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BACKGROUND: Resection of the contrast-enhancing (CE) tumor represents the standard of care in newly diagnosed glioblastoma. However, some tumors ultimately diagnosed as glioblastoma lack contrast enhancement and have a low-grade appearance on imaging (non-CE glioblastoma). We aimed to (a) volumetrically define the value of non-CE tumor resection in the absence of contrast enhancement, and to (b) delineate outcome differences between glioblastoma patients with and without contrast enhancement. METHODS: The RANO resect group retrospectively compiled a global, eight-center cohort of patients with newly diagnosed glioblastoma per WHO 2021 classification. The associations between postoperative tumor volumes and outcome were analyzed. Propensity score-matched analyses were constructed to compare glioblastomas with and without contrast enhancement. RESULTS: Among 1323 newly diagnosed IDH-wildtype glioblastomas, we identified 98 patients (7.4%) without contrast enhancement. In such patients, smaller postoperative tumor volumes were associated with more favorable outcome. There was an exponential increase in risk for death with larger residual non-CE tumor. Accordingly, extensive resection was associated with improved survival compared to lesion biopsy. These findings were retained on a multivariable analysis adjusting for demographic and clinical markers. Compared to CE glioblastoma, patients with non-CE glioblastoma had a more favorable clinical profile and superior outcome as confirmed in propensity score analyses by matching the patients with non-CE glioblastoma to patients with CE glioblastoma using a large set of clinical variables. CONCLUSIONS: The absence of contrast enhancement characterizes a less aggressive clinical phenotype of IDH-wildtype glioblastomas. Maximal resection of non-CE tumors has prognostic implications and translates into favorable outcome.

The underwater imaging environment is very different from land, and some common land image enhancement methods are often not applicable to the underwater environment. This paper proposes a two-step underwater image enhancement method. White balance is a commonly used color correction method. In underwater environments, the traditional white balance method has certain limitations and results in severe color bias. This is caused by the faster attenuation of red light in underwater environments. We develop a new white balance method based on the assumption of the gray world method. A red correction module is embedded in the method, which is more suitable for underwater environments. For contrast correction, we design an illuminance correction method based on the Retinex model. The method significantly reduces the computational burden compared to traditional methods, while enhancing the brightness and contrast of the images. In addition, most of the current underwater image enhancement methods deal with color and contrast issues separately. However, these two factors influence each other, and processing them separately may lead to suboptimal results. Therefore, we investigate the relationship between color and contrast and propose a trade-off method. Our method integrates color and contrast within a histogram framework, achieving a balanced enhancement of both aspects. To avoid chance, we utilized four datasets, each containing 800 randomly selected images for metric testing. On the five non-referential metrics, three firsts and two seconds were ranked. Our method ranked second on two referenced metrics. Superior results were also achieved in runtime comparisons. Finally, we further demonstrate the superiority of our method through detailed demonstrations and ablation experiments. [ABSTRACT FROM AUTHOR]

In autonomous driving, capturing high-quality images with visual sensors in adverse weather conditions presents a significant challenge for object detection. This paper introduces a candid and effective preprocessing method called Contrast Enhancement through Mutual Entropy (CEME) to improve the visual quality of images. Unlike previous methods such as traditional image processing, image restoration, and deep learning techniques, CEME enhances image quality using simple filtering operations. CEME works by adjusting gray levels appropriately through the calculation of mutual entropy between adjacent gray levels in each plane of a color image. Experimental simulations were conducted on various images taken in weather conditions like snow, fog, sand, and rain. To evaluate performance, this study used two natural image quality assessment metrics: Novel Blind Image Quality Assessment (NBIQA) and Natural Image Quality Evaluator (NIQE). The proposed method achieved an average NBIQA for sandy, snowy, rainy, and foggy images of 28.1576, 35.7233, 29.8796, and 36.1944 respectively. [ABSTRACT FROM AUTHOR]

Contrast enhancement is a crucial aspect of image processing, as it improves visual quality by adjusting the brightness and contrast of an image. This paper comprehensively explores contrast enhancement techniques, classified into three categories: Image Processing (IP) based methods Deep Learning (DL) based approaches, and Generative Adversarial Network (GAN) methods. The paper also details various quality evaluation methods for enhanced images and compares different algorithms. The performance of the presented algorithms is evaluated using metrics such as Structural Similarity Index Measurement (SSIM), Absolute Mean Brightness Error (AMBE), Average Information Content (AIC), Contrast Improvement Index (CII), Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Universal Quality Index (UQI), and Color Enhancement Factor (CEF). The comparative analysis aims to provide insights into improving image quality, information content and error production within each category, facilitating informed decision-making in selecting contrast enhancement techniques for diverse applications. [ABSTRACT FROM AUTHOR]

Image enhancement is a fundamental prerequisite for every computer vision program that intends to process an image further. When applied to practically undetectable photos, one of the most common limitations of most existing approaches is the loss of color information throughout the improvement process. In this work, a method based on the enhancement of base as well as detail images using the concept of global and local enhancement for highly degraded images has been proposed which improves the highly degraded image along with preserving its color as well as naturalness. In this novel method, a global–local technique is proposed that breaks the image into smoother and sharper regions called base and detail images. The base image consists of the smoother regions in the input image whereas the detail image contains the sharp edges. The global approach is applied for improving the base image and the local technique is applied for the enhancement of the detail image, containing the sharper edges. The base and detail images are estimated using the median and weighted least squares (WLS) filters respectively. The base image is enhanced using the global approach using the modified form of AGC based on the cumulative histogram. The value of gamma is derived from the image parameters, which makes the proposed method adaptive and applicable to a wide range of images with different contrast degradations. The detail image is enhanced using the newly introduced parameter RoE, which ensures that the enhancement of the detail image is in fine tune with the base image. The enhanced base and detail images are combined and scaled to bring the intensity levels to the permitted range. Finally, mean adjustment is applied to develop the final improved image. The approach improves visual contrast while preserving naturalness. The simulation results on typical datasets show that the suggested technique outperforms numerous state-of-the-art as well as traditional algorithms for extremely degraded images. [ABSTRACT FROM AUTHOR]

