Objective Quality Evaluation Research Articles

Implementing verifiable oncological imaging by quality assurance and optimization (i‑Violin) : Protocol for aEuropean multicenter study.

Advanced imaging techniques play apivotal role in oncology. Alarge variety of computed tomography (CT) scanners, scan protocols, and acquisition techniques have led to awide range in image quality and radiation exposure. This study aims at implementing verifiable oncological imaging by quality assurance and optimization (i-Violin) through harmonizing image quality and radiation dose across Europe. The 2‑year multicenter implementation study outlined here will focus on CT imaging of lung, stomach, and colorectal cancer and include imaging for four radiological indications: diagnosis, radiation therapy planning, staging, and follow-up. Therefore, 480 anonymized CT data sets of patients will be collected by the associated university hospitals and uploaded to arepository. Radiologists will determine key abdominopelvic structures for image quality assessment by consensus and subsequently adapt apreviously developed lung CT tool for the objective evaluation of image quality. The quality metrics will be evaluated for their correlation with perceived image quality and the standardized optimization strategy will be disseminated across Europe. The results of the outlined study will be used to obtain European reference data, to build teaching programs for the developed tools, and to create aculture of optimization in oncological CT imaging. The study protocol and rationale for i‑Violin, aEuropean approach for standardization and harmonization of image quality and optimization of CT procedures in oncological imaging, is presented. Future results will be disseminated across all EU member states, and i‑Violin is thus expected to have asustained impact on CT imaging for cancer patients across Europe.

Subjective and objective quality evaluation for industrial images

Recently, the demand for ever-better image processing technique continues to grow in field of industrial scenario monitoring and industrial process inspection. The subjective and objective quality evaluation of industrial images are vital for advancing the development of industrial visual perception and enhancing the quality of industrial image/video processing applications. However, the scarcity of publicly available industrial image databases with reliable subjective scores restricts the development of industrial image quality evaluation (IIQE). In preparation for a vacancy, this article first establishes two industrial image databases (i.e., industrial scenario image dataset (ISID) and industrial process image dataset (IPID)) for assessing IIQE metrics. Furthermore, in order to avoid overwhelming industrial image nuances due to the wavelet subband summation, we then present a novel industrial application subband information fidelity standard (SIFS) evaluation method using the channel capacity of visual signals in wavelet domain. Specifically, we first build a visual signals channel model based on perception process from human eyes to brain. Second, we compute and compare the channel capacity for reference and distorted images to measure the information fidelity in each wavelet subband. Third, we sum over the subbands for information fidelity ratio to obtain the overall quality score. Finally, we fairly compare some up-to-date and our proposed image quality evaluation (IQE) methods in two novelty industrial datasets respectively. Our ISID and IPID datasets are capable of evaluating most IQE metrics comprehensively and paves the way for further research on IIQE. Our SIFS model show a remarkable performance comparing with other up-to-date IQE methods.

Spatial-temporal response of the regional ecological quality to urban settlement development

ABSTRACT Based on the objective evaluation of the regional ecological quality (Urban Cluster in Mid-inner Zhejiang) by Remote Sensing based Ecological Index (RSEI), it was proposed to study the spatial-temporal response of the regional ecological quality to the urban built-up areas, impervious surface, land use and “production-living-ecological” space under urban settlement change to quantitatively describe the mechanism of human activities’ influence on the regional ecology. The results showed that: (1) From 1985 to 2020, the RSEI of the Urban Cluster in Mid-inner Zhejiang was above 0.50 as a whole, showing a trend of first decreasing and then increasing with a slight decrease in some parts. (2) From the perspective of the urban built-up areas, it was dominated by ecological level 2–3, gradually changing from ecological level 3 to ecological level 2. (3) From the perspective of the impervious surface, it was dominated by ecological level 2–3, accounting for more than 75.00% of the total study area. (4) From the perspective of land use, the cropland, forestland, water and construction land were mainly ecological level 2–4, 3–4, 3–4 and 2–3. (5) From the perspective of “production-living-ecological” space, production space, ecological space and living space were dominated by ecological level 3, 3–4 and 2–3. The results can deepen the understanding of the impact of urban settlement development on the regional ecological quality, avoid the unbalanced situation of blindly pursuing socio-economic development while ignoring the ecological environment, which scientifically helps the sustainable construction and high-quality development of the Urban Cluster in Mid-inner Zhejiang.

