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In this paper, a streak camera system is used for obtaining the spectral images in laser-induced breakdown spectroscopy. Streak images are time-resolved, thus enabling tracking of the temporal development of the atomic and ionic emission lines from the plasma plume. To make our study more comprehensive, we have used a machine-learning approach to data analysis. We propose how to reduce the level of noise on streak images by applying the principal component analysis technique. The usual method of improving the signal-to-noise ratio by taking a large number of expositions in streak camera photon counting mode is often not practical. [ABSTRACT FROM AUTHOR]

MRI reconstruction of undersampled data using a deep learning (DL) network has been recently performed as part of accelerated imaging. Herein, we compared DL-reconstructed T2-weighted image (T2-WI) to conventional T2-WI regarding image quality and degenerative lesion detection. Sixty-two patients underwent C-spine (n = 27) or L-spine (n = 35) MRIs, including conventional and DL-reconstructed T2-WI. Image quality was assessed with non-uniformity measurement and 4-scale grading of structural visibility. Three readers (R1, R2, R3) independently assessed the presence and types of degenerative lesions. Student t -test was used to compare non-uniformity measurements. Interprotocol and interobserver agreement of structural visibility was analyzed with Wilcoxon signed-rank test and weighted-κ values, respectively. The diagnostic equivalence of degenerative lesion detection between two protocols was assessed with interchangeability test. The acquisition time of DL-reconstructed images was reduced to about 21–58% compared to conventional images. Non-uniformity measurement was insignificantly different between the two images (p -value = 0.17). All readers rated DL-reconstructed images as showing the same or superior structural visibility compared to conventional images. Significantly improved visibility was observed at disk margin of C-spine (R1, p < 0.001; R2, p = 0.04) and dorsal root ganglia (R1, p = 0.03; R3, p = 0.02) and facet joint (R1, p = 0.04; R2, p < 0.001; R3, p = 0.03) of L-spine. Interobserver agreements of image quality were variable in each structure. Clinical interchangeability between two protocols for degenerative lesion detection was verified showing <5% in the upper bounds of 95% confidence intervals of agreement rate differences. DL-reconstructed T2-WI demonstrates comparable image quality and diagnostic performance with conventional T2-WI in spine imaging, with reduced acquisition time. • Deep learning network can reconstruct undersampled MR data with reduced scan time. • Deep learning can produce images of equivalent quality compared to conventional MRI. • Conventional and DL-reconstructed spine MRI are clinically interchangeable. [ABSTRACT FROM AUTHOR]

In this paper, we construct a deep framework for full-reference image quality assessment (FR-IQA) by combining convolution and self-attention features; this approach effectively uses multiscale features to mimic the image evaluation in human eyes. We achieve the integration of information from local to global. First, convolutional neural network (CNN) and encoder of Swin transformer are used to extract multiscale paired features. Second, for each type of features, we fuse them after converting them to a fixed number of channels. For the fused features, we use the square of the difference between two pixel values at the corresponding channel positions to represent the features of the distortion degree. Then, we introduce a recurrent neural network (RNN) to capture the global features. Finally, for the two types of features, we use the full connection (FC) layer to regress two scores and then add the weights to compute the ultimate perceived score. By training and testing on publicly available FR-IQA datasets, experimental results further validate the superiority of our approach. [ABSTRACT FROM AUTHOR]

Ultrafast X-ray computed tomography is an advanced imaging technique for multiphase flows. It has been used with great success for studying gas–liquid as well as gas–solid flows. Here, we apply this technique to analyze density-driven particle segregation in a rotating drum as an exemplary use case for analyzing industrial particle mixing systems. As glass particles are used as the denser of two granular species to be mixed, beam hardening artefacts occur and hamper the data analysis. In the general case of a distribution of arbitrary materials, the inverse problem of image reconstruction with energy-dependent attenuation is often ill-posed. Consequently, commonly known beam hardening correction algorithms are often quite complex. In our case, however, the number of materials is limited. We therefore propose a correction algorithm simplified by taking advantage of the known material properties, and demonstrate its ability to improve image quality and subsequent analyses significantly. [ABSTRACT FROM AUTHOR]

In hyperspectral image (HSI) reconstruction tasks, due to the lack of ground truth in real imaging processes, models are usually trained and validated on simulation datasets and then tested on real measurements captured by real HSI imaging systems. However, due to the gap between the simulation imaging process and the real imaging process, the best model validated on the simulation dataset may fail on real measurements. To obtain the best model for the real-world task, it is crucial to design a suitable no-reference HSI quality assessment metric to reflect the reconstruction performance of different models. In this paper, we propose a novel no-reference HSI quality assessment metric via ranking feature learning (R-NHSIQA), which calculates the Wasserstein distance between the distribution of the deep features of the reconstructed HSIs and the benchmark distribution. Additionally, by introducing the spectral self-attention mechanism, we propose a Spectral Transformer (S-Transformer) to extract the spatial-spectral representative deep features of HSIs. Furthermore, to extract quality-sensitive deep features, we use quality ranking as a pre-training task to enhance the representation capability of the S-Transformer. Finally, we introduce the Wasserstein distance to measure the distance between the distribution of the deep features and the benchmark distribution, improving the assessment capacity of our method, even with non-overlapping distributions. The experimental results demonstrate that the proposed metric yields consistent results with multiple full-reference image quality assessment (FR-IQA) metrics, validating the idea that the proposed metric can serve as a substitute for FR-IQA metrics in real-world tasks. [ABSTRACT FROM AUTHOR]

