field MRI in the brain. Methods and MaterialsSimulation MR-CT pairs from twelve glioma patients imaged with a head and neck surface coil and treated on a 0.35T MR-linac were prospectively included to train the model consisting of a 9-block residual network generator and a PatchGAN discriminator. Four-fold cross validation was implemented. SynCT was quantitatively evaluated against real CT using mean absolute error (MAE), Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). Dose was calculated on synCT applying original treatment plan. Dosimetric performance was evaluated by dose-volume histogram (DVH) metric comparison and local three-dimensional gamma analysis. To demonstrate utilization in treatment adaptation, longitudinal synCTs were generated for qualitative evaluation, and one offline adaptation case underwent two comparative plan evaluations. Secondary validation was conducted with 9 patients on a different MR-linac using a high-resolution brain coil. ResultsOur model generated high-quality synCTs with MAE, PSNR and SSIM of 70.9±10.4 HU, 28.4±1.5 d.B. and 0.87±0.02 within the field-of-view, respectively. Underrepresented post-surgical anomalies challenged model performance. Nevertheless, excellent dosimetric agreement was observed with the mean difference between real and synCT DVH metrics of -0.07±0.29 Gy for target D95 and within [-0.14, 0.02] Gy for organs at risk. Significant differences were only observed in the right lens D0.01cc with negligible overall difference (<0.13 Gy). Mean gamma analysis pass rates were 92.2%±3.0%, 99.2%±0.7% and 99.9%±0.1% at 1%/1mm, 2%/2mm and 3%/3mm, respectively. Secondary validation yielded no significant differences in synCT performance for whole brain MAE, PSNR, and SSIM with comparable dosimetric results. ConclusionsOur cGAN model generated high-fidelity brain synCTs from low-field MRI with excellent dosimetric performance. Secondary validation suggests great promise of implementing synCTs to facilitate robust dose calculation for online adaptive brain MRgRT.

Metric learning guided sinogram denoising for cone beam CT enhancement.

Cone beam computed tomography (CBCT) is a widely available modality, but its clinical utility has been limited by low detail conspicuity and quantitative accuracy. Convenient post-reconstruction denoising is subject to back projected patterned residual, but joint denoise-reconstruction is typically computationally expensive and complex. In this study, we develop and evaluate a novel Metric-learning guided wavelet transform reconstruction (MEGATRON) approach to enhance image domain quality with projection-domain processing. Projection domain based processing has the benefit of being simple, efficient, and compatible with various reconstruction toolkit and vendor platforms. However, they also typically show inferior performance in the final reconstructed image, because the denoising goals in projection and image domains do not necessarily align. Motivated by these observations, this work aims to translate the demand for quality enhancement from the quantitative image domain to the more easily operable projection domain. Specifically, the proposed paradigm consists of a metric learning module and a denoising network module. Via metric learning, enhancement objectives on the wavelet encoded sinogram domain data are defined to reflect post-reconstruction image discrepancy. The denoising network maps measured cone-beam projection to its enhanced version, driven by the learnt objective. In doing so, the denoiser operates in the convenient sinogram to sinogram fashion but reflects improvement in reconstructed image as the final goal. Implementation-wise, metric learning was formalized as optimizing the weighted fitting of wavelet subbands, and a res-Unet, which is a Unet structure with residual blocks, was used for denoising. To access quantitative reference, cone-beam projections were simulated using the X-ray based Cancer Imaging Simulation Toolkit (XCIST). In both learning modules, a data set of 123 human thoraxes, which was from Open-Source Imaging Consortium (OSIC) Pulmonary Fibrosis Progression challenge, was used. Reconstructed CBCT thoracic images were compared against ground truth FB and performance was assessed in root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). MEGATRON achieved RMSE in HU value, PSNR, and SSIM were 30.97±4.25, 37.45±1.78, and 93.23±1.62, respectively. These values are on par with reported results from sophisticated physics-driven CBCT enhancement, demonstrating promise and utility of the proposed MEGATRON method. We have demonstrated that incorporating the proposed metric learning into sinogram denoising introduces awareness of reconstruction goal and improves final quantitative performance. The proposed approach is compatible with a wide range of denoiser network structures and reconstruction modules, to suit customized need or further improve performance.

