Image Quality Index Research Articles

An optimal system for increasing the contrast resolution qualities of histopathology images in the wavelet domain

AbstractIn the field of histopathology, it has been observed that most images are of poor quality as a result of inadequate luminance and contrast. The first step in identifying and analyzing them is to enhance their quality while maintaining their inherent characteristics. This paper presents a novel method for enhancing histopathology images' important features, brightness preservation, and contrast by utilizing the adaptive intensity transformation gamma function in the wavelet domain. The RGB histopathological color image is first converted to the HSV color model. The V component is then stretched in order to compensate for any color distortion. In the proposed scheme, the stretched V component is split into four sub‐bands using discrete wavelet transform (DWT). Next, it determines the optimal value of the gamma correction function in the DWT lower sub‐band. A correction factor is applied using the singular value decomposition (SVD) technique to determine the optimal gamma function value using the nature‐inspired particle swarm optimization method (NIPSO). The corrected LL sub‐band is then applied to the inverse DWT (IDWT) together with the other unprocessed sub‐bands. In addition, the contrast of each feature is enhanced by employing an adaptive histogram equalization model that is contrast‐limited. The proposed method was evaluated through experiments and validations on a variety of histopathology images retrieved from multiple databases. Several quantitative studies have demonstrated that the proposed method outperforms existing enhancement techniques in terms of entropy, edge preservation index, contrast ratio, and Universal Image Quality Index (UIQI). This proposed technique increases contrast, while preserving the inherent characteristics of the original image, resulting in better quality histopathology images that can be used in disease diagnosis and inspection.

Harmonization of brain PET images in multi-center PET studies using Hoffman phantom scan

BackgroundImage harmonization has been proposed to minimize heterogeneity in brain PET scans acquired in multi-center studies. However, standard validated methods and software tools are lacking. Here, we assessed the performance of a framework for the harmonization of brain PET scans in a multi-center European clinical trial.MethodHoffman 3D brain phantoms were acquired in 28 PET systems and reconstructed using site-specific settings. Full Width at Half Maximum (FWHM) of the Effective Image Resolution (EIR) and harmonization kernels were estimated for each scan. The target EIR was selected as the coarsest EIR in the imaging network. Using “Hoffman 3D brain Analysis tool,” indicators of image quality were calculated before and after the harmonization: The Coefficient of Variance (COV%), Gray Matter Recovery Coefficient (GMRC), Contrast, Cold-Spot RC, and left-to-right GMRC ratio. A COV% ≤ 15% and Contrast ≥ 2.2 were set as acceptance criteria. The procedure was repeated to achieve a 6-mm target EIR in a subset of scans. The method’s robustness against typical dose-calibrator-based errors was assessed.ResultsThe EIR across systems ranged from 3.3 to 8.1 mm, and an EIR of 8 mm was selected as the target resolution. After harmonization, all scans met acceptable image quality criteria, while only 13 (39.4%) did before. The harmonization procedure resulted in lower inter-system variability indicators: Mean ± SD COV% (from 16.97 ± 6.03 to 7.86 ± 1.47%), GMRC Inter-Quartile Range (0.040–0.012), and Contrast SD (0.14–0.05). Similar results were obtained with a 6-mm FWHM target EIR. Errors of ± 10% in the DRO activity resulted in differences below 1 mm in the estimated EIR.ConclusionHarmonizing the EIR of brain PET scans significantly reduced image quality variability while minimally affecting quantitative accuracy. This method can be used prospectively for harmonizing scans to target sharper resolutions and is robust against dose-calibrator errors. Comparable image quality is attainable in brain PET multi-center studies while maintaining quantitative accuracy.

A new simpler image quality index based on body size for FDG-PET/CT.

