Loss Of Image Quality Research Articles

Nuclease‐Resistant L‐DNA Tension Probes Enable Long‐Term Force Mapping of Single Cells and Cell Consortia

AbstractDNA‐based tension probes with precisely programmable force responses provide important insights into cellular mechanosensing. However, their degradability in cell culture limits their use for long‐term imaging, for instance, when cells migrate, divide, and differentiate. This is a critical limitation for providing insights into mechanobiology for these longer‐term processes. Here, we present DNA‐based tension probes that are entirely designed based on the stereoisomer of biological D‐DNA, i.e., L‐DNA. We demonstrate that L‐DNA tension probes are essentially indestructible by nucleases and provide days‐long imaging without significant loss in image quality. We also show their superiority already for short imaging times commonly used for classical D‐DNA tension probes. We showcase the potential of these resilient probes to image minute movements, and for generating long term force maps of single cells and of collectively migrating cell populations.

The optimal way to use geoinformation systems with graphical elements of symbols of veterinary significant objects in vector format

A significant problem in the field of standardization in the monitoring of infectious animal diseases is the lack of a system of conventional symbols of veterinary significant objects used in geoinformation technologies, in particular in digital and electronic cartography. The object of the study is the conventional signs of veterinary significant objects, which in the context of display on digital maps can be defined as "non–scalable", marked with corresponding pictograms. A systematic approach has been applied as a methodological basis for the epizootological research method. The basic graphic format of the proposed SVG symbols is used – (Scalable Vector Graphics), which is a modern generally accepted vector format. This article presents the results of cartographic display of pictograms of one of the three sets of symbols for the database – veterinary facilities for various purposes – for which the method of selecting basic signs is used. An algorithm for fixing elements of symbols of veterinary significant objects in the vector format of the SVG geoinformation system with maximum use of free software and secure information technologies is presented. Images in SVG format can be directly represented in current GIS and converted to almost any vector and raster formats without loss of image quality. The algorithm for developing a system of symbols for veterinary significant objects will be used in the Draft of the preliminary national standard in the future.

Nuclease-Resistant L-DNA Tension Probes Enable Long-Term Force Mapping of Single Cells and Cell Consortia.

DNA-based tension probes with precisely programmableforce response provide important insights into cellularmechanosensing. However, their degradability in cell culture limitstheir use for long-term imaging, for instance, when cells migrate,divide, and differentiate. This is a critical limitation for providinginsights into mechanobiology for these longer-term processes. Here,we present DNA-based tension probes that are entirely designedbased on the stereoisomer of biological D-DNA, i.e., L-DNA. Wedemonstrate that L-DNA tension probes are essentially indestructibleby nucleases and provide days-long imaging without significant lossin image quality. We also show their superiority already for shortimaging times commonly used for classical D-DNA tension probes.We showcase the potential of these resilient probes to image minutemovements, and for generating long term force maps of single cellsand of collectively migrating cell populations.

An Image-Domain Deep-Learning Denoising Technique for Accelerated Parallel Brain MRI: Prospective Clinical Evaluation

Abstract Background Parallel imaging can accelerate MRI acquisitions, but excessive accelerations can introduce amplified noise and aliasing artifacts. Purpose To evaluate a vendor-agnostic AI-based approach to remove image degradation artifacts in highly-accelerated MRI scans, improving image quality and reducing scan time. Materials and Methods Training was performed by retrospectively degrading standard accelerated images. Evaluation was performed with both retrospective and prospectively-collected highly-accelerated images. Retrospective data were taken from ∼2000 MRI studies obtained between August 2016 and October 2022, and prospective data were collected in > 200 subjects between June and November 2022, using scanners from multiple vendors and locations. Scan time data were collected from prospective studies and used to compute time savings per sequence and per protocol for each vendor. Images were evaluated qualitatively by five board-certified radiologists and quantitatively by assessing noise, contrast and spatial resolution. Paired Wilcoxon signed-rank tests were used to compare model outputs to model inputs and low-acceleration images. Results Images from 101 adults from 5 sites and 6 scanner models from different vendors were enrolled. 89% of imaged subjects had noteworthy imaging features or pathology. Model outputs were rated superior to model inputs (p < 0.001) and most were either non-inferior (p inf > 0.05) or superior (p sup < 0.05) to baseline images in qualitative metrics of image quality and feature visibility. Quantitative evaluation of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) improved for model outputs compared to inputs (p < 0.001) or baseline images (p < 0.005). Apparent resolution measured using the full width at half maximum or minimum (FWHM) was either enhanced (p sup < 0.05) or preserved (non-superior p sup > 0.05 and non-inferior p inf > 0.05). The scan time was reduced by an average of 29% (19%-41% per sequence). Conclusion This vendor-agnostic AI-based method achieved robust scan time savings without loss of image quality, potentially allowing for reduced cost and improved patient experience.