The No‐Reference Image Quality Assessment (NR‐IQA) method can predict quality scores of distorted images without the reference image. However, due to the variability in both color and structure of images, existing NR‐IQA methods struggle to accurately predict quality scores of distorted images. Therefore, an NR‐IQA method based on a Dual‐Branch Residual Network (DBRIQA) for evaluating the quality scores of color‐distorted images is proposed. First, guided filtering is applied to the hue images in the HSV space to extract distortion information from the image's color. Then, due to significant differences in distorted images, enhancements are made to the traditional residual blocks, forming a feature extraction module that captures multi‐scale features from the image. In order to capture the global relationships in the distorted image, a Global‐Level Attention Block (GLAB) is introduced, facilitating the interaction of information among the extracted features. Experiments were conducted across four publicly available IQA datasets, including LIVE, CSIQ, TID2008, and TID2013. The experimental results demonstrate that the proposed method exhibits strong performance and generalization capabilities in predicting image quality compared to peer methods. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to evaluate the feasibility of 11C-CFT PET brain imaging in Parkinson's Disease using a total-body PET/CT scanner and explore the optimal scan duration to guide the clinical practice. Methods: Thirty-two patients with Parkinson's disease (PD) performing 11C-CFT PET/CT brain imaging using a total-body PET/CT scanner were retrospectively enrolled. The PET data acquired over a period of 900 s were reconstructed into groups of different durations: 900-s, 720-s, 600-s, 480-s, 300-s, 180-s, 120-s, 60-s, and 30-s (G900 to G30). The subjective image quality analysis was performed using 5-point scales. Semi-quantitative measurements were analyzed by SUVmean and dopamine transporter (DAT) binding of key brain regions implicated in PD, including the caudate nucleus and putamen. The full-time images (G900) were served as reference. Results: The overall G900, G720, and G600 image quality scores were 5.0 ± 0.0, 5.0 ± 0.0, and 4.9 ± 0.3 points, respectively, and there was no significant difference among these groups (P > 0.05). A significant decrease in these scores at durations shorter than 600 s was observed when compared to G900 images (P < 0.05). However, all G300 image quality was clinically acceptable (≥ 3 points). As the scan duration reduced, the SUVmean and DAT binding of caudate nucleus and putamen decreased progressively, while there were no statistically significant variations in the SUVmean of the background among the different groups. Moreover, the changes in the lesion DAT binding (ΔDAT-binding) between the full-time reference G900 image and other reconstructed group G720 to G30 images generally increased along with the reduced scan time. Conclusion: Sufficient image quality and lesion conspicuity could be achieved at 600-s scan duration for 11C-CFT PET brain imaging in PD assessment using a total-body PET/CT scanner, while the image quality of G300 was acceptable to meet clinical diagnosis, contributing to improve patient compliance and throughput of PET brain imaging. [ABSTRACT FROM AUTHOR]

Purpose To evaluate intra-patient and interobserver agreement in patients who underwent liver MRI with gadoxetic acid using two different multi-arterial phase (AP) techniques. Methods A total of 154 prospectively enrolled patients underwent clinical gadoxetic acid-enhanced liver MRI twice within 12 months, using two different multi-arterial algorithms: CAIPIRINHA-VIBE and TWIST-VIBE. For every patient, breath-holding time, body mass index, sex, age were recorded. The phase without contrast media and the APs were independently evaluated by two radiologists who quantified Gibbs artefacts, noise, respiratory motion artefacts, and general image quality. Presence or absence of Gibbs artefacts and noise was compared by the McNemar's test. Respiratory motion artefacts and image quality scores were compared using Wilcoxon signed rank test. Interobserver agreement was assessed by Cohen kappa statistics. Results Compared with TWIST-VIBE, CAIPIRINHA-VIBE images had better scores for every parameter except higher noise score. Triple APs were always acquired with TWIST-VIBE but failed in 37% using CAIPIRINHA-VIBE: 11% have only one AP, 26% have two. Breath-holding time was the only parameter that influenced the success of multi-arterial techniques. TWIST-VIBE images had worst score for Gibbs and respiratory motion artefacts but lower noise score. Conclusion CAIPIRINHA-VIBE images were always diagnostic, but with a failure of triple-AP in 37%. TWIST-VIBE was successful in obtaining three APs in all patients. Breath-holding time is the only parameter which can influence the preliminary choice between CAIPIRINHA-VIBE and TWIST-VIBE algorithm. Advances in knowledge If the patient is expected to perform good breath-holds, TWIST-VIBE is preferable; otherwise, CAIPIRINHA-VIBE is more appropriate. [ABSTRACT FROM AUTHOR]

In recent years, hyperspectral (HS) sharpening technology has received high attention and HS sharpened images have been widely applied. However, the quality assessment of HS sharpened images has not been well addressed and is still limited to the use of full-reference quality evaluation. In this paper, a novel no-reference quality assessment method based on Benford's law for HS sharpened images is proposed. Without a reference image, the proposed method detects fusion distortion by performing first digit distribution on three quality perception features in HS sharpened images, using the standard Benford's law as a benchmark. The experiment evaluates 10 HS fusion methods on three HS datasets and selects four full-reference metrics and four no-reference metrics to compare with the proposed method. The experimental results demonstrate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