For a better understanding of living tissues and materials, it is essential to study the intricate spatial relationship between cells and their surrounding tissue on the nanoscale, with a need for 3D, high‐resolution imaging techniques. In the case of bone, focused ion beam‐scanning electron microscopy (FIB‐SEM) operated in the backscattered electron (BSE) mode proves to be a suitable method to image mineralized areas with a nominal resolution of 5 nm. However, as clinically relevant samples are often resin‐embedded, the lack of atomic number (Z) contrast makes it difficult to distinguish the embedding material from unmineralized parts of the tissue, such as osteoid, in BSE images. Staining embedded samples with iodine vapor has been shown to be effective in revealing osteoid microstructure by 2D BSE imaging. Based on this idea, an iodine (Z = 53) staining protocol is developed for 3D imaging with FIB‐SEM, investigating how the amount of iodine and exposure time influences the imaging outcome. Bone samples stained with this protocol also remain compatible with confocal laser scanning microscopy to visualize the lacunocanalicular network. The proposed protocol can be applied for 3D imaging of tissues exhibiting mineralized and nonmineralized regions to study physiological and pathological biomineralization. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim Materials and Methods Results Conclusion To develop and evaluate a novel multi‐method micro‐computed tomography (μCT) imaging protocol for enhanced visualization of both hard and soft tissues in murine mandibles, addressing the limitations of traditional imaging techniques in dental research.We employed a contrast‐enhanced (CE) μCT imaging technique using Lugol's iodine as a contrast agent to visualize the intricate structures of murine mandibles. The protocol involved the combination of conventional μCT imaging as well as CE‐μCT, including decalcification with EDTA, allowing for simultaneous assessment of hard and soft tissues. The method is compared with standard imaging modalities, and the ability to visualize detailed anatomical features is discussed.The CE‐μCT imaging technique provided superior visualization of murine mandibular structures, including dental pulp, periodontal ligaments and the surrounding soft tissues, along with conventional μCT imaging of alveolar bone and teeth. This method revealed detailed anatomical features with high specificity and contrast, surpassing traditional imaging approaches.Our findings demonstrate the potential of CE‐μCT imaging with Lugol's iodine as a powerful tool for dental research. This technique offers a comprehensive view of the murine mandible, facilitating advanced studies in tissue engineering, dental pathology and the development of dental materials. [ABSTRACT FROM AUTHOR]

Passive millimeter-wave (MMW) and TeraHertz (THz) imaging systems have become increasingly popular in recent years due to their cost-effectiveness and non-invasive characteristics compared to active systems, prompting a surge in research interest. Evaluating the quality of reconstructed images used in these systems is essential for revealing the fine details. General image quality metrics such as the structural similarity index (SSIM) and the peak signal-to-noise ratio (PSNR) require a reference image in order to compare the reconstructed image. However, there is a notable gap in the literature regarding the evaluation of reconstruction or deconvolution algorithms with a reference image in the passive MMW/THz bands. This study proposes a reference image generation technique for passive MMW/THz imaging systems using an infrared imaging system that shares a similar physical background. Then, passive MMW/THz images were evaluated using the reference images at varying target distances and spatial resolutions. Besides these, the assessment of passive MMW/THz images with the SSIM and PSNR metrics after the reconstruction algorithms were performed. The metrics SSIM and PSNR, are inadequate in the evaluation of reconstruction algorithms alone in terms of concealed object (CO) detection. Because of this reason, the contrast level (CL) method was proposed to address the application-based shortcomings of PSNR and SSIM metrics. Hence, the image quality metric, CL, indicates that the Richardson–Lucy (RL) algorithm yielded superior results in variable optical configurations and target distances with the aid of CL metric. Finally, contrast enhancement techniques were developed in order to increase the contrast level of the CO. As a result, the introduction of these novel methods—the reference image generation technique using an infrared imaging system in passive MMW/THz bands, the evaluation of the reconstructed images with the application-based CL metric, and contrast enhancement techniques for single-band or multi-band imaging methods—holds the potential for the development of innovative techniques. These advancements may contribute to the creation of new applications within the passive MMW/THz bands, particularly focusing on the improvement of detection methods in the future. [ABSTRACT FROM AUTHOR]

Sensor limitations in capturing devices and environmental factors can result in radiance artifacts in rendered images. This paper presents an entropy-driven exposure interpolation framework in the context of large exposure-ratio fusion. The proposed framework generates intermediate exposure-corrected images through transmission map estimation to obtain initial radiance and illumination maps. Fusion weight maps, within a pyramidal framework, are derived from the transmission map and spatial entropy, thereby enhancing the visual quality of images while preventing color artifacts. Experiments demonstrate that the proposed framework outperforms several state-of-the-art multi-exposure fusion schemes. [ABSTRACT FROM AUTHOR]

One of the most challenging, interesting, and influential areas in image processing is image enhancement. Image enhancement techniques manipulate the existing image so as to ameliorate the quality as well as the visual appearance of the image to the viewer. Different types of image enhancement methods are utilized to tackle the complex problems of image visualization in medical imaging. Many imaging techniques are available, such as CT scans, magnetic resonance imaging, X-rays, and others. MRI is a kind of scan that uses strong magnetic fields and radio waves to capture images of the internal structure of the patient's body. Medical imaging is an exceptionally normal and fundamental medium for clinical experts to conclude illnesses with respect to unseen regions inside the body. In many situations, these images suffer from low contrast and bad illumination. To overcome these problems of low contrast and poor illumination, this paper presents an enhancement scheme using a modified type-2 fuzzy set for MRI images. The results of the proposed scheme are shown in terms of both qualitative and quantitative analysis. All the experiments are carried out for a fixed value of a parameter β = 0.7 . For qualitative analysis, results are visualized with state-of-the-art methods and for quantitative analysis, PSNR, SSIM, AMBE, REC and PL are used. Qualitative and quantitative analysis bear witness to the fact that the performance of the proposed scheme is better in many places in comparison to other existing methods. [ABSTRACT FROM AUTHOR]