Optimized underwater light attenuation prior-based depth estimation and adaptive feature fusion CNN for underwater image and video enhancement

Underwater images and videos play an essential communication tool in exploring ocean resources and understanding underwater scene perception. The underwater imaging environment and the complex lighting conditions degrade the quality of the images and make them low contrast, color deviation, and blurring. These degraded images lack effective target recognition information, significantly impacting the performance of underwater application systems. To solve the above issues, a new module for underwater video as well as image enhancement is proposed. The input video is given to the frame extraction phase for converting the video into the frame. Thereafter, the blur detection and determination are done using Laplacian's variance technique for determining blurred images. The depth estimation is done using Underwater Light Attenuation Prior (ULAP), wherein coefficients are determined using the proposed Competitive Multi-Verse bird swarm Optimization (CMVBSO). The CMVBSO is combined by integrating a competitive multiverse optimizer (CMVO) and a Bird Swarm Algorithm (BSA). The blurred value, input video frame and depth estimated output are given to adaptive feature fusion CNN in which training is done utilizing Manta-Ray Foraging Lion Optimization (MRLLO). The pixel enhancement is carried out using a Type II Fuzzy system and Cuckoo Search optimization algorithm filter (T2FCS) filter. The proposed CMVBSO_ULAP provided better performance with a Peak signal to noise ratio (PSNR) of 35.037 dB, Mean square error (MSE) of 3.684, Structural similarity index (SSIM) of 0.911, underwater image quality measure (UIQM) of 5.266 and Underwater Color Image Quality Evaluation (UCIQE) of 0.818 respectively.

ClarityDiffuseNet: Enhancing fundus image quality under black shadows with diffusion model-based research

Deep learning models have achieved commendable success in the analysis of tasks related to fundus images. However, the performance of many models is affected by the quality of fundus images. A common quality issue observed in fundus images is the presence of severe black shadow artefact, primarily caused by opacities in the refractive media or due to insufficient or uneven illumination. Such low-quality images can compromise the model training and result in the models learning incorrect feature representations. The removal of black shadows can be regarded as a preprocessing problem in the enhancement of degraded images. Solutions typically involve either increasing the overall brightness of the image or restoring the dark shadowy areas specifically. Prior work on increasing image brightness has often utilized generative adversarial networks (GANs), while restoration has been approached with autoencoders and variational autoencoders (VAEs). However, approaches that focus on brightening often fall short in properly addressing local degradations, and restoration techniques can lead to loss of details or over-smoothing in the shadow areas.In this study, we introduce a method named ClarityDiffuseNet, a model for restoring low-quality fundus images based on diffusion generative models targeting severe black shadows. Our method restores areas with black shadows from regions of high quality, enhancing the image to be richer in detail and visually closer to artefact-free images. Compared to models based on GANs and inpainting methods, our approach demonstrates superior performance on four benchmark public datasets, with Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity index (SSIM) indices significantly surpassing the state-of-the-art models by 7% and 9%, respectively. Our method demonstrates notable improvements in downstream tasks, such as disease diagnosis—evidenced by 9% increase in the area under the curve (AUC) metric when tested on low-quality datasets—in vessel segmentation, which saw about 6% improvement in the Dice coefficient under similar conditions. These outcomes underscore the substantial promise of diffusion generative models within the realm of fundus image restoration, highlighting their effectiveness in enhancing image quality for further analysis.