Objectives: Visualizing left atrial anatomy including the pulmonary veins (PVs) is important for planning the procedure of pulmonary vein isolation with ablation in patients with atrial fibrillation (AF). The aims of our study are to investigate the feasibility of the 3D whole-heart bright-blood and black-blood phase-sensitive (BOOST) inversion recovery sequence in patients with AF scheduled for ablation or electro-cardioversion, and to analyze the correlation between image quality and heart rate and rhythm of patients. Methods: BOOST was performed for assessing PVs both with T2 preparation pre-pulse (T2prep) and magnetization transfer preparation (MTC) in 45 patients with paroxysmal or permanent AF scheduled for ablation or electro-cardioversion. Image quality analyses were performed by two independent observers. Qualitative assessment was made using the Likert scale; for quantitative analysis, signal to noise ratios (SNR) and contrast to noise ratios (CNR) were calculated for each PV. Heart rate and rhythm were analyzed based on standard 12-lead ECGs. Results: All MTC-BOOST acquisitions achieved diagnostic quality in the PVs, while a significant proportion of T2prep-BOOST images were not suitable for assessing PVs. SNR and CNR values of the MTC-BOOST bright-blood images were higher if patients had sinus rhythm. We found a significant or nearly significant negative correlation between heart rate and the SNR and CNR values of MTC-BOOST bright-blood images. Conclusion: 3D whole-heart MTC-BOOST bright-blood imaging is suitable for visualizing the PVs in patients with AF, producing diagnostic image quality in 100% of cases. However, image quality was influenced by heart rate and rhythm. Clinical relevance statement: The novel 3D whole-heart BOOST CMR sequence needs no contrast administration and is performed during free-breathing; therefore, it is easy to use for a wide range of patients and is suitable for visualizing the PVs in patients with AF. Key Points: • The applicability of the novel 3D whole-heart bright-blood and black-blood phase-sensitive sequence to pulmonary vein imaging in clinical practice is unknown. • Magnetization transfer-bright-blood and black-blood phase-sensitive imaging is suitable for visualizing the pulmonary veins in patients with atrial fibrillation with excellent or good image quality. • Bright-blood and black-blood phase-sensitive cardiac magnetic resonance sequence is easy to use for a wide range of patients as it needs no contrast administration and is performed during free-breathing. [ABSTRACT FROM AUTHOR]

Objectives: To perform a comprehensive within-subject image quality analysis of abdominal CT examinations reconstructed with DLIR and to evaluate diagnostic accuracy compared to the routinely applied adaptive statistical iterative reconstruction (ASiR-V) algorithm. Materials and methods: Oncologic patients were prospectively enrolled and underwent contrast-enhanced CT. Images were reconstructed with DLIR with three intensity levels of reconstruction (high, medium, and low) and ASiR-V at strength levels from 10 to 100% with a 10% interval. Three radiologists characterized the lesions and two readers assessed diagnostic accuracy and calculated signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), figure of merit (FOM), and subjective image quality, the latter with a 5-point Likert scale. Results: Fifty patients (mean age: 70 ± 10 years, 23 men) were enrolled and 130 liver lesions (105 benign lesions, 25 metastases) were identified. DLIR_H achieved the highest SNR and CNR, comparable to ASiR-V 100% (p ≥.051). DLIR_M returned the highest subjective image quality (score: 5; IQR: 4–5; p ≤.001) and significant median increase (29%) in FOM (p <.001). Differences in detection were identified only for lesions ≤ 0.5 cm: 32/33 lesions were detected with DLIR_M and 26 lesions were detected with ASiR-V 50% (p =.031). Lesion accuracy of was 93.8% (95% CI: 88.1, 97.3; 122 of 130 lesions) for DLIR and 87.7% (95% CI: 80.8, 92.8; 114 of 130 lesions) for ASiR-V 50%. Conclusions: DLIR yields superior image quality and provides higher diagnostic accuracy compared to ASiR-V in the assessment of hypovascular liver lesions, in particular for lesions ≤ 0.5 cm. Clinical relevance statement: Deep learning image reconstruction algorithm demonstrates higher diagnostic accuracy compared to iterative reconstruction in the identification of hypovascular liver lesions, especially for lesions ≤ 0.5 cm. Key Points: • Iterative reconstruction algorithm impacts image texture, with negative effects on diagnostic capabilities. • Medium-strength deep learning image reconstruction algorithm outperforms iterative reconstruction in the diagnostic accuracy of ≤ 0.5 cm hypovascular liver lesions (93.9% vs 78.8%), also granting higher objective and subjective image quality. • Deep learning image reconstruction algorithm can be safely implemented in routine abdominal CT protocols in place of iterative reconstruction. [ABSTRACT FROM AUTHOR]

With technological developments, the resolution of the display systems is increasing, causing image data to increase. Several technologies are aiding in optimizing these data during transmission and storage while maintaining quality. Still, the internal data transmission for accessing the memory sub‐system during reading and writing cycles consumes significant power while requiring large memory for storage, which is inefficient, especially for portable displays. Hence, this work investigates the feasibility of reducing data through approximation for images in different formats and resolutions for human and computer vision based applications. Different image quality assessment metrics are utilized for performance evaluations with optimum image quality assessment selection according to application requirements. In addition, the feasibility analysis is conducted for three different applications as examples of computer vision. The experimental analysis highlights theoretical and industrial importance by showing that image data can be reduced from 25.0% to 62.5%, while satisfying requirements for HVS‐based and computer vision‐based applications. [ABSTRACT FROM AUTHOR]

Background: As people pay more attention to their skin health and the demand of developing skin care products for facial blackheads grows, the value of objective and efficient image recognition methods for blackheads is becoming more evident. Inspired by this current situation, this study attempted to analyze the number of blackheads of different severity automatically on the nose using an object recognition method on photographs of the nasal blackheads of subjects. Method: This study collected 350 subjects' facial photos in the laboratory environment, who aged 18–60, with blackhead symptoms in the nasal region. And expert assessment was used as a reference for machine learning to verify the performance of the nasal blackhead image recognition model through consistency and correlation analysis. Results: The study concluded that the algorithm accuracy reached above 0.9, the model itself was effective, and the consistency between the model and the expert assessor assessment results was good, with the number of nasal blackheads, the count of blackheads of different severity, and the intra‐group correlation coefficient ICC of blackhead severity all above 0.9, indicating that the deep learning‐based assessment model had high overall performance and the evaluation results were comparable to those of the expert assessor. Conclusion: The recognition and analyzing model of nasal blackhead images provides a scientifically objective and accurate method for identifying the number and evaluating the severity of nasal blackheads. By using this model, the efficiency of evaluating nasal blackhead images in the cosmetics clinical trial will be improved. The assessment result of nasal blackheads will be objective and stable, and not only rely on the professional knowledge and clinical experience of assessors. The model can try to be applied in cosmetics efficacy testing and continuously optimized. [ABSTRACT FROM AUTHOR]