Noise equivalent count density (NECdensity) is often used to evaluate the image quality of whole-body fluorodeoxyglucose tomography tests. However, this index is calculated using the patient volume, which is difficult to obtain at every facility. In this study, we proposed new image quality indices that can be evaluated at all facilities. In total, 94 patients were enrolled in the study. The correlations of patients' body weight and BMI with volume were examined. New image quality indices normalized by body weight and BMI were defined as NECbw and NECbmi, respectively. Correlations between NECbw, NECbmi, and NECdensity were examined. Further, the correlations between these two new indices and visual scores were evaluated. Good correlations were observed between volume and body weight (r = 0.861, P < 0.001) and between volume and BMI (r = 0.728, P < 0.001). NECbw and NECbmi correlated well with NECdensity (r = 0.954 for NECbw and r = 0.897 for NECbmi, P < 0.001). These correlations improved when the examined bed positions were set to the same number. Additionally, the correlations of visual scores with NECbw and NECbmi were similar to those between the visual score and NECdensity. Our investigation indicated that the newly proposed image quality metrics, NECbw and NECbmi, were easily calculated and as useful as NECdensity for evaluating image quality when subjects had similar physiques.

A conventional and non-conventional analysis of SAR image despeckling technique

The importance of high-resolution Synthetic Aperture Radar (SAR) imaging is undeniable in applications for Earth monitoring since it offers useful data for analysis. Post-image processing methods including edge and object detection, segmentation, and speckle noise removal are important to performed to find the information of images. The key technique that makes images aesthetically pleasing and understandable is despeckling. The presented study assays the latest trends &amp; state of the art techniques for SAR image despeckling to gauge the performance &amp; agility of existing techniques by performing a contemporary combination of theoretical &amp; experimental analysis. This study analyses various techniques used in literature. The quantitative &amp; qualitative analysis is done using Peak Signal-to-Noise Ratio (PSNR) and Universal Image Quality Index (UIQI) indices to find the better approach

A new fast and efficient dehazing and defogging algorithm for single remote sensing images

Information about the earth’s surface is difficult to capture in remote sensing images because bad weather greatly curtails visibility and diminishes visual contrast in the images. For the purpose of military survey and aerial surveillance, these images are crucial for providing information. It is quintessential to eradicate bad weather conditions like haze and fog from remote sensing images. Recently, numerous deweathering initiatives and endeavors have been undertaken to alleviate these limitations. These contemporary deweathering approaches, however, are inadequate to recover dense haze images. This work presented a novel visibility restoration approach based on segmentation and unsharp mask guided filtering method. It consists of the following steps to restore the scene’s radiance: First, a segmentation method is employed to determine atmospheric light to quantify contrast and color. Estimation of the transmission map utilizing the dark channel prior is then performed to precisely determine the proximity between objects. The resultant output image contains halo artifacts and inconsistencies in the structure. A guided filter method based on unsharp masking is employed to optimize the transmission map in order to solve this issue. The experimental results demonstrate that the proposed indicators ensures high uniformity regarding qualitative and quantitative evaluation using six performance metrics: mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), universal image quality index (UQI), fog-aware density evaluator (FADE), dehazing algorithm index (DHQI), standard deviation (CC), and blind contrast enhancement assessment (e and r).

Computed tomography dose index determination in dose modulation prospectively involving the third-generation iterative reconstruction and noise index.

Optimizing CT protocols is challenging in the presence of automatic dose modulation because the CT dose index (CTDIvol) at different patient sizes is unknown to the operator. The task is more difficult when both the image quality index and iterative reconstruction prospectively affect the dose determination. It is of interest in practice to be informed of the CTDIvol during the protocol initialization and evaluation. It was our objective to obtain a predictive relationship between CTDIvol, the image quality index, and iterative reconstruction strength at various patient sizes. Dose modulation data were collected on a GE Revolution 256-slice scanner utilizing a Mercury phantom and selections of the noise index (NI) from 8 to 17, the third generation iterative reconstruction (ASIR-V) from 0% to 80%, and phantom diameters from 16 to 36cm. The fixed parameters were 120kVp, a pitch of .984, and a collimation of 40mm with a primary slice width of 2.5mm. The CTDIvol per diameter was based on the average tube current over three adjacent slices (same or similar diameter) multiplied by a conversion factor between the average mA of the series and the reported CTDIvol. The relationship between CTDIvol, NI, and ASIR-V for each diameter was fitted with a 2nd order polynomial of ASIR-V multiplied by a power law of NI. The ASIR-V fit parameters versus diameter followed a Lorentz function while the NI exponent versus diameter followed an exponential growth function. The CTDIvol predictions were accurate within 15% compared to phantom results on a separate GE Revolution. For clinical relevance, the phantom diameter was converted to an abdomen or chest equivalent diameter and was well matched to patient data. The fitted relationship for CTDIvol. for given values of NI and ASIR-V blending for a range of phantom sizes was a good match to phantom and patient data. The results can be of direct help for selecting adequate parameters in CT protocol development.