Task-based assessment for neural networks: evaluating undersampled MRI reconstructions based on human observer signal detection.

Recent research explores using neural networks to reconstruct undersampled magnetic resonance imaging. Because of the complexity of the artifacts in the reconstructed images, there is a need to develop task-based approaches to image quality. We compared conventional global quantitative metrics to evaluate image quality in undersampled images generated by a neural network with human observer performance in a detection task. The purpose is to study which acceleration (2×, 3×, 4×, 5×) would be chosen with the conventional metrics and compare it to the acceleration chosen by human observer performance. We used common global metrics for evaluating image quality: the normalized root mean squared error (NRMSE) and structural similarity (SSIM). These metrics are compared with a measure of image quality that incorporates a subtle signal for a specific task to allow for image quality assessment that locally evaluates the effect of undersampling on a signal. We used a U-Net to reconstruct under-sampled images with 2×, 3×, 4×, and 5× one-dimensional undersampling rates. Cross-validation was performed for a 500- and a 4000-image training set with both SSIM and MSE losses. A two-alternative forced choice (2-AFC) observer study was carried out for detecting a subtle signal (small blurred disk) from images with the 4000-image training set. We found that for both loss functions, the human observer performance on the 2-AFC studies led to a choice of a 2× undersampling, but the SSIM and NRMSE led to a choice of a 3× undersampling. For this detection task using a subtle small signal at the edge of detectability, SSIM and NRMSE led to an overestimate of the achievable undersampling using a U-Net before a steep loss of image quality between 2×, 3×, 4×, 5× undersampling rates when compared to the performance of human observers in the detection task.

Application of low-dose CT in image-guided radiotherapy based on CT-linac

BackgroundImage-guided radiation therapy (IGRT) involves online medical imaging systems. CT-guided imaging systems not only have high image quality but also allow for direct dose calculation for radiation treatment planning, making it an ideal image guidance method. However, the imaging dose from CT images may pose additional risks to patients. Therefore, the aim of this study was to evaluate the substitutability of using low-dose CT images for IGRT. Materials and methodsThe CIRS-062 phantom, Catphan 604 phantom, CT dose index phantom were used to obtain the conversion curves of the CT values to the relative electron density (RED), image quality, and imaging dose of the low-dose CT, respectively. Catphan 604 phantom and clinical cases were used to analyze the feasibility of low-dose CT for dose calculation. ResultsNo significant differences were observed in the conversion curves of CT values to RED between low-dose and standard-dose CT scans. In terms of image quality, the geometric distortion of low-dose CT was 0.1 mm, which was the same as that of standard-dose CT. The spatial resolution of the images was 0.42–0.45 lp/mm, which was slightly better than that of standard-dose CT (0.40–0.44 lp/mm). The image uniformity was 98.4%–99.0%, slightly worse than that of standard-dose CT (99.4%–99.5%), but still met the clinical requirements. The imaging dose was 2.96–4.62 mGy, significantly lower than that of standard-dose CT (9.71–26.68 mGy), especially for head CT protocol. In terms of dose calculation, the γ passing rates were not lower than 98% for either the Catphan 604 phantom or the clinical cases. ConclusionLow-dose CT can significantly reduce the imaging dose without a significant loss of image quality and can be directly used for dose calculation in radiation treatment planning, providing a safer and more effective image guidance method for IGRT.