Research on a Multidimensional Digital Printing Image Quality Evaluation Method Based on MLP Neural Network Regression

High-quality printing is a longstanding objective in the printing and replication industry. However, the methods used to evaluate print quality suffer from subjectivity and multidimensionality, relying on personal preferences and subjective perceptions to assess the quality of printed images, which poses significant limitations. To address these issues, a set of evaluation metrics aimed at assessing the quality of digital printing products is proposed to achieve evaluation results consistent with human visual perception. Given the differing imaging principles of pre-press digital images and post-scan images, these images are first preprocessed to standardize them for comparison. Next, features are extracted in both spatial and frequency domains, and similarity metrics are used to quantify the differences in features between pre-press digital images and post-scan images. Finally, a multilayer perceptron (MLP) neural network regression model is trained to predict the final objective quality scores. Experimental results on two standard databases demonstrate that this metric exhibits high consistency in both subjective and objective quality evaluation metrics for printed image quality assessment and outperforms other metrics in terms of accuracy.

Low-cost IoT-based multichannel spectral acquisition systems for roasted coffee beans evaluation: Case study of roasting degree classification using machine learning

Spectral analyses have become dependable and promising analytical methods for objective evaluation of coffee bean quality. However, although commercial instruments are available, financially accessible device solutions still need to be provided for this technique to become more widely available, particularly among emerging small and medium-scale coffee enterprises. This study developed an innovative multichannel spectral acquisition system integrated with an Internet of Things (IoT) platform for evaluating roasted coffee beans . The system was built using commercially available low-cost components with a total cost of approximately 114 USD. A study on the roasting degree classification combined with five machine learning algorithms was conducted based on 18 channels of spectral data (410–940 nm) acquired from the proposed device. Original spectral datasets were directly used for model development to minimize data processing and simplify the implementation of the best machine learning model on the device. The results revealed that the Random Forest (RF) model demonstrated a satisfactory performance (validation accuracy values reaching 0.988, precision 0.988, recall 0.988, and F1-score 0.987). Therefore, the proposed system can effectively classifiy roasted coffee beans and might have important applications in assisting roasteries during the roasting process in a real-time, non-subjective, and non-invasive manner.

Objective sound quality evaluation of the electric two-stage centrifugal compressor for fuel cell vehicles-experimental investigations

Sound quality is one of the most challenging studies in the field of automotive NVH. The most important noise source in fuel cell vehicles (FCVs) is the air compressor, and its noise seriously affects the sound quality of FCVs. The centrifugal compressor is now the mainstream choice for commercial FCVs, but there are few studies on its sound quality evaluation using objective psychoacoustic parameters. Therefore, a comprehensive vibration-noise experiment is conducted on an electric two-stage centrifugal compressor (ETCC) for FCVs to evaluate the sound quality accurately, including all operating conditions. The sound quality is evaluated by four objective psychoacoustic parameters: loudness, sharpness, fluctuation strength and roughness, which are the most widely used indicators. The reasons for the variation of these acoustic parameters under dynamic operating conditions are accordingly explained. It can be concluded that the loudness and sharpness show a strong and positive correlation with the rotational speed, while the fluctuation strength and roughness just reflect the sound quality of the ETCC for the low-frequency band and high-frequency band, respectively. So, taking loudness and sharpness as the core indicators under accelerating conditions is recommended. Then, the sound quality of the ETCC under unstable conditions at variable speeds is investigated. It is found that the sound quality of the ETCC deteriorates under surge conditions. When entering into the deep surge, the loudness and sharpness both fluctuate drastically, which is caused by the periodic impulse noise. The fluctuation strength rises sharply and keeps a high level in the process of deep surge because of the obvious low-frequency characteristics of the surge. The above three parameters have a clear correlation with the rotational speed, while roughness is the only parameter that is not significantly connected with the rotational speed, which can be explained by the existence of a high-frequency fluctuation under the entire surge process. Finally, the causes of sound quality deterioration are explored using secondary signal processing. The results show that the outlet pressure pulsations of the ETCC under surge conditions lead to the increases and fluctuations of these acoustic parameters, and compressor surge is the main cause of deterioration in sound quality for FCVs. Based on the results, the objective sound quality evaluation of the ETCC is accomplished, and four psychoacoustic parameters can be modified to improve the sound quality. This study provides a theoretical basis for proposing methods to accurately evaluate and improve the sound quality of FCVs.