Retinal imaging often falls short in image quality due to limitations in imaging conditions. Issues such as low contrast and inadequate brightness are frequently encountered. However, fundus pictures play a crucial role in diagnosing various retinal diseases within the field of ophthalmology. Nonetheless, specific ocular abnormalities and capturing environments result in low-grade fundus images, hampering the diagnostic abilities of both human experts and machines. Analyzing color fundus images to detect retinal abnormalities necessitates enhanced representation of image properties, including contrast, illumination, and precise edge points. The proposed method introduces a new technique for improving color fundus photos. The algorithm comprises three stages. Firstly, a feed-forward denoising convolutional neural network (DnCNN) removes noise. Subsequently, a contrast enhancement method, recursive separated weighted histogram equalization (RSWHE), addresses low contrast issues. Finally, adaptive Gamma correction (AGC) improves uneven luminosity. Experiments were conducted using the STARE benchmark datasets to evaluate the algorithm. The suggested algorithm's output is equated against state-of-the-art enhancement methods. Objective validation was performed using performance parameters such as NIQE, PCQI, CEIQ, MEME, and PSNR. It suggests that the algorithm has the potential to serve as an efficient method for enhancing retinal images, thereby improving diagnostic capabilities in the field of ophthalmology. [ABSTRACT FROM AUTHOR]

Background: Adequate image enhancement of organs and blood vessels of interest is an important aspect of image quality in contrast‐enhanced computed tomography (CT). There is a need for an objective method for evaluation of vessel contrast that can be automatically and systematically applied to large sets of CT exams. Purpose: The purpose of this work was to develop a method to automatically segment and measure attenuation Hounsfield Unit (HU) in the portal vein (PV) in contrast‐enhanced abdomen CT examinations. Methods: Input CT images were processed by a vessel enhancing filter to determine candidate PV segmentations. Multiple machine learning (ML) classifiers were evaluated for classifying a segmentation as corresponding to the PV based on segmentation shape, location, and intensity features. A public data set of 82 contrast‐enhanced abdomen CT examinations was used to train the method. An optimal ML classifier was selected by training and tuning on 66 out of the 82 exams (80% training split) in the public data set. The method was evaluated in terms of segmentation classification accuracy and PV attenuation measurement accuracy, compared to manually determined ground truth, on a test set of the remaining 16 exams (20% test split) held out from public data set. The method was further evaluated on a separate, independently collected test set of 21 examinations. Results: The best classifier was found to be a random forest, with a precision of 0.892 in the held‐out test set to correctly identify the PV from among the input candidate segmentations. The mean absolute error of the measured PV attenuation relative to ground truth manual measurement was 13.4 HU. On the independent test set, the overall precision decreased to 0.684. However, the PV attenuation measurement remained relatively accurate with a mean absolute error of 15.2 HU. Conclusions: The method was shown to accurately measure PV attenuation over a large range of attenuation values, and was validated in an independently collected dataset. The method did not require time‐consuming manual contouring to supervise training. The method may be applied to systematic quality control of contrast‐enhanced CT examinations. [ABSTRACT FROM AUTHOR]

Background: The best settings of deep learning image reconstruction (DLIR) algorithm for abdominal low-kiloelectron volt (keV) virtual monoenergetic imaging (VMI) have not been determined. Purpose: To determine the optimal settings of the DLIR algorithm for abdominal low-keV VMI. Material and Methods: The portal-venous phase computed tomography (CT) scans of 109 participants with 152 lesions were reconstructed into four image series: VMI at 50 keV using adaptive statistical iterative reconstruction (Asir-V) at 50% blending (AV-50); and VMI at 40 keV using AV-50 and DLIR at medium (DLIR-M) and high strength (DLIR-H). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of nine anatomical sites were calculated. Noise power spectrum (NPS) using homogenous region of liver, and edge rise slope (ERS) at five edges were measured. Five radiologists rated image quality and diagnostic acceptability, and evaluated the lesion conspicuity. Results: The SNR and CNR values, and noise and noise peak in NPS measurements, were significantly lower in DLIR images than AV-50 images in all anatomical sites (all P < 0.001). The ERS values were significantly higher in 40-keV images than 50-keV images at all edges (all P < 0.001). The differences of the peak and average spatial frequency among the four reconstruction algorithms were significant but relatively small. The 40-keV images were rated higher with DLIR-M than DLIR-H for diagnostic acceptance (P < 0.001) and lesion conspicuity (P = 0.010). Conclusion: DLIR provides lower noise, higher sharpness, and more natural texture to allow 40 keV to be a new standard for routine VMI reconstruction for the abdomen and DLIR-M gains higher diagnostic acceptance and lesion conspicuity rating than DLIR-H. [ABSTRACT FROM AUTHOR]

Digital radiography in medicine is a widely used imaging method for obtaining visual information about the inside of a body. To prepare the acquired raw image for diagnostic evaluation, the contrast must be adjusted depending on the examined part of the body and the reason of acquisition. The contrast enhancement of an image can be considered as a style transfer or an image-to-image translation which is an important field in deep learning. Based on common methods like the pix2pix network that only translate from one domain into one other, we propose a method (cc-pix2pix) for translating into multiple domains in one training. We provide additional information about the examination to the network for a specific contrast adjustment. Compared to the pix2pix network, the ccpix2pix reduces the mean squared error by a factor of six and achieves an improvement of approximately seven percentage points based on the histogram intersection of source and target images. [ABSTRACT FROM AUTHOR]

Human gait is an essential biometric feature in the area of computer vision research. Over the past ten years, there has been a growing demand for a non-contact biometric approach to identify potential candidates, mainly since the global COVID-19 epidemic emerged. Gait recognition involves automatically capturing and extracting characteristics of human movement, which are subsequently utilized to verify the identity of a moving individual. Nevertheless, covariates like walking while carrying a bag, changing clothes, environmental conditions, and any unusual gait patterns all have an impact on the accuracy of gait recognition accuracy. This paper presents a new end-to-end deep learning framework for human gait recognition. The proposed framework contains a few important steps that help in the improvement of the recognition accuracy. A contrast enhancement technique named Enhancing Human Body Shape and Reducing Noise is proposed at the initial step and used for the dataset augmentation. The second step involves deep learning architecture development, such as the proposed GNET-23 model and a fine-tuned pre-trained AlexNet model. Both models are trained on selected datasets and later extract deep features from the average pooling layer. A novel parallel correlation fusion technique is proposed to fuse the richer information of both models that are further optimized using an improved Satin Bowerbird optimization algorithm. Finally, the most optimal features are classified using Neural Networks and nearest-neighbor classifiers. The experiment was conducted using four different angles of publicly accessible CASIA-B datasets, resulting in mean accuracy scores of 91.6%, 96.2%, 94.3%, and 96.8%, respectively. The proposed framework surpasses other deep learning networks and recently published techniques in both accuracy and processing speed. [ABSTRACT FROM AUTHOR]