Enhanced Semantic Visual Cryptography with AI-driven error reduction for improved two-dimensional image quality and security

Abstract Visual Cryptography (VC) has emerged as a vital technique in the information security domain, with the fundamental purpose of securing 2-Dimensional (2D) image content through encryption and facilitating secure communication. While traditional VC has been instrumental in safeguarding data, it often falls short in maintaining image quality and semantic accuracy upon reconstruction. To address these limitations, this research encompasses the development of an Enhanced Semantic VC (ESVC) model, which aims to refine the encryption process while ensuring the semantic integrity of the images. The ESVC model introduces a new approach that merges VC with artificial intelligence (AI) to enhance 2D image encryption and decryption. The novel aspect of this research lies in the integration of AI-driven reinforcement learning (RL) to increase the quality of the 2D image by measuring the errors between the original secret image and the reconstructed image. This innovative framework is tailored for the secure transmission of 2D grayscale images, ensuring the preservation of semantic integrity while measuring and minimizing image quality loss. By integrating RL algorithms with a measurement of error reduction protocol, the model promises robust encryption capabilities with enhanced resilience against a plethora of cyber threats, thereby elevating the standard for secure image communication. Empirical evaluation of the ESVC model yields promising results, with the Peak Signal-to-Noise Ratio (PSNR) of reconstructed images achieving impressive values between +39 and +42 decibels (dB). These findings underscore the ESVC model’s capability to produce high-fidelity decrypted images, significantly surpassing traditional VC methods in both security and image quality. The research findings illuminate the potential of merging AI with VC to achieve a harmonious balance between computational efficiency and encryption strength, marking a significant advancement in the domain of visual data protection.

Binary classification of dead detector elements in flat panel detectors using convolutional neural networks.

Objective.Medical physicists routinely perform quality assurance on digital detection systems, part of which involves the testing of flat panel detectors. Flat panels may degrade over time as an increasing number of individual detector elements begin to malfunction. The pixels that correspond to these elements are corrected for using information elsewhere in the detector system, however these corrected elements still constitute a loss in image quality for the system as a whole. These correction methods, as well as the location and number of dead detector elements, are often only available to the vendor of the digital detection system, but not to the medical physicist responsible for the quality assurance of the system.Approach.We greatly expand upon a previous work by providing a novel technique for classifying dead detector elements at single pixel resolution. We also demonstrate that this technique can be trained on one detector, and then tested and validated on another with moderate success, which demonstrates some ability to generalize to different detectors. The technique requires 3 flat field, or 'noise', images to be taken to predict the dead detector element maps for the system.Main results.Models using only for-processing pixel data were unable to successfully generalize from one detector to the other. Models preprocessed using the standard deviation across three for-processing images were able to classify dead detector element maps with an F1score ranging from 0.4527 to 0.8107 and recall ranging from 0.5420 to 0.9303 with better performance, on average, observed using the low exposure data set.Significance. Many physicists do not have access to the dead detector maps for their diagnostic digital radiography systems. CNNs are capable of predicting the dead detector maps of flat panel detectors with single pixel resolution. Physicists can implement this tool by acquiring three flat field images and then inputting them into the model. Model performance saw a marginal increase when trained on the low exposure set data, as opposed to the high exposure set data, indicating high exposure, low relative noise images may not be necessary for optimal performance. Model performance across detectors manufactured by different vendors requires further investigation.