Reversible image hiding algorithm based on adaptive embedding mechanism

A reversible blind image hiding algorithm (ReBIHA) with help of an adaptive embedding mechanism and a compressive sensing technology is presented in this paper, of which can effectively hide the secret image through a natural meaningful carrier image. In the first phase, a new key generation model (KeyGM) is built by combining random numbers and the plaintext, followed by a newly designed four dimensional chaotic map (NewCM) with stronger pseudo-randomness. Then, the generated chaotic sequence is taken to produce a measurement matrix for compressive sensing. In the second phase, the plain image is sparsely processed, confused, encrypted and compressed to get measurements. Afterward, these measurements are subjected to diffusion and binary exchange to achieve a middle cipher image (MCI). In the third stage, a meaningful carrier image is randomly selected, and integer wavelet transform (IWT) is applied to it, getting one low-frequency and three high-frequency coefficients. Then, the pixels of MCI are subjected by splitting and symmetrically shifting transformation for three element matrices. Finally, these matrices are respectively embedded into the high-frequency coefficients through an adaptive mechanism to find replaceable elements, and a final carrier image hiding secrets (CiHS) can be achieved after performing an inverse IWT. The experimental results show that the proposed algorithm can avoid the extra transmission of original carrier image. Moreover, peak signal-to-noise ratio (PSNR) of reconstructed image can surpass 32 dB at compression ratio 0.5. Especially, the proposed adaptive embedding mechanism can not only achieve blind extraction of secrets well, but also ensure the visual quality for the original carrier image.

An Adaptive X‐Ray Dynamic Image Estimation Method Based on OMNI Solar Wind Parameters and SXI Simulated Observations

AbstractObservations of the overall interactions between solar wind and the Earth's magnetosphere are crucial for space weather monitoring. Upcoming missions like the Solar Wind Magnetosphere Ionosphere Link Explorer (SMILE) and the Lunar Environment heliosphere X‐ray Imager (LEXI) aim to make comprehensive global imaging of Earth's magnetosphere using soft X‐ray imager (SXI) in order to understand its dynamic response to solar wind impact. Short‐duration X‐ray images have a low signal‐to‐noise ratio (SNR), limited by cosmic background and Poisson noise. Longer integration times provide better SNR of magnetospheric structures but fail to capture the short‐term dynamics during the integration. Our study introduces a neural network method which is able to estimate the short‐term dynamics during a long integration, driven by OMNI solar wind data and simulated soft X‐ray images. Specifically, an adaptive X‐ray image estimator and a spatio‐temporal discriminator are used. It leverages X‐ray models like Magnetohydrodynamic (MHD) and Jorgensen &amp; Sun model, driven by OMNI data to provide high‐temporal‐resolution prior information on magnetosphere motion, with SXI observation images acting as a posterior constraint on the magnetosphere's state. Experimental validation demonstrates apparent improvements in Peak signal‐to‐noise ratio (PSNR) and Structural Similarity (SSIM) compared to traditional linear and optical flow interpolation methods. The method's flexibility, considering input‐output consistency, enables easy extension to any interval (&gt;3 min), meeting diverse application needs. In conclusion, our study presents a new approach to soft X‐ray image estimation based on neural networks, providing insights into magnetospheric dynamics as observed in soft X‐rays.

Supervised deep learning-based synthetic computed tomography from kilovoltage cone-beam computed tomography images for adaptive radiation therapy in head and neck cancer.

To generate and investigate a supervised deep learning algorithm for creating synthetic computed tomography (sCT) images from kilovoltage cone-beam computed tomography (kV-CBCT) images for adaptive radiation therapy (ART) in head and neck cancer (HNC). This study generated the supervised U-Net deep learning model using 3,491 image pairs from planning computed tomography (pCT) and kV-CBCT datasets obtained from 40 HNC patients. The dataset was split into 80% for training and 20% for testing. The evaluation of the sCT images compared to pCT images focused on three aspects: Hounsfield units accuracy, assessed using mean absolute error (MAE) and root mean square error (RMSE); image quality, evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) between sCT and pCT images; and dosimetric accuracy, encompassing 3D gamma passing rates for dose distribution and percentage dose difference. MAE, RMSE, PSNR, and SSIM showed improvements from their initial values of 53.15 ± 40.09, 153.99 ± 79.78, 47.91 ± 4.98 dB, and 0.97 ± 0.02 to 41.47 ± 30.59, 130.39 ± 78.06, 49.93 ± 6.00 dB, and 0.98 ± 0.02, respectively. Regarding dose evaluation, 3D gamma passing rates for dose distribution within sCT images under 2%/2 mm, 3%/2 mm, and 3%/3 mm criteria, yielded passing rates of 92.1% ± 3.8%, 93.8% ± 3.0%, and 96.9% ± 2.0%, respectively. The sCT images exhibited minor variations in the percentage dose distribution of the investigated target and structure volumes. However, it is worth noting that the sCT images exhibited anatomical variations when compared to the pCT images. These findings highlight the potential of the supervised U-Net deep learningmodel in generating kV-CBCT-based sCT images for ART in patients with HNC.