Point Cloud Quality Assessment Using a One-Dimensional Model Based on the Convolutional Neural Network.

Recent advancements in 3D modeling have revolutionized various fields, including virtual reality, computer-aided diagnosis, and architectural design, emphasizing the importance of accurate quality assessment for 3D point clouds. As these models undergo operations such as simplification and compression, introducing distortions can significantly impact their visual quality. There is a growing need for reliable and efficient objective quality evaluation methods to address this challenge. In this context, this paper introduces a novel methodology to assess the quality of 3D point clouds using a deep learning-based no-reference (NR) method. First, it extracts geometric and perceptual attributes from distorted point clouds and represent them as a set of 1D vectors. Then, transfer learning is applied to obtain high-level features using a 1D convolutional neural network (1D CNN) adapted from 2D CNN models through weight conversion from ImageNet. Finally, quality scores are predicted through regression utilizing fully connected layers. The effectiveness of the proposed approach is evaluated across diverse datasets, including the Colored Point Cloud Quality Assessment Database (SJTU_PCQA), the Waterloo Point Cloud Assessment Database (WPC), and the Colored Point Cloud Quality Assessment Database featured at ICIP2020. The outcomes reveal superior performance compared to several competing methodologies, as evidenced by enhanced correlation with average opinion scores.

Subjective and objective quality evaluation of UGC video after encoding and decoding

The development of the Internet and new media technology has given rise to various forms of media to meet users’ usage needs and expectations. User-Generated-Content (UGC) videos have become a way for users to maximize their participation and express themselves. A large number of UGC videos are uploaded every day on platforms such as Tiktok, Kwai, and Station B. However, the quality of UGC videos is also mixed, with videos with poor image quality providing viewers with a lower user experience. Media service providers need to compress videos uploaded by users to reduce transmission bit rates. How to evaluate the quality of compressed UGC videos is crucial for selecting an appropriate bit rate. In this paper, we have established a UGC database called UGC-New. Concretely, 405 UGC source videos are collected from major video platforms and we encoded the original video using H.264 and H.265 to obtain 2430 distorted videos, resulting in a total of 2835 videos in the dataset. We conducted a subjective human study on this database and obtained 56700 human quality ratings recorded by 100 subjects. At the same time, we also tested a series of VQA models on the dataset for objective analysis and evaluation, including a new one, called New-VQA.

Data driven assessment of rock mass quality in red-bed hilly area: a case study of Guang’an city, SW China

The evaluation of geological suitability for urban underground space (UUS) development is an indispensable prerequisite for its optimal utilization. As the actual carrier of underground facilities, the evaluation of rock mass quality plays a crucial role in assessing geological suitability. However, it is notable that the evaluation of rock mass quality has regrettably remained somewhat marginalized within the broader framework of the geological suitability assessment in recent years. The selection of pertinent indicators for the evaluation of rock mass quality inherently presents an appreciable degree of subjectivity. Predominantly subjective evaluation methods continue to dominate the field, while the application of objective algorithms, such as unsupervised clustering, remains in its nascent stage. Furthermore, there is a lack of comprehensive investigations into distinct combinations of attributes. This limitation confines the broader applicability of the evaluation outcomes in the context of urban underground space. Within this study, we meticulously amassed rock core test data from over 40 boreholes of engineering geological significance within the urban planning ambit of Guang'An City. Utilizing the K-means unsupervised clustering algorithm and the Principal Component Analysis (PCA) algorithm. We successfully conducted an unsupervised clustering procedure with nine distinct physical and mechanical attributes. This yielded an aggregation into five discernible clusters. Building upon the derived clustering outcomes, a stratification of rock mass quality was effectuated into three distinct tiers: Level 1 (characterized by pure sandstone), Level 2 (primarily dominated by sandstone), and Level 3 (denoting fair conditions predominantly influenced by mudstone). This structured stratification facilitates a relatively objective and comprehensive evaluation of rock mass quality within the context of the red-bed hilly terrain. In the course of this analytical trajectory, we conducted a dissection of the clustering efficacy. For strongly correlated attributes, we propose a preliminary dimensionality reduction procedure prior to the clustering endeavor. Moreover, we recommend intervals of 10 m for the stratified evaluation in red bed hilly urban terrains.