No reference image quality assessment using gabor convolutional neural networks

With the emergence of image capturing devices and increase in usage of internet, massive volume of network data was occupied with digital images. For efficient data transmission, the image may undergo several processing units in from the point it captures to the display/storage device. It may result in loss of the perceptual image quality. Therefore, it is necessary to estimate the image quality to measure the quality of experience. It was found that the convolutional neural networks serve as potential tool for effective feature extraction in many image processing applications. Particularly, with the first layer as Gabor filters, the robustness of the network can be reinforced with learnable Gabor parameters. This paper proposes Gabor Convolutional Neural Network method for No-Reference Image Quality Assessment. Their well-defined spatial structured filters are promising in extracting quality features from the local patches and maps them to perceptual quality scores. Our proposed architecture was tested over synthetic and authentic databases such as LIVE, TID2013, CSIQ, LIVE-MD, MDID2016, LIVE Wild and KoNiQ-10k. The proposed approach was also tested on the Waterloo 3D phase-II database, which contains high-resolution images of both the eyes individually with their respective DMOS scores. The proposed approach out performs over LIVE-MD and LIVE Wild and competes with existing algorithm over other databases.

P‐77: A Novel Frequency Doubling Technology with Adaptive Dither

In this paper, in order to improve the refresh rate of large‐size LCD displays, a new optimized driving scheme combined with new GOA timing is implemented in hardware platform, which is called adaptive scanning for frequency multiplication (ASF) technology, and it can be applied to TV, MNT, NB and other fields. Firstly, this paper introduces the basic principle of Dual Line Gate technology (DLG), which is widely used, and analyzes its advantages and disadvantages. And then ASF technology is proposed to solve the disadvantages of DLG technology. The new optimized driving scheme mainly applied inter‐frame adaptive dither algorithm(ADA), odd and even frame interlaced compensation algorithm(OEC) and application mode for intelligent detection of pictures. A prototype driving circuit was fabricated, and various detection tests have been implemented. The results show the refresh rate is doubled without increasing hardware resources, and this technology avoids the loss of image quality.

Dose Reduction in Pediatric Oncology Patients with Delayed Total-Body [18F]FDG PET/CT.

Our aim was to define a lower limit of reduced injected activity in delayed [18F]FDG total-body (TB) PET/CT in pediatric oncology patients. Methods: In this single-center prospective study, children were scanned for 20 min with TB PET/CT, 120 min after intravenous administration of a 4.07 ± 0.49 MBq/kg dose of [18F]FDG. Five randomly subsampled low-count reconstructions were generated using ¼, ⅛, [Formula: see text], and [Formula: see text] of the counts in the full-dose list-mode reference standard acquisition (20 min), to simulate dose reduction. For the 2 lowest-count reconstructions, smoothing was applied. Background uptake was measured with volumes of interest placed on the ascending aorta, right liver lobe, and third lumbar vertebra body (L3). Tumor lesions were segmented using a 40% isocontour volume-of-interest approach. Signal-to-noise ratio, tumor-to-background ratio, and contrast-to-noise ratio were calculated. Three physicians identified malignant lesions independently and assessed the image quality using a 5-point Likert scale. Results: In total, 113 malignant lesions were identified in 18 patients, who met the inclusion criteria. Of these lesions, 87.6% were quantifiable. Liver SUVmean did not change significantly, whereas a lower signal-to-noise ratio was observed in all low-count reconstructions compared with the reference standard (P < 0.0001) because of higher noise rates. Tumor uptake (SUVmax), tumor-to-background ratio, and total lesion count were significantly lower in the reconstructions with [Formula: see text] and [Formula: see text] of the counts of the reference standard (P < 0.001). Contrast-to-noise ratio and clinical image quality were significantly lower in all low-count reconstructions than with the reference standard. Conclusion: Dose reduction for delayed [18F]FDG TB PET/CT imaging in children is possible without loss of image quality or lesion conspicuity. However, our results indicate that to maintain comparable tumor uptake and lesion conspicuity, PET centers should not reduce the injected [18F]FDG activity below 0.5 MBq/kg when using TB PET/CT in pediatric imaging at 120 min after injection.