A Hybrid System Based on Three Levels to Hide Information using JPEG Color Images

Nowadays, due to the circulation of information among people, information security has become an important requirement to protect information from hacking. Many techniques have been discovered to protect this information and maintain its confidentiality from hacking, the most prominent of which are cryptography and steganography techniques. In this paper, a hybrid system based on three levels to hide information using Joint Photographic Experts Group (JPEG) color images, where this system works in both processes sender and receiver. The sender process at the first level uses a mixing of the Advanced Encryption Standard (AES) and Rivest Shamir Adleman (RSA), while at the second level uses a Fuzzy Stream Algorithm (FSA) adds a higher complexity and eliminates the non-linearity of the encrypted information. The third level is to hide encrypted information using the Least Significant Bit (LSB) technique, while the receiver process, performs the reverse process of three levels. This system provides high information embedding capability and the inability to perceive hidden information, the system was evaluated based on criteria like Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE), Structure Similarity (SSIM), and correlation, which were characterized by high and good rates. The study was compared with modern steganography techniques.

Thin Cloud Removal Generative Adversarial Network Based on Sparse Transformer in Remote Sensing Images

Thin clouds in Remote Sensing (RS) imagery can negatively impact subsequent applications. Current Deep Learning (DL) approaches often prioritize information recovery in cloud-covered areas but may not adequately preserve information in cloud-free regions, leading to color distortion, detail loss, and visual artifacts. This study proposes a Sparse Transformer-based Generative Adversarial Network (SpT-GAN) to solve these problems. First, a global enhancement feature extraction module is added to the generator’s top layer to enhance the model’s ability to preserve ground feature information in cloud-free areas. Then, the processed feature map is reconstructed using the sparse transformer-based encoder and decoder with an adaptive threshold filtering mechanism to ensure sparsity. This mechanism enables that the model preserves robust long-range modeling capabilities while disregarding irrelevant details. In addition, inverted residual Fourier transformation blocks are added at each level of the structure to filter redundant information and enhance the quality of the generated cloud-free images. Finally, a composite loss function is created to minimize error in the generated images, resulting in improved resolution and color fidelity. SpT-GAN achieves outstanding results in removing clouds both quantitatively and visually, with Structural Similarity Index (SSIM) values of 98.06% and 92.19% and Peak Signal-to-Noise Ratio (PSNR) values of 36.19 dB and 30.53 dB on the RICE1 and T-Cloud datasets, respectively. On the T-Cloud dataset, especially with more complex cloud components, the superior ability of SpT-GAN to restore ground details is more evident.

A Method for Fingerprint Edge Enhancement Based on Radial Hilbert Transform

Fingerprints play a significant role in various fields due to their uniqueness. In order to effectively utilize fingerprint information, it is necessary to enhance image quality. This paper introduces a method based on Radial Hilbert transform (RHLT), which simulates the vortex filter using the point spread function (PSF) of spiral phase plate (SPP) with a topological charge l=1, for fingerprint edge enhancement. The experimental results show that the processed fingerprint image has more distinct edges, with an increase in information entropy and average gradient. Unlike classical edge detection operators, the fingerprint edge image obtained by the RHLT method exhibits a lower mean square error (MSE) and a higher peak signal-to-noise ratio (PSNR). This indicates that the RHLT method provides more accurate edge detection and demonstrates higher noise-resistance capabilities. Due to its ability to highlight edge information while preserving more original features, this method has great application potential in fingerprint image processing.