Background and objectives: Contrast enhancement in glioblastoma, IDH-wildtype is common but not systematic. In the era of the WHO 2021 Classification of CNS Tumors, the prognostic impact of a contrast enhancement and the pattern of contrast enhancement is not clearly elucidated. Methods: We performed an observational, retrospective, single-centre cohort study at a tertiary neurosurgical oncology centre (January 2006 - December 2022). We screened adult patients with a newly-diagnosed glioblastoma, IDH-wildtype in order to assess the prognosis role of the contrast enhancement and the pattern of contrast enhancement. Results: We included 1149 glioblastomas, IDH-wildtype: 26 (2.3%) had a no contrast enhancement, 45 (4.0%) had a faint and patchy contrast enhancement, 118 (10.5%) had a nodular contrast enhancement, and 960 (85.5%) had a ring-like contrast enhancement. Overall survival was longer in non-contrast enhanced glioblastomas (26.7 months) than in contrast enhanced glioblastomas (10.9 months) (p < 0.001). In contrast enhanced glioblastomas, a ring-like pattern was associated with shorter overall survival than in faint and patchy and nodular patterns (10.0 months versus 13.0 months, respectively) (p = 0.033). Whatever the presence of a contrast enhancement and the pattern of contrast enhancement, surgical resection was an independent predictor of longer overall survival, while age ≥ 70 years, preoperative KPS score < 70, tumour volume ≥ 30cm3, and postoperative residual contrast enhancement were independent predictors of shorter overall survival. Conclusion: A contrast enhancement is present in the majority (97.7%) of glioblastomas, IDH-wildtype and, regardless of the pattern, is associated with a shorter overall survival. The ring-like pattern of contrast enhancement is typical in glioblastomas, IDH-wildtype (85.5%) and remains an independent predictor of shorter overall survival compared to other patterns (faint and patchy and nodular). [ABSTRACT FROM AUTHOR]

Aim. To evaluate the possibilities of post-processing of multidetector computed tomography (CT) results in the non-invasive diagnosis of pancreatic fibrosis (PF). Materials and methods. The study included 165 patients aged 57.91±13.5 years who underwent preoperative CT during surgical treatment for chronic pancreatitis and pancreatic cancer from April 2022 to February 2024. The normalized contrast ratios of pancreatic tissue in the pancreatic (NCPP) and venous (NCVP) phases, as well as the contrast ratio (CR) were measured. Pathomorphological assessment of PF performed in tissues outside neoplasm or desmoplastic reaction by the Kloppel and Maillet scale. Results. The values of post-processing CT results were compared in groups with different degrees of PF. Mean CR values were significantly higher (p=0.001) in patients with severe PF (CR 1.16±0.65 HU) than in patients with mild PF (CR 0.78±0.31 HU). CR value significant increase (p=0.03) was found in patients with signs of inflammatory changes in the pancreas tissue (CR 1.14±0.6 HU) than in those without them (CR 0.81±0.3 HU). There were no significant differences between the values of NCPP and NCVP, and the degree of PF. Conclusion. The CR value increased in patients with severe degree of PF. There was a relationship between CR value increase and the radiological density of pancreatic tissue in non-contrast phase and presence of early signs of pancreatic inflammatory changes. Thus, there was a relationship between CT postprocessing results and morphological signs of PF, which can be used for pancreatic fibrosis non-invasive diagnosis and identification of additional signs of early chronic pancreatitis.

Image enhancement is a technique for improving the quality of an image so that it can be viewed by both humans and machines. The primary purpose of picture contrast enhancement is to improve the image's visual quality. Histogram equalisation (HE) is one way to increase contrast. One disadvantage of He is that it does not sustain brightness while increasing contrast since a sdden mean shift occurs during the equalisation process. A new image enhancement method is established with weighted histogram equalisation with Oppositional-based Customised Monarch Butterfly Optimisation (OCMBO), for better visual perception and improving image quality. The collected input image is in RGB format. This image is then converted into YCbCr format for contrast stretching. In digital image processing, the YCbCr colour space is often used to take advantage of the lower resolution capability of the human visual system for colour concerning luminosity. The weighted histogram equalisation with Oppositional-based Customised Monarch Butterfly Optimisation (OCMBO) is applied to the converted image's Y component, and then the Cr, Cb and modified Y components are combined back into RGB format. [ABSTRACT FROM AUTHOR]

Integrating fractional calculus into image processing techniques offers a useful and robust approach. In this study, we proposed contrast enhancement filters using Prabhakar fractional integral operator based on Grunwald–Letnikov and forward Euler. We evaluated the performance of the proposed enhancement methods on both high and low contrast images and compared them with fractional and non-fractional contrast enhancement methods. To demonstrate the superiority of our methods, we employed five different image quality metrics: PSNR, MSE, SSIM, FSIM, and entropy. For low contrast images, our methods not only achieved acceptable results for each metric—PSNR values above 25, SSIM values above 0.9, MSE values below 200, FSIM values above 0.97, and entropy values above 7—but also demonstrated better performance compared to other methods. In high contrast images, despite an overall decline in metric scores, the Grunwald–Letnikov based method remains the leading approach among both fractional and non-fractional methods. Additionally, empirical results provide evidence that the proposed methods are more effective in enhancing low contrast images compared to high contrast images. [ABSTRACT FROM AUTHOR]