Efficient Conditional Diffusion Model with Probability Flow Sampling for Image Super-resolution

Image super-resolution is a fundamentally ill-posed problem because multiple valid high-resolution images exist for one low-resolution image. Super-resolution methods based on diffusion probabilistic models can deal with the ill-posed nature by learning the distribution of high-resolution images conditioned on low-resolution images, avoiding the problem of blurry images in PSNR-oriented methods. However, existing diffusion-based super-resolution methods have high time consumption with the use of iterative sampling, while the quality and consistency of generated images are less than ideal due to problems like color shifting. In this paper, we propose Efficient Conditional Diffusion Model with Probability Flow Sampling (ECDP) for image super-resolution. To reduce the time consumption, we design a continuous-time conditional diffusion model for image super-resolution, which enables the use of probability flow sampling for efficient generation. Additionally, to improve the consistency of generated images, we propose a hybrid parametrization for the denoiser network, which interpolates between the data-predicting parametrization and the noise-predicting parametrization for different noise scales. Moreover, we design an image quality loss as a complement to the score matching loss of diffusion models, further improving the consistency and quality of super-resolution. Extensive experiments on DIV2K, ImageNet, and CelebA demonstrate that our method achieves higher super-resolution quality than existing diffusion-based image super-resolution methods while having lower time consumption. Our code is available at https://github.com/Yuan-Yutao/ECDP.

Image quality in CT thorax: effect of altering reconstruction algorithm and tube load.

Non-linear properties of iterative reconstruction (IR) algorithms can alter image texture. We evaluated the effect of a model-based IR algorithm (advanced modelled iterative reconstruction; ADMIRE) and dose on computed tomography thorax image quality. Dual-source scanner data were acquired at 20, 45 and 65 reference mAs in 20 patients. Images reconstructed with filtered back projection (FBP) and ADMIRE Strengths 3-5 were assessed independently by six radiologists and analysed using an ordinal logistic regression model. For all image criteria studied, the effects of tube load 20 mAs and all ADMIRE strengths were significant (p<0.001) when compared to reference categories 65 mAs and FBP. Increase in tube load from 45 to 65 mAs showed image quality improvement in three of six criteria. Replacing FBP with ADMIRE significantly improves perceived image quality for all criteria studied, potentially permitting a dose reduction of almost 70% without loss in image quality.

The Potential of Subsampling and Inpainting for Fast Low-Dose Cryo FIB-SEM Imaging.

Traditional image acquisition for cryo focused ion-beam scanning electron microscopy (FIB-SEM) tomography often sees thousands of images being captured over a period of many hours, with immense data sets being produced. When imaging beam sensitive materials, these images are often compromised by additional constraints related to beam damage and the devitrification of the material during imaging, which renders data acquisition both costly and unreliable. Subsampling and inpainting are proposed as solutions for both of these aspects, allowing fast and low-dose imaging to take place in the Focused ion-beam scanning electron microscopy FIB-SEM without an appreciable loss in image quality. In this work, experimental data are presented which validate subsampling and inpainting as a useful tool for convenient and reliable data acquisition in a FIB-SEM, with new methods of handling three-dimensional data being employed in the context of dictionary learning and inpainting algorithms using a newly developed microscope control software and data recovery algorithm.

Feasibility study of dual-accelerated simultaneous multi-slice imaging in diffusion tensor imaging of glioma.

To explore the value of dual-accelerated simultaneous multi-slice (SMS) imaging in diffusion tensor imaging (DTI) of glioma. Thirty-four patients with glioma who underwent magnetic resonance imaging (MRI) in our hospital from January 2022 to March 2023 were randomly selected. The results of dual-accelerated SMS-DTI and conventional DTI were retrospectively analyzed. All patients were scanned using a uMR790 3.0T MRI scanner, and the scanning technicians followed a predefined sequence to ensure consistency in scan parameters. The images were subjectively evaluated using a Likert 5-point scoring system. Objective evaluation was performed by measuring the required values of the images with b-value = 1000 s/mm2, primarily measuring the signal intensity in the tumor region and the contralateral normal brain white matter region. The standard deviation values were used to calculate the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in the same encoding direction as the background noise. The number of generated fiber pathways, fractional anisotropy (FA), and mean diffusivity (MD) were measured and analyzed using post-processing software. The relative FA (rFA) and relative MD (rMD) were calculated. The results of conventional DTI and SMS-accelerated DTI were compared. In terms of subjective evaluation, including overall image quality, tumor edge clarity, and magnetic sensitivity artifacts, both techniques showed no significant differences, indicating comparable diagnostic performance in anatomical visualization. In terms of objective evaluation and quantitative parameter measurement, there were statistically significant differences in SNR and CNR values, with slightly lower values in the dual-accelerated SMS-DTI compared with conventional DTI, a significant reduction in scanning time can be achieved through a slight loss in image quality. The number of fiber pathways and the rFA and rMD values did not show typical differences between the two techniques. The correlation between these measures was highly similar, with no significant differences observed. The application of dual-accelerated simultaneous multi-slice imaging in DTI of glioma is feasible.