To assess the image quality parameters of dual-energy computed tomography angiography (DECTA) 40-, and 60 keV virtual monoenergetic images (VMIs) combined with deep learning-based image reconstruction model (DLM) and iterative reconstructions (IR). CT scans of 28 post EVAR patients were enrolled. The 60 s delayed phase of DECTA was evaluated. Objective [noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR)] and subjective (overall image quality and endoleak conspicuity – 3 blinded readers assessment) image quality analyses were performed. The following reconstructions were evaluated: VMI 40, 60 keV VMI; IR VMI 40, 60 keV; DLM VMI 40, 60 keV. The noise level of the DLM VMI images was approximately 50% lower than that of VMI reconstruction. The highest CNR and SNR values were measured in VMI DLM images. The mean CNR in endoleak in 40 keV was accounted for as 1.83 ± 1.2; 2.07 ± 2.02; 3.6 ± 3.26 in VMI, VMI IR, and VMI DLM, respectively. The DLM algorithm significantly reduced noise and increased lesion conspicuity, resulting in higher objective and subjective image quality compared to other reconstruction techniques. The application of DLM algorithms to low-energy VMIs significantly enhances the diagnostic value of DECTA in evaluating endoleaks. DLM reconstructions surpass traditional VMIs and IR in terms of image quality. [ABSTRACT FROM AUTHOR]

Background: To compare the potential of various bone evaluations by considering photon-counting CT (PCCT) and multiple energy-integrating-detector CT (EIDCT), including three dual-energy CT (DECT) scanners with standardized various parameters in both standard resolution (STD) and ultra-high-resolution (UHR) modes. Methods: Four cadaveric forearms were scanned using PCCT and five EIDCTs, by applying STD and UHR modes. Visibility of bone architecture, image quality, and a non-displaced fracture were subjectively scored against a reference EIDCT image by using a five-point scale. Image noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were also compared. To assess metal artifacts, a forearm with radial plate fixation was scanned by with and without Tin filter (Sn+ and Sn−), and virtual monoenergetic image (VMI) at 120 keV was created. Regarding Sn+ and VMI, images were only obtained from the technically available scanners. Subjective scores and the areas of streak artifacts were compared. Results: PCCT demonstrated significantly lower noise (p < 0.001) and higher bone SNR and CNR (p < 0.001) than all EIDCTs in both resolution modes. However, there was no significant difference between PCCT and EIDCTs in almost all subjective scores, regardless of scan modes, except for image quality where a significant difference was observed, compared to several EIDCTs. Metal artifact analysis revealed PCCT had larger artifact in Sn− and Sn+ (p < 0.001), but fewer in VMIs than three DECTs (p < 0.001 or 0.001). Conclusions: Under standardized conditions, while PCCT had almost no subjective superiority in visualizing bone structures and fracture line when compared to EIDCTs, it outperformed in quantitative analysis related to image quality, especially in lower noise and higher tissue contrast. When using PCCT to assess cases with metal implants, it may be recommended to use VMIs to minimize the possible tendency for artifact to be pronounced. [ABSTRACT FROM AUTHOR]

Objectives To compare the image quality and diagnostic performance of low-dose CT urography to that of concurrently acquired conventional CT using dual-source CT. Methods This retrospective study included 357 consecutive CT urograms performed by third-generation dual-source CT in a single institution between April 2020 and August 2021. Two-phase CT images (unenhanced phase, excretory phase with split bolus) were obtained with two different tube current-time products (280 mAs for the conventional-dose protocol and 70 mAs for the low-dose protocol) and the same tube voltage (90 kVp) for the two X-ray tubes. Iterative reconstruction was applied for both protocols. Two radiologists independently performed quantitative and qualitative image quality analysis and made diagnoses. The correlation between the noise level or the effective radiation dose and the patients' body weight was evaluated. Results Significantly higher noise levels resulting in a significantly lower liver signal-to-noise ratio and contrast-to-noise ratio were noted in low-dose images compared to conventional images (P  <  .001). Qualitative analysis by both radiologists showed significantly lower image quality in low-dose CT than in conventional CT images (P  <  .001). Patient's body weight was positively correlated with noise and effective radiation dose (P  <  .001). Diagnostic performance for various diseases, including urolithiasis, inflammation, and mass, was not different between the two protocols. Conclusions Despite inferior image quality, low-dose CT urography with 70 mAs and 90 kVp and iterative reconstruction demonstrated diagnostic performance equivalent to that of conventional CT for identifying various diseases of the urinary tract. Advances in knowledge Low-dose CT (25% radiation dose) with low tube current demonstrated diagnostic performance comparable to that of conventional CT for a variety of urinary tract diseases. [ABSTRACT FROM AUTHOR]