Image Restoration Technology of Tang Dynasty Tomb Murals Using Adversarial Edge Learning

The city of Xi’an, China houses a vast collection of valuable Tang dynasty (618 AD-907 AD) tomb murals that have experienced various degrees of damage over time due to weathering. This research proposes an adversarial edge learning-based mural restoration technique that provides a fast and accurate automatic repair of the murals. The method uses generative adversarial networks to learn how to repair the edge contour of a damaged mural and restore the missing content. The contour restoration network and mural restoration networks are trained and updated simultaneously to enhance the feature extraction and restoration coordination performance of the network. The experiments were conducted on a self-built Tang dynasty tomb mural painting restoration dataset and compared to several existing algorithms. The proposed algorithm showed a 1.3 increase in peak signal-to-noise ratio (PSNR) and a 1.43% increase in structural similarity (SSIM) value, providing a precise restoration of the structure and contour of the mural content. The algorithm successfully repaired damaged murals with fractures and small missing parts. The suggested method can be useful for both the technical repair and conservation of significant national visual cultural property and the digital restoration of historic murals.

Ground penetrating radar clutter suppression based on the weighted tensor Schatten p-paradigm minimization of tensor principal components

The issue of clutter suppression in ground-penetrating radar (GPR) detection has consistently been a prominent area of research. The existing tensor robust principal component analysis (TRPCA) approach encounters a challenge when handling different singular values, whereby all singular values are reduced to the same degree, leading to loss of information in the low-rank part. To address the aforementioned issues, this study developed a TRPCA method based on weighted tensor Schatten p-paradigm minimisation (p-TRPCA). This approach assigns distinct weights to each singular value by using a weighted tensor Schatten p-paradigm, facilitating the optimal decomposition of GPR data into low-rank clutter and sparse target components. The processing of target echoes is then completed by applying a threshold judgement to the results. The efficacy of the proposed method was validated through simulations and real-world testing. Its performance was benchmarked against established advanced clutter removal techniques in terms of peak signal-to-noise ratio (PSNR) and signal-to-clutter ratio. The findings demonstrated that the method effectively delineated the low-rank clutter matrix and sparse target matrix within the B-scan image and exhibited superior SNR compared to other methods.

Image deconvolution using hybrid threshold based on modified L1-clipped penalty in EM framework

Image deconvolution remains a challenging task due to its inherent ill-posedness. While existing algorithms show strong numerical performance, their complexity often complicates analysis and implementation. This paper introduces a computationally efficient image deconvolution method within the expectation maximization (EM) framework. The proposed algorithm alternates between an E-step leveraging the fast Fourier transform (FFT) and an M-step utilizing the discrete wavelet transform (DWT). In the M-step, we introduce a novel L1-clipped penalty to compute the maximum a posteriori (MAP) estimate, resulting in a hybrid threshold that combines the strengths of soft and hard thresholding. This hybrid threshold is mathematically derived, overcoming the high variance of hard-thresholding and the high bias of soft-thresholding, thus optimizing the trade-off between variance and bias. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art techniques in terms of improved signal-to-noise ratio (ISNR) and peak signal-to-noise ratio (PSNR), as well as visual quality. Notably, the proposed method shows average PSNR improvements of 3.49 dB, 4.23 dB, and 1.44 dB for uniform blur and 0.76 dB, 3.57 dB, and 0.66 dB for Gaussian blur on the Set12, BSD68, and Set14 datasets, respectively.

Removal of Fix Magnitude Impulsive Noise (FMIN) Through Innovative Recursive MDBUTMF Procedure

This article proposes an innovative recursive modied decision based unsymmetrical trimmed median filter (RMDBUTMF) procedure for noisy overriding of digital photographs, which are eminently contaminated by FMIN. The proposed procedure reinstates the noisy photographical basis (which has magnitude at 0 or 255) by trimmed median magnitude (or the mean magnitude of all the free-noise photographical basis) in the computational photographical basis region under the recursive framework. The proposed procedure is experimented on distinctive digital photographs (Lena, Girl, Pepper and F16) on broad noise density and the proposed procedure reveals superior noisy-overridden photographs than the Mean Filter (MF), Median Filter (SMF), Adaptive Median Filter (AMF), Weight Median Filter (WMF), MDBUTMF in both Peak Signal-to-Noise Ratio (PSNR) and photographical quality.