Imperceptible and Reversible Acoustic Watermarking Based on Modified Integer Discrete Cosine Transform Coefficient Expansion

This paper aims to explore an alternative reversible digital watermarking solution to guarantee the integrity of and detect tampering with data of probative importance. Since the payload for verification is embedded in the contents, algorithms for reversible embedding and extraction, imperceptibility, payload capacity, and computational time are issues to evaluate. Thus, we propose a reversible and imperceptible audio information-hiding algorithm based on modified integer discrete cosine transform (intDCT) coefficient expansion. In this work, the original signal is segmented into fixed-length frames, and then intDCT is applied to each frame to transform signals from the time domain into integer DCT coefficients. Expansion is applied to DCT coefficients at a higher frequency to reserve hiding capacity. Objective evaluation of speech quality is conducted using listening quality objective mean opinion (MOS-LQO) and the segmental signal-to-noise ratio (segSNR). The audio quality of different frame lengths and capacities is evaluated. Averages of 4.41 for MOS-LQO and 23.314 [dB] for segSNR for 112 ITU-T test signals were obtained with a capacity of 8000 bps, which assured imperceptibility with the sufficient capacity of the proposed method. This shows comparable audio quality to conventional work based on Linear Predictive Coding (LPC) regarding MOS-LQO. However, all segSNR scores of the proposed method have comparable or better performance in the time domain. Additionally, comparing histograms of the normalized maximum absolute value of stego data shows a lower possibility of overflow than the LPC method. A computational cost, including hiding and transforming, is an average of 4.884 s to process a 10 s audio clip. Blind tampering detection without the original data is achieved by the proposed embedding and extraction method.

122 Objective evaluation of plan quality in the PATHOS clinical trial using automated treatment planning

122 Objective evaluation of plan quality in the PATHOS clinical trial using automated treatment planning

A novel method for weighting decision makers for failure mode and effect analysis under intuitionistic fuzzy environment

AbstractThis study introduces an innovative method to implement Failure Mode and Effect Analysis (FMEA) in an Intuitionistic Fuzzy (IF) environment and targets inherent limitations of traditional FMEA, particularly its reliance on precise numerical values that can lead to human subjectivity and compromise accuracy of analysis. The primary aim is to increase the objectivity in evaluating Failure Modes (FMs) in industrial settings, thus enhancing safety and quality assessments. The novel approach integrates the Measurement Alternatives and Ranking according to the Compromise Solution (MARCOS) method within an IF framework. An implementation at a defense industry company is performed for evaluation of the FMs by four Decision Makers (DMs), each possessing different levels of experience and system knowledge. A key innovation of this study is the unique weighting methodology applied to DMs, based on predefined rules, which acknowledges and quantifies their varied expertise and insights. Our methodology significantly contributes to the study's practicality and significance in ranking FMs, emphasizing its effectiveness, robustness, and accuracy. The findings reveal that this method greatly reduces the subjectivity found in traditional FMEA, also providing a more detailed and reliable assessment. This is achieved by adapting the analysis to the specific knowledge levels of the DMs, leading to more dependable and objective quality evaluations in industrial processes. The application of this method in the real‐world setting demonstrates its practical relevance and potential for wider adoption.