Robust Image Steganography Based on Hybrid Edge Detection

Steganography is a technique for hiding digital messages into other digital media, such as images, audio, video, and others so that the messages can be disguised and not directly visible to the human vision. There are two kinds of steganography by domain, i.e., spatial domain and frequency domain [7]. Least Significant Bit (LSB) is a popular method in spatial domains [8]. LSB is not a new method. However, this method has many advantages, such as simple algorithm and the quality of imperceptibility of relatively good stego-image [6, 9]. This makes the LSB still open for more development and further research. This paper presents the image hiding technique using Least Significant Bit (LSB) steganography based on hybrid edge detection that maximized the data embedding capacity of the cover image. The performance of the system is evaluated for hybrid edge detection using pairs of various edge detection techniques such as Canny, Kirsch, Sobel, and Prewitt. The results of the proposed steganography approach are estimated for different payload capacities based on Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Metrics (SSIM), and Universal Image Quality Index (UQI). It is examined that the proposed approach performs better than the existing state of arts for image steganography.

Image rejects in digital skeletal radiography in two public hospitals in Norway

IntroductionThe proportion of diagnostic images not applied for diagnostic purposes is an indicator of image quality, safety, and efficiency in radiography. Despite increased awareness, image reject is still a substantial problem and needs continued observation and targeted measures. Accordingly, the objective of this study is to estimate the extent, variation, and characteristics of image rejects, in order to improve the quality, safety, and efficiency in radiography. MethodsAll skeletal images at two digital X-ray rooms at two public hospitals in Norway were reviewed for four weeks in 2020. The number of exposed images, type of examination, and number of deleted images were registered. For each deleted image the deduced reasons for deleting the image were recorded. Results2183 and 1467 X-ray images were taken at Hospital 1 and 2 respectively. The corresponding reject rates were 14.2% and 9.1%. The reject rate varied greatly from day to day (from 0% to 22%), and the examinations with the highest reject rate were X-ray of extremities (knee, elbow, ankle, wrist) (12–25%) and of the spine (14–19%). The two clearly dominating reasons for image rejects were positioning and centering errors. ConclusionThe reject rate is high and reduces quality, safety, and efficiency of imaging services. The reasons for image rejects are typical professionally reducible errors indicating great potential for improvement. Implications for practiceMonitoring and assessing image rejects are of great importance to management, training, education, patient safety, and for quality improvement of imaging services.

THE MEASURE OF TARGET SALIENCY FOR TARGET-ORIENTED IMAGE FUSION

Our analysis of the available literature on quantitative quality indices characterizing the visibility, conspicuity, and saliency of the objects in an image reveals that the used approaches do not fit the military needs, in particular those required by the target sightseeing systems of armored vehicles. Their main drawback is that these indices are not target-oriented. The long elapsed time needed for their evaluation is another inconvenience, making them inapplicable for military purposes, which in most cases require a real-time response. Special target-oriented image quality indices are needed for military applications. In this paper, we propose the very such target-oriented normalized visibility, conspicuity, and saliency indices and corresponding methods for their measurements. Comparison of the times elapsed for the generation of saliency maps by the analyzed literature methods and the adequacy of the saliency scores produced by them with those for the saliency index, proposed in this paper, reveals that the computation with our approach is several orders of magnitude faster than for the prototype methods available in the literature while providing comparable adequacy of the measured target-oriented image quality indices.