A robust audio steganography technique based on image encryption using different chaotic maps

The development of innovative methods for concealing critical data in multimedia files has exploded in information security in recent years. Cryptography and steganography cannot be used alone to protect data; rather, they can be combined and used in a single system. Audio steganography is among the most important information security techniques. It involves the concealment of information within audio signals to achieve covert communication. This paper introduces a comprehensive technique that integrates chaos Henon, Baker, and Arnold maps for image encryption with audio steganography to create a robust and secure audio steganography technique. First, the target image is encrypted using chaotic maps. Then, it is embeded within the high frequencies of the cover audio signal based on the Inverse Short Time Fourier Transform (ISTFT) to be transmitted to the destination through the channel. By integrating both encryption and concealment techniques, the cover audio signal quality can be preserved. Moreover, the hidden image security and robustness are improved, making the technique resistant to many types of attacks. The simulation results confirm that the suggested technique is robust in the presence of attacks. It achieves a distinct perceptual quality with an appreciated peak signal-to-noise ratio (PSNR) of 91.2 dB and a Mean Square Error (MSE) of 7.5 × 10–10. The randomness of the resulting encrypted image has successfully passed the National Institute of Standards and Technology (NIST) statistical test suite.

Analysis of methods and algorithms for image filtering and quality enhancement

This paper addresses the prevalent issue of image noise and presents methods for its mitigation. The paper describes, analyses and tests a variety of image filtering techniques, with specific reference to their use in different contexts. The filtering methods can be classified into two principal categories: linear filters, which include the Gaussian and mean filters, and non-linear filters, which comprise the median filter, the Fast Fourier Transform (FFT), the Non-Local Means (NLM) filter, and the anisotropic diffusion filter. The efficacy of each filter is mathematically described and evaluated on RGB images using the Python programming language. The study delineates the evaluation metrics and their respective advantages and disadvantages. The Root Mean Square Error (RMSE) and Peak Signal-to-Noise Ratio (PSNR) are employed as criteria for the analysis of algorithm efficiency. Furthermore, the mean execution time for each algorithm is also monitored. The experimental data suggests that linear filters are relatively fast but produce inferior results and are best employed as preparatory measures. Non-linear filters have been demonstrated to be more robust and applicable to a variety of noise types, although it has been established that they require parameter fine-tuning. The study demonstrates that anisotropic diffusion is suitable for both manual image processing and real-time applications, offering an optimal balance between processing speed and denoised image quality. NLM is optimal for high-quality single image processing due to its superior results, despite a slower processing speed. FFT is noted for its efficiency in eliminating periodic noise. Further research will be conducted on advancing filtering techniques for different real-world scenarios and autonomous systems.

Multiobjective optimization guided by image quality index for limited-angle CT image reconstruction.

Due to the incomplete projection data collected by limited-angle computed tomography (CT), severe artifacts are present in the reconstructed image. Classical regularization methods such as total variation (TV) minimization, ℓ0 minimization, are unable to suppress artifacts at the edges perfectly. Most existing regularization methods are single-objective optimization approaches, stemming from scalarization methods for multiobjective optimization problems (MOP). To further suppress the artifacts and effectively preserve the edge structures of the reconstructed image. This study presents a multiobjective optimization model incorporates both data fidelity term and ℓ0-norm of the image gradient as objective functions. It employs an iterative approach different from traditional scalarization methods, using the maximization of structural similarity (SSIM) values to guide optimization rather than minimizing the objective function.The iterative method involves two steps, firstly, simultaneous algebraic reconstruction technique (SART) optimizes the data fidelity term using SSIM and the Simulated Annealing (SA) algorithm for guidance. The degradation solution is accepted in the form of probability, and guided image filtering (GIF) is introduced to further preserve the image edge when the degradation solution is rejected. Secondly, the result from the first step is integrated into the second objective function as a constraint, we use ℓ0 minimization to optimize ℓ0-norm of the image gradient, and the SSIM, SA algorithm and GIF are introduced to guide optimization process by improving SSIM value like the first step. With visual inspection, the peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and SSIM values indicate that our approach outperforms other traditional methods. The experiments demonstrate the effectiveness of our method and its superiority over other classical methods in artifact suppression and edge detail restoration.

Assessment of image-derived input functions from small vessels for patlak parametric imaging using total-body PET/CT.