In the realm of few-shot classification learning, the judicious application of data augmentation methods has a significantly positive impact on classification performance. In the context of few-shot classification tasks for remote sensing images, the augmentation of features and the efficient utilization of limited features are of paramount importance. To address the performance degradation caused by challenges such as high interclass overlap and large intraclass variance in remote sensing image features, we present a data augmentation-based classification optimization method for few-shot remote sensing image scene classification. First, we construct a distortion magnitude space using different types of features, and we perform distortion adjustments on the support set samples while introducing an optimal search for the distortion magnitude (ODS) method. Then, the augmented support set offers a wide array of feature distortions in terms of types and degrees, significantly enhancing the generalization of intrasample features. Subsequently, we devise a dual-path classification (DC) decision strategy, effectively leveraging the discriminative information provided by the postdistortion features to further reduce the likelihood of classification errors. Finally, we evaluate the proposed method using a widely used remote sensing dataset. Our experimental results demonstrate that our approach outperforms benchmark methods, achieving improved classification accuracy. [ABSTRACT FROM AUTHOR]

Fluorescence microscopes are indispensable tools for the life science research community. Nevertheless, the presence of optical component limitations, coupled with the maximum photon budget that the specimen can tolerate, inevitably leads to a decline in imaging quality and a lack of useful signals. Therefore, image restoration becomes essential for ensuring high-quality and accurate analyses. This paper presents the Wavelet-Enhanced Convolutional-Transformer (WECT), a novel deep learning technique developed specifically for the purpose of reducing noise in microscopy images and attaining super-resolution. Unlike traditional approaches, WECT integrates wavelet transform and inverse-transform for multi-resolution image decomposition and reconstruction, resulting in an expanded receptive field for the network without compromising information integrity. Subsequently, multiple consecutive parallel CNN-Transformer modules are utilized to collaboratively model local and global dependencies, thus facilitating the extraction of more comprehensive and diversified deep features. In addition, the incorporation of generative adversarial networks (GANs) into WECT enhances its capacity to generate high perceptual quality microscopic images. Extensive experiments have demonstrated that the WECT framework outperforms current state-of-the-art restoration methods on real fluorescence microscopy data under various imaging modalities and conditions, in terms of quantitative and qualitative analysis. [ABSTRACT FROM AUTHOR]

Background: Low-dose CT (LDCT) chest protocols have widespread clinical applications for many indications; as a result, there is a need for protocol assessment prior to standardization. Dalhousie University and Oslo Metropolitan University have a formally established cooperative relationship. Purpose: The purpose is to assess radiation dose and image quality for LDCT chest protocols in seven different hospital locations in Norway and Canada. Material and methods: Retrospective dosimetry data, volumetric CT dose index (CTDIvol), and dose length product (DLP) from 240 average-sized patients as well as CT protocol parameters were included in the survey. Effective dose (ED) and size-specific dose estimate (SSDE) were calculated for each examination. For a quantitative image quality analysis, noise, CT number, and signal-to-noise ratio (SNR) were determined for three regions in the chest. The contrast-to-noise ratio (CNR) was calculated for lung parenchyma in comparison to the subcutaneous fat. Differences in dose and image quality were evaluated by a single-factor ANOVA test. A two-sample t -test was performed to determine differences in means between individual scanners. Results: The ANOVA test revealed significant differences (p <.05) in dose values for all scanners, including identical scanner models. Statistically significant differences (p <.05) were determined in mean values of the SNR distributions between the scanners in all three measured regions in the chest, as well as the CNR values. Conclusion: The observed variations in dose and image quality measurements, even within the same hospitals and between identical scanner models, indicate a potential for protocol optimization in the involved hospitals in both countries. [ABSTRACT FROM AUTHOR]

We present estimated images of a hypothetical satellite system in the presence of Seidel aberrations. We exemplified the effects of defocus, spherical, coma, astigmatism, field curvature, and distortion aberrations. Our analysis methods allow the investigation of aberration effects individually and in any combined form. Thereby, we introduce a potentially rich analysis dimension for the imaging chain, image processing, and image quality analyses. For a number of degrees of aberrations, we provide image estimates, show and quantify how individual (field‐dependent) aberrations would introduce degradations on the satellite images. We got correlation coefficient values for defocus, spherical, coma, astigmatism, field curvature, and distortion aberrations as 0.69, 0.71, 0.66, 0.79, 0.76, and 0.60, respectively. Thereby, we identfied the significance order of Seidel aberrations for this system by using image simulations and image similarity measures. [ABSTRACT FROM AUTHOR]