Due to the complex underwater environment and the selective absorption and scattering effect of water on light waves, underwater images often suffer from issues such as low contrast, color distortion, and blurred details. This paper presents a stable and effective algorithm for enhancing underwater images to address these challenges. Firstly, an improved color correction algorithm based on the gray world and minimum information loss is employed to remove the blue-green bias present in the images. Secondly, a contrast enhancement algorithm is based on the guided filter and wavelet decomposition to make the texture details of the image clearer. Then, the normalized weight map of the image is obtained to carry out multi-scale fusion. Finally, the fused image is applied to perform the multi-scale decomposition. The experimental results show that the algorithm proposed in this paper can correct the image color deviation, improve the image contrast and enhance the image details. [ABSTRACT FROM AUTHOR]

Background: The widely used magnetization‐prepared rapid gradient‐echo (MPRAGE) sequence makes enhancing lesions and blood vessels appear bright after gadolinium administration. However, dark blood imaging using T1‐weighted Sampling Perfection with Application optimized Contrast using different flip angle Evolution (T1 SPACE) can be advantageous since it improves the conspicuity of small metastases and leptomeningeal disease. As a potential alternative to T1 SPACE, we evaluated a new dark blood sequence called echo‐uT1RESS (unbalanced T1 Relaxation‐Enhanced Steady‐State). Purpose: We compared the performance of echo‐uT1RESS with Dixon fid‐uT1RESS, MPRAGE, and T1 SPACE. Study Type: Retrospective, IRB approved. Subjects/Phantom: Phantom to assess flow properties of echo‐uT1RESS. Twenty‐one patients (14 female, age range 35–82 years) with primary and secondary brain tumors. Field Strength/Sequences: 3 Tesla/MPRAGE, T1 SPACE, Dixon fid‐uT1RESS, echo‐uT1RESS. Assessment: Flow phantom signal vs. velocity as a function of flip angle and sequence. Qualitative image assessment on 4‐point scale. Quantitative evaluation of tumor‐to‐brain contrast, apparent contrast‐to‐noise ratio (aCNR), and vessel‐to‐brain aCNR. Statistical Tests: Friedman and Mann–Whitney U tests. A P value <0.05 was considered statistically significant. Results: In the phantom, echo‐uT1RESS showed greater flow‐dependent signal loss than fid‐uT1RESS. In patients, blood vessels appeared bright with MPRAGE, gray with fid‐uT1RESS, and dark with T1 SPACE and echo‐uT1RESS. For MPRAGE, Dixon fid‐uT1RESS, echo‐uT1RESS, and T1 SPACE, respective tumor‐to‐brain contrast values were 0.6 ± 0.3, 1.3 ± 0.5, 1.0 ± 0.4, and 0.6 ± 0.4, while normalized aCNR values were 68.9 ± 50.9, 128.4 ± 59.2, 74.2 ± 42.1, and 99.4 ± 73.9. Data Conclusion: Volumetric dark blood contrast‐enhanced brain MRI is feasible using echo‐uT1RESS. The dark blood effect was improved vs. fid‐uT1RESS, while both uT1RESS versions provided better tumor‐to‐brain contrast than MPRAGE. Whereas T1 SPACE provided better tumor aSNR, echo‐uT1RESS provided better Weber contrast, lesion sharpness and a more consistent dark blood effect. Evidence Level: 3 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

Image contrast enhancement is a crucial computer vision step aiming to improve the quality of the visual information in processed images. In the literature, several proposed methods for image contrast enhancement are Histogram Equalization-based (HE) techniques that use one transformation function and optimize its parameters for mapping the pixels to new gray-intensity values. However, using only one transformation function would leave other enhancement options unexplored. Therefore, the proposed approach generates several transformation functions and selects the one that best improves the image's contrast. This method is based on the Differential Evolution (DE) algorithm, which produces multiple candidate solutions representing transformation functions. The transformation functions map the input pixel values in their enhanced versions to equalize the histogram and improve the image's contrast. Furthermore, a new formulation is proposed as the objective function based on the number of edge pixels, the intensity of the pixels, image entropy, and the number of gray intensity levels. The performance of this approach has been tested on low-contrast dataset images and compared to similar HE techniques, such as AVHEQ, BBHE, RSESIHE, MMBEBHE, and ESIHE. The results demonstrate the proposed algorithm's robustness and high performance in improving the grayscale images' contrast. [ABSTRACT FROM AUTHOR]

Background and Purpose: We aimed to test whether synthetic T1‐weighted imaging derived from a post‐contrast Quantitative Transient‐state Imaging (QTI) acquisition enabled revealing pathological contrast enhancement in intracranial lesions. Methods: The analysis included 141 patients who underwent a 3 Tesla‐MRI brain exam with intravenous contrast media administration, with the post‐contrast acquisition protocol comprising a three‐dimensional fast spoiled gradient echo (FSPGR) sequence and a QTI acquisition. Synthetic T1‐weighted images were generated from QTI‐derived quantitative maps of relaxation times and proton density. Two neuroradiologists assessed synthetic and conventional post‐contrast T1‐weighted images for the presence and pattern of pathological contrast enhancement in intracranial lesions. Enhancement volumes were quantitatively compared. Results: Using conventional imaging as a reference, synthetic T1‐weighted imaging was 93% sensitive in revealing the presence of contrast enhancing lesions. The agreement for the presence/absence of contrast enhancement was almost perfect both between readers (k = 1 for both conventional and synthetic imaging) and between sequences (k = 0.98 for both readers). In 91% of lesions, synthetic T1‐weighted imaging showed the same pattern of contrast enhancement visible in conventional imaging. Differences in enhancement pattern in the remaining lesions can be due to the lower spatial resolution and the longer acquisition delay from contrast media administration of QTI compared to FSPGR. Overall, enhancement volumes appeared larger in synthetic imaging. Conclusions: QTI‐derived post‐contrast synthetic T1‐weighted imaging captures pathological contrast enhancement in most intracranial enhancing lesions. Further comparative studies employing quantitative imaging with higher spatial resolution is needed to support our data and explore possible future applications in clinical trials. [ABSTRACT FROM AUTHOR]