Adaptive convolutional sparsity with sub-band correlation in the NSCT domain for MRI image fusion

Objective. Multimodal medical image fusion (MMIF) technologies merges diverse medical images with rich information, boosting diagnostic efficiency and accuracy. Due to global optimization and single-valued nature, convolutional sparse representation (CSR) outshines the standard sparse representation (SR) in significance. By addressing the challenges of sensitivity to highly redundant dictionaries and robustness to misregistration, an adaptive convolutional sparsity scheme with measurement of the sub-band correlation in the non-subsampled contourlet transform (NSCT) domain is proposed for MMIF. Approach. The fusion scheme incorporates four main components: image decomposition into two scales, fusion of detail layers, fusion of base layers, and reconstruction of the two scales. We solved a Tikhonov regularization optimization problem with source images to obtain the base and detail layers. Then, after CSR processing, detail layers were sparsely decomposed using pre-trained dictionary filters for initial coefficient maps. NSCT domain’s sub-band correlation was used to refine fusion coefficient maps, and sparse reconstruction produced the fused detail layer. Meanwhile, base layers were fused using averaging. The final fused image was obtained via two-scale reconstruction. Main results. Experimental validation of clinical image sets revealed that the proposed fusion scheme can not only effectively eliminate the interference of partial misregistration, but also outperform the representative state-of-the-art fusion schemes in the preservation of structural and textural details according to subjective visual evaluations and objective quality evaluations. Significance. The proposed fusion scheme is competitive due to its low-redundancy dictionary, robustness to misregistration, and better fusion performance. This is achieved by training the dictionary with minimal samples through CSR to adaptively preserve overcompleteness for detail layers, and constructing fusion activity level with sub-band correlation in the NSCT domain to maintain CSR attributes. Additionally, ordering the NSCT for reverse sparse representation further enhances sub-band correlation to promote the preservation of structural and textural details.

Automated Quality Evaluation Index for Arterial Spin Labeling Derived Cerebral Blood Flow Maps.

Arterial spin labeling (ASL) derived cerebral blood flow (CBF) maps are prone to artifacts and noise that can degrade image quality. To develop an automated and objective quality evaluation index (QEI) for ASL CBF maps. Retrospective. Data from N = 221 adults, including patients with Alzheimer's disease (AD), Parkinson's disease, and traumatic brain injury. Pulsed or pseudocontinuous ASL acquired at 3 T using non-background suppressed 2D gradient-echo echoplanar imaging or background suppressed 3D spiral spin-echo readouts. The QEI was developed using N = 101 2D CBF maps rated as unacceptable, poor, average, or excellent by two neuroradiologists and validated by 1) leave-one-out cross validation, 2) assessing if CBF reproducibility in N = 53 cognitively normal adults correlates inversely with QEI, 3) if iterative discarding of low QEI data improves the Cohen's d effect size for CBF differences between preclinical AD (N = 27) and controls (N = 53), 4) comparing the QEI with manual ratings for N = 50 3D CBF maps, and 5) comparing the QEI with another automated quality metric. Inter-rater reliability and manual vs. automated QEI were quantified using Pearson's correlation. P < 0.05 was considered significant. The correlation between QEI and manual ratings (R = 0.83, CI: 0.76-0.88) was similar (P = 0.56) to inter-rater correlation (R = 0.81, CI: 0.73-0.87) for the 2D data. CBF reproducibility correlated negatively (R = -0.74, CI: -0.84 to -0.59) with QEI. The effect size comparing patients and controls improved (R = 0.72, CI: 0.59-0.82) as low QEI data was discarded iteratively. The correlation between QEI and manual ratings (R = 0.86, CI: 0.77-0.92) of 3D ASL was similar (P = 0.09) to inter-rater correlation (R = 0.78, CI: 0.64-0.87). The QEI correlated (R = 0.87, CI: 0.77-0.92) significantly better with manual ratings than did an existing approach (R = 0.54, CI: 0.30-0.72). Automated QEI performed similarly to manual ratings and can provide scalable ASL quality control. 2 TECHNICAL EFFICACY: Stage 1.

Functional Assessment of Human Articular Cartilage Using Second Harmonic Generation (SHG) Imaging: A Feasibility Study.