Statistical analyses of motion-corrupted MRI relaxometry data computed from multiple scans

BackgroundConsistent noise variance across data points (i.e. homoscedasticity) is required to ensure the validity of statistical analyses of MRI data conducted using linear regression methods. However, head motion leads to degradation of image quality, introducing noise heteroscedasticity into ordinary-least square analyses. New methodThe recently introduced QUIQI method restores noise homoscedasticity by means of weighted least square analyses in which the weights, specific for each dataset of an analysis, are computed from an index of motion-induced image quality degradation. QUIQI was first demonstrated in the context of brain maps of the MRI parameter R2 * , which were computed from a single set of images with variable echo time. Here, we extend this framework to quantitative maps of the MRI parameters R1, R2 * , and MTsat, computed from multiple sets of images. ResultsQUIQI restores homoscedasticity in analyses of quantitative MRI data computed from multiple scans. QUIQI allows for optimization of the noise model by using metrics quantifying heteroscedasticity and free energy. Comparison with existing methodsQUIQI restores homoscedasticity more effectively than insertion of an image quality index in the analysis design and yields higher sensitivity than simply removing the datasets most corrupted by head motion from the analysis. ConclusionQUIQI provides an optimal approach to group-wise analyses of a range of quantitative MRI parameter maps that is robust to inherent homoscedasticity.

Deep learning-based reconstruction for canine brain magnetic resonance imaging could improve image quality while reducing scan time.

Optimal magnetic resonance imaging (MRI) quality and shorter scan time are challenging to achieve in veterinary practices. Recently, deep learning-based reconstruction (DLR) has been proposed for ideal image quality. We hypothesized that DLR-based MRI will improve brain imaging quality and reduce scan time. This prospective, methods comparison study compared the MR image denoising performances of DLR and conventional methods, with the aim of reducing scan time and improving canine brain image quality. Transverse T2-weighted and fluid-attenuated inversion recovery (FLAIR) sequences of the brain were performed in 12 clinically healthy beagle dogs. Different numbers of excitations (NEX) were used to obtain the image groups NEX4, NEX2, and NEX1. DLR was applied to NEX2 and NEX1 to obtain NEX2DL and NEX1DL . The scan times were recorded, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated for quantitative analysis. Five blinded veterinarians assessed the overall quality, contrast, and perceived SNR on four-point Likert scales. Quantitative and qualitative values were compared among the five groups. Compared with NEX4, NEX2 and NEX1 reduced scan time by 50% and 75%, respectively. The mean SNR and CNR of NEX2DL and NEX1DL were significantly superior to those of NEX4, NEX2, and NEX1 (P<0.05). In all image quality indices, DLR-applied images for both T2-weighted and FLAIR images were significantly higher than NEX4 and NEX2DL had significantly better quality than NEX1DL for FLAIR (P<0.05). Findings indicated that DLR reduced scan time and improved image quality compared with conventional MRI images in a sample of clinically healthy beagles.

Evaluation of a retinal oximetry image quality indicator in patients with cataract.

To evaluate a new automated retinal oximetry image quality indicator with cataract as a clinical model. Sixty-one eyes in 61 patients were imaged by the Oxymap T1 Retinal Oximeter at baseline and 25 eyes were also examined 3 weeks after cataract surgery. Image quality (0-10 on a continuous scale) was compared with standardized AREDS cataract grading and Pentacam lens densitometry. Associations with retinal oximetry measurements and visual acuity were examined. Image quality correlated with total, nuclear and posterior subcapsular cataract grades (ANOVA, p < 0.05), tended to be associated with lens densitometry and it improved from 4.3 ± 1.4 to 5.7 ± 1.0 (p < 0.05) after cataract surgery. Very low image quality, below 3, led to vessel detection failure in retinal oximetry images. Higher image qualities were linearly associated with higher measured retinal oxygen saturations (r = 0.52 in arteries and r = 0.46 in veins; p < 0.001). Retinal oximetry image quality deteriorated with increasing cataract density and improved after cataract surgery, supporting its use as a measure of optical clarity. The numerical quality indicator demonstrated a threshold below which images of poor optical quality should be discarded. Image quality affects the estimates of retinal oximetry parameters and should therefore be included in future analyses.