Background: Low-iodine-dose computed tomography (CT) protocols have emerged to mitigate the risks associated with contrast injection, often resulting in decreased image quality. Objective: To evaluate the image quality of low-iodine-dose CT combined with an artificial intelligence (AI)-based contrast-boosting technique in abdominal CT, compared to a standard-iodine-dose protocol in children. Materials and methods: This single-center retrospective study included 35 pediatric patients (mean age 9.2 years, range 1–17 years) who underwent sequential abdominal CT scans—one with a standard-iodine-dose protocol (standard-dose group, Iobitridol 350 mgI/mL) and another with a low-iodine-dose protocol (low-dose group, Iohexol 240 mgI/mL)—within a 4-month interval from January 2022 to July 2022. The low-iodine CT protocol was reconstructed using an AI-based contrast-boosting technique (contrast-boosted group). Quantitative and qualitative parameters were measured in the three groups. For qualitative parameters, interobserver agreement was assessed using the intraclass correlation coefficient, and mean values were employed for subsequent analyses. For quantitative analysis of the three groups, repeated measures one-way analysis of variance with post hoc pairwise analysis was used. For qualitative analysis, the Friedman test followed by post hoc pairwise analysis was used. Paired t-tests were employed to compare radiation dose and iodine uptake between the standard- and low-dose groups. Results: The standard-dose group exhibited higher attenuation, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) of organs and vessels compared to the low-dose group (all P-values < 0.05 except for liver SNR, P = 0.12). However, noise levels did not differ between the standard- and low-dose groups (P = 0.86). The contrast-boosted group had increased attenuation, CNR, and SNR of organs and vessels, and reduced noise compared with the low-dose group (all P < 0.05). The contrast-boosted group showed no differences in attenuation, CNR, and SNR of organs and vessels (all P > 0.05), and lower noise (P = 0.002), than the standard-dose group. In qualitative analysis, the contrast-boosted group did not differ regarding vessel enhancement and lesion conspicuity (P > 0.05) but had lower noise (P < 0.05) and higher organ enhancement and artifacts (all P < 0.05) than the standard-dose group. While iodine uptake was significantly reduced in low-iodine-dose CT (P < 0.001), there was no difference in radiation dose between standard- and low-iodine-dose CT (all P > 0.05). Conclusion: Low-iodine-dose abdominal CT, combined with an AI-based contrast-boosting technique exhibited comparable organ and vessel enhancement, as well as lesion conspicuity compared to standard-iodine-dose CT in children. Moreover, image noise decreased in the contrast-boosted group, albeit with an increase in artifacts. [ABSTRACT FROM AUTHOR]

The implications of shorter scan time and lower tube voltage in the dual-source CT coronary angiography (CTCA) scan protocol necessitate the adaptation of contrast media (CM) injection parameters. This audit evaluates the coronary arteries' vascular attenuation and image quality by comparing the personalised patient protocol technology (P3T) contrast injection software with standard injection protocol. The secondary aim is to determine the relationship between CM volume and the patient's weight. A Siemens Somatom Definition Force CT Unit was used to scan 30 sets of patients between August 2020 and October 2020. Patients were selected retrospectively and separated into Standard Injection and P3T injection protocols. An experienced radiologist blinded to the groups reviewed the coronary vessels' contrast enhancement and image quality. Overall, the mean HU of all the main coronary artery vessels obtained from P3T injection software reached above 350 HU and was diagnostically sufficient. The mean attenuation at the proximal region of RCA in the 80–99 kg weight category was significantly higher in the P3T injection software than the standard injection protocol (p < 0.001). The CM volume proposed by P3T injection software for 40–59 kg was approximately 57 ± 5 mls, while 75 ml was used for the standard injection protocol. P3T injection software in CTCA resulted in an adequate diagnostic attenuation of coronary arteries (>350HU) in all weight groups, most effectively in the higher weight group, while maintaining diagnostic image quality. Further, the P3T software reduces CM volumes in lower-weight patients. P3T software enables reducing CM volume in lower-weight patients while improving vascular enhancement in CTCA scans in higher-weight patients. [ABSTRACT FROM AUTHOR]

In the autonomous driving industry, there is a growing trend to employ long-wave infrared (LWIR)-based uncooled thermal-imaging cameras, capable of robustly collecting data even in extreme environments. Consequently, both industry and academia are actively researching contrast-enhancement techniques to improve the quality of LWIR-based thermal-imaging cameras. However, most research results only showcase experimental outcomes using mass-produced products that already incorporate contrast-enhancement techniques. Put differently, there is a lack of experimental data on contrast enhancement post-non-uniformity (NUC) and temperature compensation (TC) processes, which generate the images seen in the final products. To bridge this gap, we propose a histogram equalization (HE)-based contrast enhancement method that incorporates a region-based clipping technique. Furthermore, we present experimental results on the images obtained after applying NUC and TC processes. We simultaneously conducted visual and qualitative performance evaluations on images acquired after NUC and TC processes. In the visual evaluation, it was confirmed that the proposed method improves image clarity and contrast ratio compared to conventional HE-based methods, even in challenging driving scenarios such as tunnels. In the qualitative evaluation, the proposed method demonstrated upper-middle-class rankings in both image quality and processing speed metrics. Therefore, our proposed method proves to be effective for the essential contrast enhancement process in LWIR-based uncooled thermal-imaging cameras intended for autonomous driving platforms. [ABSTRACT FROM AUTHOR]

Low-light images are challenging for both human observation and computer vision algorithms due to low visibility. To address this issue, various image enhancement techniques such as dehazing, histogram equalization, and neural network-based methods have been proposed. However, most existing methods often suffer from the problems of insufficient contrast and over-enhancement while enhancing the brightness, which not only affects the visual quality of images but also adversely impacts their subsequent analysis and processing. To tackle these problems, this paper proposes a low-light image enhancement method called LEFB. Specifically, the low-light image is first transformed into the LAB color space, and the L channel controlling brightness is enhanced using a local contrast enhancement algorithm. Then, the enhanced image is further enhanced using an exposure fusion-based contrast enhancement algorithm, and finally, a bilateral filtering function is applied to reduce image edge blurriness. Experimental evaluations are conducted on real datasets with four comparison algorithms. The results demonstrate that the proposed method has superior performance in enhancing low-light images, effectively addressing problems of insufficient contrast and over-enhancement, while preserving fine details and texture information, resulting in more natural and realistic enhanced images. [ABSTRACT FROM AUTHOR]