Purpose: Standardized uptake value (SUV) has been prevalently used to measure [68 Ga]Ga-PSMA-11 activity in prostate cancer, but it is susceptible to multiple factors. Parametric imaging allows for absolute quantification of tracer uptake and provides a better diagnostic accuracy that is crucial for lesion detection. However, the clinical significance of total-body parametric imaging of [68 Ga]Ga-PSMA-11 remains to be fully assessed. Therefore, the aim of our study is to delve into the diagnostic implications of total-body parametric imaging of [68 Ga]Ga-PSMA-11 PET/CT for patients with prostate cancer. Methods: Twenty prostate cancer patients were included and underwent a dynamic total-body [68 Ga]Ga-PSMA-11 PET/CT scan. An irreversible two-tissue compartment model (2T3k) was fitted for each tissue time-to-activity curve, and the net influx rate (Ki) was obtained. The image quality and semi-quantitative analysis of lesion-to-background ratio (LBR), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were compared between parametric images and SUV images. Results: Kinetic modeling using 2T3k demonstrated favorable model fitting in both normal organs and lesions. All of the lesions detected on SUV images (55–60 min) could be detected on Ki images. The correlation between Ki, SUVmean, and SUVmax in both normal organs and pathological lesions was found to be positive and statistically significant. Conversely, a moderate positive correlations were found between Ki and K1 (R = 0.69, P < 0.001; R = 0.61, P < 0.001) and Ki and k3 (R = 0.69, P < 0.001; R = 0.62, P < 0.001), in normal organs and pathological lesions, respectively. Visual assessment in Ki images showed less image noise and higher lesions conspicuity compared to SUV images. Ki image-derived LBR, SNR, and CBR of pathological lesions including primary tumors (PTs), lymph node metastases (LNMs) and bone metastases (BMs), exhibited remarkably higher folds (1.4–3.6 folds) compared to those derived from SUV of corresponding lesions. Conclusions: Total-body parametric imaging of [68 Ga]Ga-PSMA-11 enhanced lesion contrast and improved lesion detectability compared to SUV images. This may potentially serve as an imaging biomarker and theranostic tool for precise diagnosis and treatment evaluation in prostate cancer patients. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study is to prospectively compare quantitative and subjective image quality, scanning time, and diagnostic confidence between a new deep learning-based reconstruction(DLR) algorithm and standard MRI protocol of lumbar spine. Materials and methods: Eighty healthy volunteers underwent 1.5T MRI examination of lumbar spine from September 2021 to May 2023. Protocol acquisition comprised sagittal T1- and T2-weighted fast spin echo and short-tau inversion recovery images and axial multislices T2-weighted fast spin echo images. All sequences were acquired with both DLR algorithm and standard protocols. Two radiologists, blinded to the reconstruction technique, performed quantitative and qualitative image quality analysis in consensus reading; diagnostic confidence was also assessed. Quantitative image quality analysis was assessed by calculating signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Qualitative image quality analysis and diagnostic confidence were assessed with a five-point Likert scale. Scanning times were also compared. Results: DLR SNR was higher in all sequences (all p<0.001). CNR of the DLR was superior to conventional dataset only for axial and sagittal T2-weighted fast spin echo images (p<0.001). Qualitative analysis showed DLR had higher overall quality in all sequences (all p<0.001), with an inter-rater agreement of 0.83 (0.78–0.86). DLR total protocol scanning time was lower compared to standard protocol (6:26 vs 12:59 min, p<0.001). Diagnostic confidence for DLR algorithm was not inferior to standard protocol. Conclusion: DLR applied to 1.5T MRI is a feasible method for lumbar spine imaging providing morphologic sequences with higher image quality and similar diagnostic confidence compared with standard protocol, enabling a remarkable time saving (up to 50%). [ABSTRACT FROM AUTHOR]

Background: Chest computed tomography (CT) image quality impacts radiologists' diagnoses. Pre-diagnostic image quality assessment is essential but labor-intensive and may have human limitations (fatigue, perceptual biases, and cognitive biases). This study aims to develop and validate a deep learning (DL)-driven multi-view multi-task image quality assessment (M 2 IQA) method for assessing the quality of chest CT images in patients, to determine if they are suitable for assessing the patient's physical condition. Methods: This retrospective study utilizes and analyzes chest CT images from 327 patients. Among them, 1613 images from 286 patients are used for model training and validation, while the remaining 41 patients are reserved as an additional test set for conducting ablation studies, comparative studies, and observer studies. The M 2 IQA method is driven by DL technology and employs a multi-view fusion strategy, which incorporates three scanning planes (coronal, axial, and sagittal). It assesses image quality for multiple tasks, including inspiration evaluation, position evaluation, radiation protection evaluation, and artifact evaluation. Four algorithms (pixel threshold, neural statistics, region measurement, and distance measurement) have been proposed, each tailored for specific evaluation tasks, with the aim of optimizing the evaluation performance of the M 2 IQA method. Results: In the additional test set, the M 2 IQA method achieved 87% precision, 93% sensitivity, 69% specificity, and a 0.90 F1-score. Extensive ablation and comparative studies have demonstrated the effectiveness of the proposed algorithms and the generalization performance of the proposed method across various assessment tasks. Conclusion: This study develops and validates a DL-driven M 2 IQA method, complemented by four proposed algorithms. It holds great promise in automating the assessment of chest CT image quality. The performance of this method, as well as the effectiveness of the four algorithms, is demonstrated on an additional test set. [ABSTRACT FROM AUTHOR]

In biomedical imaging, it is desirable that custom-made accessories for restraint, anesthesia, and monitoring can be easily cleaned and not interfere with the imaging quality or analyses. With the rise of 3D printing as a form of rapid prototyping or manufacturing for imaging tools and accessories, it is important to understand which printable materials are durable and not likely to interfere with imaging applications. Here, 15 3D printable materials were evaluated for radiodensity, optical properties, simulated wear, and capacity for repeated cleaning and disinfection. Materials that were durable, easily cleaned, and not expected to interfere with CT, PET, or optical imaging applications were identified. A guide for selecting 3D printed materials for custom research tools through characterization of their merits and limitations in biomedical imaging. [ABSTRACT FROM AUTHOR]

Digital outcrop models (DOMs) have revolutionized the way twenty-first century geoscientists work. DOMs are georeferenced three-dimensional (3-D) digital representations of outcrops that facilitate quantitative work on outcrops at various scales. Outcrop digitalization has been traditionally conducted using laser scanners, but in the past decade, it has seen an exponential growth because of efficient and consumer-friendly structure-from-motion (SfM) algorithms concurrent with the rapid development of cost-effective aerial drones with high-resolution onboard cameras. While DOMs are routinely used in geoscientific research, education, and industry, enhanced DOM usage is restricted because raw data (e.g., photographs) and metadata are often incomplete and/or unavailable. In this contribution, we present the Svalbox Digital Model Database (Svalbox DMDb), a database of metadata and openly available data packages for individual DOMs. The Svalbox DMDb is a regional DOM database geographically constrained to the Norwegian High Arctic archipelago of Svalbard at 74°N-81°N and 10°E-35°E. Svalbard offers exceptional-quality, vegetation-free outcrops with a wide range of lithologies and tectono-magmatic styles, including extension, compression, and magmatism. Data and metadata of the systematically digitalized outcrops across Svalbard are shared according to FAIR principles through the Svalbox DMDb. Fully open-access and downloadable DOMs include not just the DOMs themselves, but also the input data, processing reports and projects, and other data products such as footprints and orthomosaics. Rich metadata for each DOM include both the technical and geological parameters (metadata), enabling visualization and integration with regional geoscientific data available through the Norwegian Polar Institute and the Svalbox online portal. The current release of Svalbox DMDb, documented in this contribution, covers 135 DOMs cumulatively covering 114 km2 of Proterozoic to Cenozoic stratigraphy. [ABSTRACT FROM AUTHOR]