Size based dependence of patient dose metrics, and image quality metrics for clinical indicator-based imaging protocols in abdominal CT procedures

IntroductionDiagnostic reference level (DRL) values for computed tomography (CT) based on clinical indication are warranted since imaging protocols are indication-dependent. This study proposes clinical DRL values using the CT dose metrics and five patient size-related parameters while considering image quality. MethodsThe volumetric CT dose index (CTDIvol), dose-length product (DLP) and five size-related parameters of size-specific dose estimates (SSDE), namely the anterior-posterior (AP) dimension, lateral (LAT) dimension, sum dimension, effective diameter, and the body mass index (BMI), were used to calculate DRL values for CT chest-abdomen-pelvis (CAP) and abdomen-pelvis (AbP) protocols. DRL values of the clinical indications for cancer, urinary system stones and other pathologies were assessed based on the BMI classifications using the median and 75th percentile. An image subtraction algorithm was used to assess the image quality metrics (IQM) of the CT images. ResultsThe 75th percentile for SSDEAP dimension for CAP cancer was 19.7, 14.9 and 12.7 mGy at Hospitals A, C and E, respectively. The median DLP for other AbP pathologies was 556.3, 1452.0 and 1960.7 mGy.cm for normal weight, overweight and obese patients, respectively, at Hospital A. The image quality varied among BMI classifications for different clinically indicated examinations. Although the dose increased with BMI, the image quality index was consistent because automatic tube current modulation (ATCM) was used. ConclusionDRL values are influenced by patient size-related parameters and the clinical indication protocols, while the image quality index is independent of the BMI. Implications for practiceSize-related clinical DRL values and image quality index can be used to monitor and optimise dose and image quality. Acquisition parameters and image quality indexes should be investigated and adjusted when unusually high DRL values are noted.

Comparative Analysis on De-Noising of MRI Uterus Image for Identification of Endometrial Carcinoma

Purpose: The anatomical and physiological processes of the human body are pictured in radiology using different modalities. Magnetic Resonance Imaging (MRI) supports capturing the images of organs using magnetic field gradients. The quality of MR images is generally affected by various noises such as Gaussian, speckle, salt and pepper, Rayleigh, Rican etc. Removal of these noises from the MR images is essential for further diagnostic procedures.&#x0D; Materials and Methods: In this article, Gaussian noise, speckle noise, and salt and pepper noise are added to the MR uterus image for which different filters are applied to remove the noise for precise identification of endometrial carcinoma.&#x0D; Results: The different filters incorporated for the additive noise removal process are the bilateral filter, Non-Local Means (NLM) filter, anisotropic diffusion filter, and Convolution Neural Network (CNN). The efficiency of the filter is calculated by evaluating the response of the filter by gradually increasing the noise intensity of the MR images.&#x0D; Conclusion: Further, peak Signal-to-Noise Ratio (SNR), structural similarity index measure, image quality index and computational cost parameters are computed and analyzed.

Investigation of the muonic atoms distribution in materials through muonic x-rays momentum simulation using Geant4

Determining the distribution of muonic atoms is essential for μ-X ray imaging. In this study, the generation and de-excitation of muonic atoms in multi-elemental targets were simulated using the Monte Carlo software toolkit Geant4. An approach that reconstructs the production sites of muonic atoms from the μ-X ray momentum is proposed. The imaging results indicated a high simulated spatial resolution of 0.1 mm and a significant increase in the detection efficiency compared to that achieved by the 3-mm pinhole imaging technique. In addition, an analysis of the image quality index (Q) revealed that a superior image quality can be achieved for elements with high and medium atomic numbers. The proposed method has the potential to be further developed into an accurate and efficient technique for positioning the distribution of elements.

Pixel-Wise Attention Residual Network for Super-Resolution of Optical Remote Sensing Images