Introduction: Many spine disorders are caused by disc degeneration or endplate defects. Because nutrients entering the avascular disc are channeled through the cartilaginous endplate (CEP), structural and compositional changes in the CEP may block this solute channel, thereby hindering disc cell function. Therefore, imaging the CEP region is important to improve the diagnostic accuracy of spine disorders. Methods: A clinically available T1-weighted and fat-suppressed spoiled gradient recalled-echo (FS-SPGR) sequence was optimized for high-contrast CEP imaging, which utilizes the short T1 property of the CEP. The FS-SPGR scans with and without breath-hold were performed for comparison on healthy subjects. Then, the FS-SPGR sequence which produced optimal image quality was employed for patient scans. In this study, seven asymptomatic volunteers and eight patients with lower back pain were recruited and scanned on a 3T wholebody MRI scanner. Clinical T2-weighted fast spin-echo (T2w-FSE) and T1- weighted FSE (T1w-FSE) sequences were also scanned for comparison. Results: For the asymptomatic volunteers, the FS-SPGR scans under free breathing conditions with NEX = 4 showed much higher contrast-to-noise ratio values between the CEP and bone marrow fat (BMF) (CNRCEP-BMF) (i.e., 7.8 ± 1.6) and between the CEP and nucleus pulposus (NP) (CNRCEP-NP) (i.e., 6.1 ± 1.2) compared to free breathing with NEX = 1 (CNRCEP-BMF: 4.0 ± 1.1 and CNRCEP-NP: 2.5 ± 0.9) and breath-hold condition with NEX = 1 (CNRCEP-BMF: 4.2 ± 1.3 and CNRCEP-NP: 2.8 ± 1.3). The CEP regions showed bright linear signals with high contrast in the T1-weighted FS-SPGR images in the controls, while irregularities of the CEP were found in the patients. Discussion: We have developed a T1-weighted 3D FS-SPGR sequence to image the CEP that is readily translatable to clinical settings. The proposed sequence can be used to highlight the CEP region and shows promise for the detection of intervertebral disc abnormalities. [ABSTRACT FROM AUTHOR]

Metadata is a key data source for researchers seeking to apply machine learning (ML) to the vast collections of digitized biological specimens that can be found online. Unfortunately, the associated metadata is often sparse and, at times, erroneous. This paper extends previous research conducted with the Illinois Natural History Survey (INHS) collection (7244 specimen images) that uses computational approaches to analyze image quality, and then automatically generates 22 metadata properties representing the image quality and morphological features of the specimens. In the research reported here, we demonstrate the extension of our initial work to University of the Wisconsin Zoological Museum (UWZM) collection (4155 specimen images). Further, we enhance our computational methods in four ways: (1) augmenting the training set, (2) applying contrast enhancement, (3) upscaling small objects, and (4) refining our processing logic. Together these new methods improved our overall error rates from 4.6 to 1.1%. These enhancements also allowed us to compute an additional set of 17 image-based metadata properties. The new metadata properties provide supplemental features and information that may also be used to analyze and classify the fish specimens. Examples of these new features include convex area, eccentricity, perimeter, skew, etc. The newly refined process further outperforms humans in terms of time and labor cost, as well as accuracy, providing a novel solution for leveraging digitized specimens with ML. This research demonstrates the ability of computational methods to enhance the digital library services associated with the tens of thousands of digitized specimens stored in open-access repositories world-wide by generating accurate and valuable metadata for those repositories. [ABSTRACT FROM AUTHOR]

Contrast Enhanced Computed Tomography (CECT) abdomen and pelvis is a common imaging procedure. Hospitals typically follow fixed protocols of contrast volume administration for triple-phase CECT abdomen and pelvis scans and have found that patients are either underdosed or overdosed with respect to their body habitus. The aim of the study was to correlate different patient characteristics such as Total body weight (TBW), Lean Body Mass (LBM), Body Mass Index (BMI), Body Surface Area (BSA) and Blood Volume (BV) with aortic enhancement in the arterial and portal venous phases for CECT Abdomen and pelvis. A total of 106 patients who underwent triple-phase CECT abdomen & pelvis were retrospectively studied. A circular region-of-interest (ROI) of 100 mm2 was positioned on descending aorta for unenhanced, arterial, and portal venous phases to measure the aortic enhancement in Hounsfield's units. Measure of contrast attenuation (ΔH) was calculated from the difference of CT values on unenhanced images and contrast images. Correlation analysis was performed to evaluate the relation of patient body characteristics with aortic enhancement. Correlation analysis revealed that BMI exhibited the least correlation when compared to the other characteristics in both arterial (r = −0.3; p = 0.002) and portovenous phases (r = −0.35; p < 0.001) whereas TBW, LBW, BSA and BV reported moderate inverse correlations. BV was found to be the strongest of all characteristics under linear regression. The study supports the use of protocols that adjust contrast volume to either TBW, LBW, BSA, or BV for CT abdomen and pelvis scan. The right body parameter ensures optimal contrast enhancement, improving the visualization of anatomical structures and helps in adapting tailored contrast injection protocols. [ABSTRACT FROM AUTHOR]

The nitrogen−vacancy (NV) center in diamond gains its versatility when negatively charged (NV−) but is mediocre when neutrally charged. Particularly, the charge states of NV centers are convertible under optical pumping and during the dark intervals, whose dynamics are mixed with the NV−s' spin polarizations and relaxations, making them difficult to detect. Here, a microwave‐pulses‐assisted charge state dynamics (CSD) measurement method of NV centers in the dark time (DT) is proposed. The microwave pulses are designed to manipulate the populations of the NV−s' ground state spin triplets (qutrit) to the equilibrium state before the DT. Thus, the longitudinal relaxations of the qutrit are balanced, and pure CSD can be detected. Interestingly, in an annealed bulk diamond, not only the traditional tunneling‐induced fast exponential CSD are observed, but also a slow and long‐term recharging process, which is probably attributed to the exchanging of the electrons between NV centers and the high‐energy‐level charge traps such as vacancy clusters. Furthermore, results demonstrate a 40% increase in NV−s' contrast by properly extending the recharging DT. These results are significant for the in‐depth study of the NV centers' CSD and can improve the sensing abilities of the NV− ensemble. [ABSTRACT FROM AUTHOR]