Many arthroscopic tools developed for knee joint assessment are contact-based, which is challenging for in vivo application in narrow joint spaces. Second harmonic generation (SHG) laser imaging is a non-invasive and non-contact method, thus presenting an attractive alternative. However, the association between SHG-based measures and cartilage quality has not been established systematically. Here, we investigated the feasibility of using image-based measures derived from SHG microscopy for objective evaluation of cartilage quality as assessed by mechanical testing. Human tibial plateaus harvested from nine patients were used. Cartilage mechanical properties were determined using indentation stiffness (Einst) and streaming potential-based quantitative parameters (QP). The correspondence of the cartilage electromechanical properties (Einst and QP) and the image-based measures derived from SHG imaging, tissue thickness and cell viability were evaluated using correlation and logistic regression analyses. The SHG-related parameters included the newly developed volumetric fraction of organised collagenous network (Φcol) and the coefficient of variation of the SHG intensity (CVSHG). We found that Φcol correlated strongly with Einst and QP (ρ = 0.97 and - 0.89, respectively). CVSHG also correlated, albeit weakly, with QP and Einst, (|ρ| = 0.52-0.58). Einst and Φcol were the most sensitive predictors of cartilage quality whereas CVSHG only showed moderate sensitivity. Cell viability and tissue thickness, often used as measures of cartilage health, predicted the cartilage quality poorly. We present a simple, objective, yet effective image-based approach for assessment of cartilage quality. Φcol correlated strongly with electromechanical properties of cartilage and could fuel the continuous development of SHG-based arthroscopy.

Development and evaluation of objective trial performance metrics for multisite clinical studies: Experience from the AlcHep Network

BackgroundRecruitment and retention are critical in clinical studies but there are limited objective metrics of trial performance. We tested if development of trial performance metrics will allow for objective evaluation of study quality. Performance metrics were developed using data from the observational cohort (OBS) and randomized clinical trial (RCT) arms of the prospective Alcoholic Hepatitis Network. MethodsYield-rate (%YR; eligible/screened), recruitment index (RI; mean recruitment time/patient), completion index (CI; average number of days to complete the follow-up/patient), and protocol adherence index (AI; average number of deviations/subject recruited) were determined. Results2250 patients (1168 for OBS; 1082 for RCT) were screened across 8 sites. Recruitment in the RCT (57% target) was similar to that in the OBS (59% target). Of those screened, 743 (63.6%) subjects in the OBS and 147 (13.6%) subjects in the RCT were enrolled in the study. In OBS study, 253 (34.1%) subjects, and in the RCT, 68 (46.3%) subjects, completed the study or reached a censoring event. Across all sites (range), YR for OBS was 63.6% (41.3–98.3%) and for RCT was 13.6% (5.5–92.6%); RI for OBS was 1.66 (8.79–19.85) and for RCT was 4.05 (19.76–36.43); CI for OBS was 4.87 (22.6–118.3) and for RCT was 8.75 (27.27–161.5); and AR for OBS was 0.56 (0.08–1.04) and for RCT was 1.55 (0.39–3.21. Factors related to participants, research design, study team, and research sponsors contributed to lower performance metrics. ConclusionsObjective measures of clinical trial performance allow for strategies to enhance study quality and development of site-specific improvement plans.Trial registration number: ClinicalTrials.gov NCT4072822 NCT 3850899.

Extended TODIM method based on VIKOR for college English teaching quality evaluation with interval-valued intuitionistic fuzzy information

In college education, English is a required course for every college student, and undergraduate colleges have certain requirements for college English proficiency. At the same time, English is directly related to its learning, so improving the quality of college English teaching (CET) is very important. Teaching quality is a key indicator for measuring the effectiveness of English teaching. Learning quality evaluation is a very complex process that involves many factors, such as evaluation indicators, evaluation methods, etc. Therefore, establishing an objective and scientific English quality evaluation system is a challenging issue. The CET quality evaluation is a MAGDM. Then, the TODIM and VIKOR was used to set up MAGDM. The interval-valued intuitionistic fuzzy sets (IVIFSs) are employed as a tool for depicting uncertain information during the CET quality evaluation. In this study, the entropy and score values are employed to establish the objective weights. Then, an integrated interval-valued intuitionistic fuzzy TODIM-VIKOR (IVIF-TODIM-VIKOR) is developed to cope with the MAGDM problem. An illustrative example for CET quality evaluation and some comparative analysis are developed to demonstrate the validity and reliability of IVIF-TODIM-VIKOR method.