The deep-learning-based image super-resolution opens a new direction for the remote sensing field to reconstruct further information and details from captured images. However, most current SR works try to improve the performance by increasing the complexity of the model, which results in significant computational costs and memory consumption. In this paper, we propose a lightweight model named pixel-wise attention residual network for optical remote sensor images, which can effectively solve the super-resolution task of multi-satellite images. The proposed method consists of three modules: the feature extraction module, feature fusion module, and feature mapping module. First, the feature extraction module is responsible for extracting the deep features from the input spatial bands with different spatial resolutions. Second, the feature fusion module with the pixel-wise attention mechanism generates weight coefficients for each pixel on the feature map and fully fuses the deep feature information. Third, the feature mapping module is aimed to maintain the fidelity of the spectrum by adding the fused residual feature map directly to the up-sampled low-resolution images. Compared with existing deep-learning-based methods, the major advantage of our method is that for the first time, the pixel-wise attention mechanism is incorporated in the task of super-resolution fusion of remote sensing images, which effectively improved the performance of the fusion network. The accuracy assessment results show that our method achieved superior performance of the root mean square error, signal-to–reconstruction ratio error, universal image quality index, and peak signal noise ratio compared to competing approaches. The improvements in the signal-to-reconstruction ratio error and peak signal noise ratio are significant, with a respective increase of 0.15 and 0.629 dB for Sentinel-2 data, and 0.196 and 1 dB for Landsat data.

Non-destructive evaluation of GTAWA, GTAWF and LBWA welded Ti6Al4V alloy

Ti6Al4V is the most commonly used titanium alloy which features high strength with low density, high fracture toughness, excellent resistance against corrosion and good weldability. Ti6Al4V finds wide application in variety of sectors such as aircraft structure, jet engines, gas turbines, defence armour and IC engine components. The quality of the titanium weldments is significant in producing titanium structures and components. This requires critical Non-Destructive Testing (NDT) procedures to be followed. There is very scarce literature reported on NDT of titanium weldments. In this investigation the Non-Destructive Testing of Ti6Al4V weldments welded using Gas Tungsten Arc Welding – Autogenous (GTAWA), Gas Tungsten Arc Welding – with filler (GTAWF) and Laser Beam Welding – Autogenous (LBWA). The weldments were subjected NDT procedures such as visual and X-ray radiography. The uniqueness of this study is that the Image Quality Indicator (IQI) of aluminium was used as the IQI for titanium is not available in the industries. Generally, the titanium gets contaminated by gases in the atmosphere, leading to loss of mechanical strength and corrosion resistance of the weldment. Hence in this investigation a novel approach for ruling out the contamination of the weld pool and the Heat Affected Zone (HAZ) has been proposed viz. Weld Quality Indicator (WQI), i.e. the discolouration on the surface of the weldment is taken as an indicator of the contamination.

SLAAHE: Selective Apex Adaptive Histogram Equalization

Medical imaging enables specialists to make diagnoses our internal body organs for detecting anomalies without being intrusive. Unfortunately, the images produced by various modalities are not only plagued by noises, but the images contrasts are also subpar, making it impossible to use them for a delicate task like diagnosis, which is directly concerned with our health. Pre-processing the image becomes obligatory to ameliorate its medical worth and contrast enhancement is a sacrosanct part of diagnostic image pre-processing. Even though some popular contrast enhancement algorithms, such as Adaptive Histogram Equalization (AHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE), etc., are widely used, they are insufficient for some medical imaging modalities, such as ultrasound imaging (USG). This paper, named as Selective Apex Adaptive Histogram Equalization (SLAAHE), proposes an effective and efficient contrast enhancement algorithm that dynamically selects the best window size for dividing the low contrast image into image grids and clip limit for clipping the histogram of the low contrast sub-image only. Unique feature of the proposed algorithm is that it clips the image histogram only when it is not well-distributed and ennobles the output image contrast. This makes the algorithm best concerning the contrast enhancement quality, and reduced time and space complexity. Both theoretical analysis and assessment of experimental results with statistical metrics like Universal Image Quality Index (UIQI), Absolute Mean Brightness Error (AMBE), Multi-Scale Structural Similarity Index Measure (MSSSIM), Entropy, and Root Mean Square Error (RMSE) exhibit how the proposed method has gained momentous improvement over existing state-of-the-art methods. For the evaluation metrics UIQI, AMBE, MSSSIM, Entropy, and RMSE, the suggested technique received average scores of 0.79, 0.12, 0.88, 0.92, and 11.52 respectively.