Background: Ferumoxytol is becoming more widely used as an off-label blood-pool contrast agent for MR angiography (MRA) and four-dimensional (4D) flow imaging in pediatric cardiovascular disease. Brand and generic versions of ferumoxytol are available with no information on relative efficacy as a contrast agent and safety profiles. Objective: This study evaluates patient safety and image quality of comparable dosages of generic ferumoxytol (GF) versus brand ferumoxytol (BF) with the following hypotheses: (1) Reducing the contrast dosage from 3 to 2 mg/kg will not affect imaging quality and diagnostic accuracy of MRA and four-dimensional 4D flow. (2) GF and BF have similar image quality. (3) GF and BF have similar patient safety profiles. Materials and methods: In an IRB-approved retrospective study, changes in vitals/clinical status between baseline, during infusion, and 30 min post-infusion were analyzed in 3 groups: group 1 (3 mg/kg BF, 216 patients, age: 19.29 ± 11.71 years ranging from 2 months to 62 years), group 2 (2 mg/kg BF, 47 patients, age: 15.35 ± 8.56 years ranging from 10 days to 41 years), and group 3 (2 mg/kg GF, 127 patients, age: 17.16 ± 12.18 years ranging from 6 days to 58 years). Both pediatric and adult patients with congenital heart disease (CHD) indications were included within the study. Adverse reactions were classified as mild, moderate, or severe. Quantitative analysis of MR image quality was performed with signal-to-noise ratio (SNR) on MRA and velocity-to-noise ratio (VNR) on 4D flow. Qualitative grading of imaging features was performed by 2 experienced observers. Two-way analysis of variance (ANOVA) and chi-square tests were used for comparison with a P value of ≤ 0.05 used for significance. Results: No statistical difference was found in clinical status and vital signs (P>0.05). No severe reactions were reported. 7.9% of GF patients experienced an adverse reaction compared to 2.3% with 3 mg/kg BF and 8.4% with 2 mg/kg BF. There was no statistical difference in SNR between the 3 groups (P>0.05). For 4D flow, 2 mg/kg GF demonstrated an increase in VNR compared to 2 mg/kg BF (P = 0.005). The qualitative scores for MRA and 4D flow were high (≥ 3) across all 3 groups. Conclusions: No significant difference was identified between 2 mg/kg GF and BF in terms of safety profile and image quality. Given the small sample size of this study, further studies are required to confirm these results. [ABSTRACT FROM AUTHOR]

Background: With the introduction of prostate specific membrane antigen (PSMA) PET/CT, the detection rate of prostate cancer metastases has improved significantly, both for primary staging and for biochemical recurrence. EANM/SNMMI guidelines recommend a 60 min time interval between [68Ga]Ga‐PSMA administration and acquisition. Purpose: This study evaluates the possibility of a shorter time interval by investigating the dynamic change in image quality measures. Method: We retrospectively analyzed 10 consecutive prostate cancer patients who underwent a dynamic whole body [68Ga]Ga‐PSMA‐11 PET/CT of 75 min from skull vertex to mid‐thigh using Siemens FlowMotion. PET images were acquired directly after injection of 1.5 MBq/kg [68Ga]Ga‐PSMA‐11. Image quality measures included lesion maximum standardized uptake value corrected for lean body mass (SULmax), tumor‐to‐background ratio (TBR), and contrast‐to‐noise ratio (CNR). Quantitative analysis of image quality in dynamic PET was performed using PMOD (version 4.2). Regions of interest (ROIs), drawn included different types of prostate lesions (primary tumor, lymph nodes, and bone metastasis), organ tissue (liver, spleen, lacrimal gland, submandibular gland, parotid gland, urinary bladder, kidneys blood pool [ascending aorta], left ventricle), bone tissue (4th lumbar vertebral body [L4]) and muscle tissue (gluteus maximus). To further investigate image quality four 10 min multi‐frame reconstructions with clinical parameters were made at different post‐injection times (15, 30, 45, and 60 min). A nuclear medicine physician performed a blinded lesion detectability evaluation on these multi‐frame reconstructions for different prostate cancer lesions. Results: Six primary prostate tumors in seven patients with prostate in situ, 13 lymph node metastases in six patients and up to 12 bone metastases in three patients were found. The different prostate lesion types (lymph nodes metastases, bone metastases, and primary prostate tumor) all show an increase in average SULmax, TBR, and CNR over time during the scan. The normalized average SULmax, TBR, and CNR of the combined prostate lesions at 15, 30, and 45 min post‐injection scans were all significant p < 0.05 lower from the 60 min post‐injection [68Ga]Ga‐PSMA‐11 PET/CT (9.5 ± 4.5, 12.7 ± 6.2, and 41.8 ± 24.5, respectively). At patient level, the reader concluded the same regarding the presence/absence of primary prostate cancer recurrence, lymph node metastases, and/or bone metastases on all <60 min post‐injection [68Ga]Ga‐PSMA‐11 PET/CT's in comparison to the reference scan (60 min post‐injection). At lesion level, all bone metastases seen on the reference scan were also seen on all <60 min post‐injection [68Ga]Ga‐PSMA‐11 PET/CT's but there were some lymph nodes (n = 2) metastases missed on the 15, 30, and 45 min post‐injection scans. One lymph node metastasis on both the 15 and 30 min post‐injection [68Ga]Ga‐PSMA‐11 PET/CT's was missed and one lymph node metastasis was missed, only on the 45 min post‐injection [68Ga]Ga‐PSMA‐11 PET/CT. Conclusion: Shorter post‐injection times (15, 30, and 45 min) compared to the recommended post‐injection time of 60 min are not optimal. However, the impact of a shorter time interval of 45 min instead of 60 min between [68Ga]Ga‐PSMA‐11 administration and the start of PET/CT acquisition on both image quality (SULmax, TBR, and CNR) and lesion detection, while significant, is small. [ABSTRACT FROM AUTHOR]