Objective evaluation of voice quality by intermittent recording of sound on English voice

Continuous sound surveillance have the problem of unintentionally recording conversational content. On the other hand, if only coarse features are stored so that speech cannot be recovered, the use of the accumulated recorded information is very limited. In this study, we propose a method to record raw background sounds while making the conversational content inaudible by periodically repeating recording and stopping. In this presentation, we will show the results of an objective evaluation using a speech recognition model by varying the ratio of recording time and the recording stop frequency. The results showed that the recording time ratio can be maximized by selecting appropriate recording stop frequency even for the same objective voice quality.

Analysis method of apparent quality of fair-faced concrete based on convolutional neural network machine learning

Fair-faced concrete has attracted much attention because of its noble texture, but the evaluation of its appearance quality often depends on personal subjective feelings (especially color difference) or complex image processing means, which hinders its development. Therefore, the accurate and objective evaluation of appearance quality is the key to fair-faced concrete engineering. This paper proposed a machine learning method to analyze the appearance quality of fair-faced concrete. Firstly, images of fair-faced concrete were collected by UAV (Unmanned Aerial Vehicle) or scanner for pretreatment. Then, based on the convolutional neural network algorithm, a visual quality database of fair-faced concrete including 2463 groups of information was established, and it was trained to recognize color difference and bubbles on the surface of fair-faced concrete pictures. The performance of the model is evaluated and optimized. The influence of environmental factors such as light intensity and surface moisture on the model is analyzed. Finally, the machine learning method is used to analyze the appearance quality of fair-faced concrete in engineering site. Compared with the traditional analysis methods, the accuracy of stomatal area rate and maximum stomatal diameter recognition was increased from 79.34 % to 93.83 %, and the accuracy of color difference recognition was increased by more than 10 %. At the same time, the identification method in this study still has strong robustness under light and surface moisture variation. The research results can provide useful thinking for evaluating fair-faced concrete engineering.

Dual-layer spectral computed tomography aortography using a seventy-five-percent-reduced iodine dose protocol and multiparameter spectral imaging: comparison with conventional computed tomography imaging.

Computed tomography angiography (CTA) is the recommended diagnostic and follow-up imaging modality for acute aortic dissection (AD). However, the high-contrast medium burden associated with repeated CT aortography follow-ups remains a significant concern. This prospective study aimed to assess whether an ultra-low contrast dose (75% cutoff) aortic CTA protocol on dual-layer spectral CT could achieve comparable image quality with the full dose protocol. We also investigated the image quality of the virtual noncontrast (VNC) images derived from the ultra-low dose protocol. This study included 37 consecutive patients who were referred to aortic CTA from May 2022 to August 2022. The enrolled patients underwent full-dose contrast CTA and ultra-low dose (reduced to 25% of conventional) contrast CTA on dual-layer spectral CT in 1 day. Virtual monochromatic images (VMIs) were reconstructed with 40 and 70 keV. The VNC images were reconstructed for both protocols. Objective image quality evaluation, recorded as signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs), was compared between the groups using 1-way analysis of variance and post hoc analysis with Bonferroni correction. Subjective image quality was also compared between the groups. Finally, VNC images derived from the low-dose (VNClow) and full-dose (VNCfull) protocols were compared to the true noncontrast (TNC) images. Neither CNR nor SNR was lower for the 40-keV images reconstructed from the ultra-low dose group compared to the conventional images. Both were significantly higher than those of the 70-keV images. Regarding subjective image quality, vessel enhancement was not significantly different between the 40-keV VMI and full-dose images [ascending aorta (AAO): 4.37±0.46 vs. 4.57±0.48, P=0.096; brachiocephalic arteries: 4.34±0.45 vs. 4.51±0.49, P=0.152; abdominal aortic side branch: 4.42±0.48 vs. 4.51±0.49, P=0.480]. The VNClow images were similar to the TNC images but significantly different from the VNCfull images (P<0.001). Ultra-low contrast aortic CTA with a 75%-reduced iodine dose using dual-layer spectral CT and the derived VNC achieved image quality comparable to that of conventional CTA and TNC images.