Most of the widely used contrast enhancement methods are based on the grey level/intensity histogram of the image, as these methods are simple and easy to understand. Due to their dependency only on the frequency of grey levels, histogram-based methods generally have less time complexity and are easy to implement. The dependency only on the frequency of grey level may cause the over enhancement in the extreme grey levels/intensity regions (dark and bright regions), and increase the noise and artifacts in these regions. Also, highly frequent grey levels are most influential in the histogram-based contrast enhancement methods and hence cause over-enhancement. To deal with these drawbacks we suggest a new idea based on the entropy curve of the image that uses the complete information associated with each grey level/intensity level instead of depending on only the frequency of the grey levels. Also, a clipping criteria is applied on the entropy curve to reduce the weightage of the highly frequent grey levels, which helps to reduce the over-enhancement. A comprehensive qualitative and quantitative analysis, where quantitative analysis is performed using SSIM, GMSD, VSI, and PSNR parameters, shows that the performance of the proposed method is better than most of the existing contrast-enhancement tools. It produces natural-looking, high-contrast images with minimal artifacts. [ABSTRACT FROM AUTHOR]

Image contrast enhancement or boosting is normally referred to as one of the most crucial tasks in image processing, and histogram equalization (HE) is one of the most pervasive methods applied to address this task. HE and its variants have been proven a simple and effective technique. However, no one consistent image quality evaluation standard has been built for them, not to say other relevant approaches. In other words, it is lack of enough attention to image quality evaluation for contrast enhancement algorithms. The authors proposed a novel evaluation standard combining Gini-index and variation coefficient. They verified the effectiveness of the proposed evaluation standard especially for double plateaus histogram equalization (DPHE) algorithm. Their experimental results showed that when H and PSNR cannot clearly describe the image quality, the proposed objective standard could provide an additional objective basis for the quality evaluation of DPHE, which may be extended to pervasive image enhancement algorithms. [ABSTRACT FROM AUTHOR]

Adaptive Histogram Equalization (AHE) and its contrast-limited variant CLAHE are well-known and effective methods for improving the local contrast in an image. However, the fastest available implementations scale linearly with the filter mask size, which results in high execution times. This presents an obstacle in real-world applications, where large filter mask sizes are desired while maintaining low execution times. In this work, we propose an efficient algorithm for AHE that reduces the per-pixel computational complexity to O (1) . To the best of our knowledge, this is the first time that a constant-time algorithm is proposed for AHE and CLAHE. In contrast to commonly used fast implementations, our method computes the exact result for each pixel without interpolation artifacts. We benchmark and compare our method to existing algorithms. Our experiments show that our method exhibits superior execution times independent of the filter mask size, which makes AHE and CLAHE fast enough to be usable in real-world applications. [ABSTRACT FROM AUTHOR]

PURPOSES: To provide abdominal contrast-enhanced MR image synthesis, we developed an gradient regularized multi-modal multi-discrimination sparse attention fusion generative adversarial network (GRMM-GAN) to avoid repeated contrast injections to patients and facilitate adaptive monitoring. METHODS: With IRB approval, 165 abdominal MR studies from 61 liver cancer patients were retrospectively solicited from our institutional database. Each study included T2, T1 pre-contrast (T1pre), and T1 contrast-enhanced (T1ce) images. The GRMM-GAN synthesis pipeline consists of a sparse attention fusion network, an image gradient regularizer (GR), and a generative adversarial network with multi-discrimination. The studies were randomly divided into 115 for training, 20 for validation, and 30 for testing. The two pre-contrast MR modalities, T2 and T1pre images, were adopted as inputs in the training phase. The T1ce image at the portal venous phase was used as an output. The synthesized T1ce images were compared with the ground truth T1ce images. The evaluation metrics include peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE). A Turing test and experts contours evaluated the image synthesis quality. RESULTS: The proposed GRMM-GAN model achieved a PSNR of 28.56, an SSIM of 0.869, and an MSE of 83.27. The proposed model showed statistically significant improvements in all metrics tested with p-values < 0.05 over the state-of-the-art model comparisons. The average Turing test score was 52.33%, which is close to random guessing, supporting the models effectiveness for clinical application. In the tumor-specific region analysis, the average tumor contrast-to-noise ratio (CNR) of the synthesized MR images was not statistically significant from the real MR images. The average DICE from real vs. synthetic images was 0.90 compared to the inter-operator DICE of 0.91. CONCLUSION: We demonstrated the function of a novel multi-modal MR image synthesis neural network GRMM-GAN for T1ce MR synthesis based on pre-contrast T1 and T2 MR images. GRMM-GAN shows promise for avoiding repeated contrast injections during radiation therapy treatment.

For a better understanding of living tissues and materials, it is essential to study the intricate spatial relationship between cells and their surrounding tissue on the nanoscale, with a need for 3D, high‐resolution imaging techniques. In the case of bone, focused ion beam‐scanning electron microscopy (FIB‐SEM) operated in the backscattered electron (BSE) mode proves to be a suitable method to image mineralized areas with a nominal resolution of 5 nm. However, as clinically relevant samples are often resin‐embedded, the lack of atomic number (Z) contrast makes it difficult to distinguish the embedding material from unmineralized parts of the tissue, such as osteoid, in BSE images. Staining embedded samples with iodine vapor has been shown to be effective in revealing osteoid microstructure by 2D BSE imaging. Based on this idea, an iodine (Z = 53) staining protocol is developed for 3D imaging with FIB‐SEM, investigating how the amount of iodine and exposure time influences the imaging outcome. Bone samples stained with this protocol also remain compatible with confocal laser scanning microscopy to visualize the lacunocanalicular network. The proposed protocol can be applied for 3D imaging of tissues exhibiting mineralized and nonmineralized regions to study physiological and pathological biomineralization.