Analysis of the effect of motion on highly accelerated 3D FatNavs in 3D brain images acquired at 3T.

3D FatNavs are rapid acquisitions of MRI fat-volumes within the head that can be used for retrospective motion correction for brain MRI. 3D FatNavs typically use very high acceleration factors and are reconstructed with the GRAPPA parallel imaging technique. However, the GRAPPA reconstruction is not expected to perform well on 3D FatNavs volumes in the presence of strong motion due to the mismatched calibration data acquired once at the start of the scan, leading to motion-parameter misestimation. This study aims to assess the accuracy and precision of 3D FatNav-derived motion-estimates in the presence of large changes in head position. Rigid motion parameters were simulated and applied retrospectively to the 3D FatNav volumes from MPRAGE datasets acquired at 3T. The transformed images were then re-reconstructed using GRAPPA to simulate real motion deterioration of the fat-navigator, and used to estimate the motion applied and evaluate the tracking inaccuracy. This information was then used to estimate the residual motion after 3D FatNav-based motion correction and applied to the original MPRAGE volumes. The effect of the misestimation was assessed using an image quality metric and the evaluation scores from two observers. Quality boundaries were then estimated to assess the motion tolerance when 3D FatNavs are used. The GRAPPA reconstruction was shown to deteriorate for large changes in the head position, affecting the quality of 3D FatNav volumes and consequently degrading the accuracy of the motion-estimates. Based on our simulations, the estimated threshold of motion that led to a noticeable degradation in the MPRAGE image quality was up to RMS values of 3.7° and 3 mm for rotations and translations respectively. 3D FatNavs were shown to be able to correct for a wide range of motion levels and types. Boundaries of acceptable motion magnitudes for different levels of acceptable loss of image quality were determined.

Common Healthcare Related Instruments Subjected To Magnetic Attraction Study (CHRISTMAS): prospective in situ experimental study.

To investigate the behaviour of common healthcare related objects in a 3 tesla (T) MRI (magnetic resonance imaging) scanner, examining their ability to self-propel towards the scanner bore and their potential for tissue penetration. Prospective in situ experimental study. Clinical 3 T MRI scanner. Customised rig designed and built to guide objects towards the scanner bore. 12 categories of objects commonly found in hospitals, or on patients or healthcare professionals, or near an MRI scanning room. Human tissue penetration simulated with ballistic gel (Federal Bureau of Investigation and North Atlantic Treaty Organisation graded). SANTA (site where applied newtonian mechanics triggers acceleration) measurements and depth of tissue penetration of the objects. SANTA measurements ranged from 0 cm for the 20 pence, 50 pence, and £2 coins to 152-161 cm for a knife and the biscuit tins. One penny, two pence, five pence, and 10 pence coins showed self-propulsion and acceleration towards the scanner bore at a distance >100 cm from the gantry entry point. Linear regression analysis showed no apparent correlation between the weight of the objects and their SANTA measurements (R2<0.1). Only five objects penetrated the ballistic gel (simulated human tissue). The deepest penetration was by the knife (5.5 cm), closely followed by the teaspoon (5.0 cm), fork (4.0 cm), spoon (3.5 cm), and a 10 pence coin (0.5 cm). Although the biscuit tins did not penetrate the simulated human tissue, they exerted substantial impact force which could potentially cause bone fractures. A smartphone, digital thermometer, metallic credit card, and pen torch remained fully functional after several passes into the MRI scanner. No discernible loss of image quality for the MRI scanner after the experiments was found. The study highlights the potential for harm (major tissue damage and bone fractures) when commonly found objects in a healthcare setting are unintentionally brought into the MRI scanner room. Patients and healthcare professionals need to be aware of the dangers associated with bringing ferromagnetic objects into the MRI environment.