Target visibility index

Quantitative description of the quality of images from the military sight-seeing systems, particularly those of armored vehicles, implies specific approaches different from those used in traditional image processing for civilian needs. Our analysis of the literature shows that there are several disadvantages in the definitions of the quantitative image quality indices, which makes them inapplicable for the characterization of targets in images from military sight-seeing systems. First, quantitative indices describing the quality of images introduced for civilian applications and techniques for their measurements are not target-oriented. In most cases, image quality indices available in the literature characterize the image as a whole but turn out to be irrelevant to the visibility and conspicuity of a target. High image quality indices do not guarantee high visibility and conspicuity of a target. Contrarily, frequently high contrast and thereby visibility of a target is accompanied by abnormal (enormously high or low) lighting of the background, target, or both, which enhances the visibility and conspicuity of the target but results in low-quality indices of the image as a whole. Second, expressions for the contrast, visibility, and conspicuity of a target available in the literature are not symmetric with respect to zero and some of them are singular functions. We claim that an image quality index describing the target visibility should satisfy, at least, the following requirements. First, the notion of the threshold local contrast, still (or no longer) resolved by human eyes, must be involved in the definition of visibility. Second, the visibility index should not be a singular function. Third, visibility is a notion strongly related to the neuronal response of the human brain and, thus, should be in the form of an activation function. Forth, the target visibility index should be of the probability character. Fifth, the visibility index should be target-oriented. In this paper, we propose an expression defining the target visibility index which satisfies all five requirements listed above and develop a technique for its measurement based on the measurements of brightness profile along a line and calculation of the local contrast of the target. The measurements of the target visibility index are illustrated for the partial (visible and infrared) images and for images fused by algorithms of different target-oriented image fusion methods.

The application value of deep learning image reconstruction on improving image quality and evaluating the Qanadli embolism index of dual low-dose CT pulmonary angiography

Objective: To compare the image quality and Qanadli embolism index between deep learning image reconstruction (DLR) and adaptive statistical iterative reconstruction-veo (ASiR-V) in dual low-dose CT pulmonary angiography (CTPA) with low contrast agent dose and low radiation dose. Methods: Eighty-eight patients who underwent dual low-dose CTPA in the radiology department of the affiliated hospital of Xuzhou Medical University from October 2020 to March 2021 were retrospectively analyzed, including 44 males and 44 females, aged from 11 to 87 years (61±15 years). The CTPA examination were performed using 80 kV tube voltage and 20 ml contrast agent. The raw data were reconstructed using standard kernel DLR high level (DL-H) and ASiR-V reconstruction, respectively. The patients were divided into standard kernel DL-H group (n=88, 33 cases of positive embolism) and ASiR-V group (n=88, 36 cases of positive embolism). The CT value, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), subjective image quality score, Qanadli embolism index, positive rate and positive Qanadli embolism index were compared between the two groups. Results: There were no statistically significant differences in CT values of the main pulmonary artery, the right pulmonary artery and the left pulmonary artery between the standard kernel DL-H group and ASiR-V group [(405.8±111.7) vs (404.0±112.0) HU, (412.9±113.1) vs (411.5±112.2) HU, (418.1±119.9) vs (415.4±118.0) HU, respectively;all P>0.05)]. The image noise of the main pulmonary artery, the right pulmonary artery and the left pulmonary artery in the standard kernel DL-H group was significantly lower than the ASiR-V group(16.6±4.7 vs 28.1±4.8, 18.3±6.1 vs 29.8±4.9, 17.6±5.6 vs 28.4±4.7, respectively;all P<0.001). The SNR and CNR of the main pulmonary artery, the right pulmonary artery and the left pulmonary artery in the standard kernel DL-H group were significantly higher than the ASiR-V group(SNR: 25.5±7.1 vs 14.5±3.9, 23.9±7.2 vs 13.9±3.4, 24.9±7.4 vs 14.8±4.1, CNR: 21.6±6.6 vs 12.3±3.9, 20.2±6.7 vs 11.8±3.4, 21.2±6.9 vs 12.6±4.1, respectively;all P<0.001). The subjective image quality score of the standard kernel DL-H group was significantly higher than the ASiR-V group (4.6 vs 3.8, P<0.001). There were no significant difference in the Qanadli embolism index, positive rate and positive Qanadli embolism index between the two groups (all P>0.05). Conclusion: Compared with ASiR-V reconstruction algorithms group, standard kernel DL-H reconstruction algorithms can significantly improve the image quality of dual low-dose CTPA.