The image-derived input function (IDIF) from the descending aorta has demonstrated performance comparable to arterial blood sampling while avoiding its invasive nature in parametric imaging. However, in conventional PET, large vessels may not always be within the imaging field of view (FOV). This study aims to evaluate the efficacy of dynamic parametric Ki imaging using image-derived input functions (IDIFs) extracted from various arteries, facilitated by total-body PET/CT. Twenty-three participants underwent a 60-minute total-body [18F]FDG PET scan. Data from each subject were used to reconstruct both total-body PET images and short-axis field-of-view PET images at different bed positions, each with a 25cm axial field-of-view (AFOV). Partial volume correction (PVC) was performed using the blurred Van Cittert iterative deconvolution. IDIFs extracted from the descending aorta, carotid artery, abdominal aorta, and iliac artery were employed for Patlak analysis. The resulting Ki images were compared using quantification indicators and subjective assessment. Linear regression analysis was conducted to examine the correlation of Ki values among IDIFs in normal organ and lesion regions of interest (ROIs). High similarities were observed in Ki images derived from the IDIFs from the descending aorta and other arteries, with a median structural similarity index measure (SSIM) above 0.98 and a median peak signal-to-noise ratio (PSNR) above 37dB. Linear regression analysis revealed strong correlations in Ki values (r² > 0.88) between the descending aorta and the three alternative vessels, with slopes of the linear fits close to 1. No significant difference in lesion detectability among IDIFs was found, as assessed visually and using metrics such as tumor-to-background ratio (TBR) and contrast-to-noise ratio (CNR) (P < 0.05). IDIFs from smaller vessels can reliably reconstruct parametric Ki images without compromising lesion detectability, providing clinically relevant information.

Enhancing brain image quality with 3D U-net for stripe removal in light sheet fluorescence microscopy

Light Sheet Fluorescence Microscopy (LSFM) is increasingly popular in neuroimaging for its ability to capture high-resolution 3D neural data. However, the presence of stripe noise significantly degrades image quality, particularly in complex 3D stripes with varying widths and brightness, posing challenges in neuroscience research. Existing stripe removal algorithms excel in suppressing noise and preserving details in 2D images with simple stripes but struggle with the complexity of 3D stripes. To address this, we propose a novel 3D U-net model for Stripe Removal in Light sheet fluorescence microscopy (USRL). This approach directly learns and removes stripes in 3D space across different scales, employing a dual-resolution strategy to effectively handle stripes of varying complexities. Additionally, we integrate a nonlinear mapping technique to normalize high dynamic range and unevenly distributed data before applying the stripe removal algorithm. We validate our method on diverse datasets, demonstrating substantial improvements in peak signal-to-noise ratio (PSNR) compared to existing algorithms. Moreover, our algorithm exhibits robust performance when applied to real LSFM data. Through extensive validation experiments, both on test sets and real-world data, our approach outperforms traditional methods, affirming its effectiveness in enhancing image quality. Furthermore, the adaptability of our algorithm extends beyond LSFM applications to encompass other imaging modalities. This versatility underscores its potential to enhance image usability across various research disciplines.

Secret image restoration with interpolation and social network search

Image restoration is an essential task in image processing. Due to the secret image extracted from the corrupted stego-image and its pixels may suffer different levels of damage, the type of noise is particular. Most of the existing methods have a challenge in directly removing this type of noise. Hence, we propose a corrupted secret image restoration algorithm with interpolation and social network search. Firstly, a corrupted secret image is divided into several blocks, and the convex hull of each block is calculated. For the corrupted pixels in the convex hull, the natural neighbor interpolation and bi-harmonic spline interpolation are utilized to generate the estimation values of corrupted pixels according to the density of the trusted pixels. For the corrupted pixels outside the convex hull, the social network search algorithm is applied to generate the estimated values of corrupted pixels. Finally, all corrupted pixels are recovered by the ones of their candidate values closest to their estimated values. The performance of the various methodologies was evaluated using the USCSIPI database, employing peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) indices, which demonstrated an enhancement of more than 0.78 dB and 0.0431, respectively. Experiments show that our algorithm outperforms other state-of-the-art (SOTA) algorithms.