Evaluation of jitter vibration-induced image interpretability degradation for remote sensing data using GIQE

Remote sensing data plays an important role in various military and civilian applications. The quality of remote sensing data is the key to the success of these applications. remote sensing data are subjected to a variety of distortions from different sources, including acquisition, onboard compression, transmission, on-ground processing, and satellite vibrations. These distortions cause a great loss of image quality. In this paper, jitter vibration is modeled according to the modulation transfer function (MTF). Jitter-induced degradation is evaluated in terms of the National Imagery Interpretability Rating Scale (NIIRS), which is measured in terms of the general image quality equation (GIQE). A study on the third, fourth, and fifth versions of the GIQE is made using a remote sensing data set to select the appropriate version that could be used in evaluating the amount of jitter-induced degradation. The key result of this paper is that the fourth version of the GIQE is the most proper one in evaluating the amount of jitter-induced degradation.

Improved adolescent identity search algorithm for block-based image compression

Image compression has become an indispensable part of the digital world, reducing the costs of storing and transmitting high-quality images. Vector quantization method, which stands out among image compression methods, facilitates the storage and transmission of images without compromising image quality in the process of reducing their size. It uses a codebook to represent similar patterns in a set of images in this method. If the optimum codebook design is achieved, the loss in image quality is minimized by increasing the data compression ratio. It is efficient to use nature-inspired metaheuristic algorithms in the optimization of complex and multidimensional problems such as optimum codebook generation in the image compression process. In this paper, Adolescent Identity Search Algorithm (AISA), which simulates the identity search process of adolescents, is designed for the first time of optimum codebook generation in the block-based image compression process. In addition, improved AISA with various modifications has been proposed to avoid the capture of the conventional AISA by local minimums and to customize the search space. The proposed improved AISA is compared with various well-known metaheuristic algorithms in the literature, and the visual and numerical results are evaluated on various metrics. Based on the simulation results, it has been shown that the proposed IAISA is more efficient than the other metaheuristic algorithms for block-based image compression.

Dual-Layer Detector Head CT to Maintain Image Quality While Reducing the Radiation Dose in Pediatric Patients.

Radiation exposure in the CT diagnostic imaging process is a conspicuous concern in pediatric patients. This study aimed to evaluate whether 60-keV virtual monoenergetic images of the pediatric cranium in dual-layer CT can reduce the radiation dose while maintaining image quality compared with conventional images. One hundred six unenhanced pediatric head scans acquired by dual-layer CT were retrospectively assessed. The patients were assigned to 2 groups of 53 and scanned with 250 and 180 mAs, respectively. Dose-length product values were retrieved, and noise, SNR, and contrast-to-noise ratio were calculated for each case. Two radiologists blinded to the reconstruction technique used evaluated image quality on a 5-point Likert scale. Statistical assessment was performed with ANOVA and the Wilcoxon test, adjusted for multiple comparisons. Mean dose-length product values were 717.47 (SD, 41.52) mGy×cm and 520.74 (SD, 42) mGy×cm for the 250- and 180-mAs groups, respectively. Irrespective of the radiation dose, noise was significantly lower, SNR and contrast-to-noise ratio were significantly higher, and subjective analysis revealed significant superiority of 60-keV virtual monoenergetic images compared with conventional images (all P < .001). SNR, contrast-to-noise ratio, and subjective evaluation in 60-keV virtual monoenergetic images were not significantly different between the 2 scan groups (P > .05). Radiation dose parameters were significantly lower in the 180-mAs group compared with the 250-mAs group (P < .001). Dual-layer CT 60-keV virtual monoenergetic images allowed a radiation dose reduction of 28% without image-quality loss in pediatric cranial CT.