In this paper, we present an unsupervised, completely blind, no-reference (NR) stereoscopic (S3D) image quality prediction model to assess the perceptual quality of natural S3D images. We study the joint dependencies between color and depth features of S3D images and empirically model these dependencies by using a bivariate generalized Gaussian distribution (BGGD). We compute the parameters of BGGD, and we also obtain the determinant and the coherence values from the covariance matrix of the proposed BGGD model. We extract the features of BGGD model and covariance matrix from the reference S3D image, followed by multivariate Gaussian (MVG) distribution modeling on the predicted features of the reference. We estimate the joint color and depth quality of the S3D images by computing the likelihood of the image features with respect to the reference MVG model. We apply the popular 2D unsupervised NIQE model on individual stereo views to estimate the overall spatial quality of the S3D images. Finally, we pool the likelihood scores and the spatial NIQE scores to achieve the estimation for the overall perceived quality of the S3D images. The performance of the proposed model is evaluated on the MICT, LIVE Phase I and II S3D image datasets. The results indicate consistent and robust performance for all datasets. Our proposed estimator is completely blind, as it requires neither training on subjective scores nor reference S3D images. [ABSTRACT FROM AUTHOR]

Scattered light from the objective lens, directly exposed to the intense sunlight, is a dominant source of stray light in internally occulted coronagraphs. The variable stray light, such as the scatter from dust on the objective lens, can produce varying scattering backgrounds in coronal images, significantly impacting image quality and data analysis. Using data acquired by the Lijiang 10-cm Coronagraph, the quantitative relationship between the distribution of dust on the objective lens and the resulting scattering background is analyzed. Two empirical models for the scattering background are derived, and used to correct the raw coronal data. The second model, which depends on three parameters and performs better, shows that the scattering-background distribution varies with angle, weakens with increasing height, and enhances with increasing dust level on the objective lens. Moreover, we find that the dust on the center of the objective lens can contribute more significantly to the scattering background than on the edge. This study not only quantitatively confirms the significant impact of the stray light produced by dust on the objective lens of the coronagraph, but also corrects the coronal data with this stray light for the first time. Correcting for dust-scattered light is crucial for the high-precision calibration of ground-based coronagraph data, enabling a more accurate analysis of coronal structures. Furthermore, our model is envisioned to support the provision of reliable observational data for future routine coronal magnetic-field measurements using ground-based coronagraphs. [ABSTRACT FROM AUTHOR]

Compressed sensing is commonly concerned with optimizing the image quality after a partial undersampling of the measurable k -space to accelerate MRI. In this article, we propose to change the focus from the quality of the reconstructed image to the quality of the downstream image analysis outcome. Specifically, we propose to optimize the patterns according to how well a sought-after pathology could be detected or localized in the reconstructed images. We find the optimal undersampling patterns in k -space that maximize target value functions of interest in commonplace medical vision problems (reconstruction, segmentation, and classification) and propose a new iterative gradient sampling routine universally suitable for these tasks. We validate the proposed MRI acceleration paradigm on three classical medical datasets, demonstrating a noticeable improvement of the target metrics at the high acceleration factors (for the segmentation problem at ×16 acceleration, we report up to 12% improvement in Dice score over the other undersampling patterns). • New paradigm for accelerating MRI, where k-space is undersampled intelligently for the benefit of downstream image analysis. • In this paradigm, the learned pattern can ruin the look of the image, while boosting the target metrics. • Our iterative gradient sampling (IGS) algorithm optimizes undersampling patterns for specific medical vision tasks and applications (cardiac, neurological, and orthopedic utility confirmed). • Noticeable improvement to target value metrics compared to the other undersampling patterns, such as equispaced or central masks. • The method proves especially instrumental at the highest acceleration factors when good image quality is impossible to obtain. [ABSTRACT FROM AUTHOR]

Objective assessment of image quality seeks to predict image quality without human perception. Given that the ultimate goal of a blind/no-reference image quality assessment (BIQA) algorithm is to provide a score consistent with the subject's prediction, it makes sense to design an algorithm that resembles human behavior. Recently, a large number of image features have been introduced to image quality assessment. However, only a few of these features are generated by using the computational mechanisms of the visual cortex. In this paper, we propose bioinspired algorithms to extract image features for BIQA by simulating the visual cortex. We extract spatial features like texture and energy from images by mimicking the retinal circuit. We extract spatial-frequency features from images by imitating the simple cell of the primary visual cortex. And we extract color features from images by employing the color opponent mechanism of the biological vision system. Then, by using the statistical features derived from these physiologically plausible features, we train a support vector regression model to predict image quality. The experimental results show that the proposed algorithm is more consistent with subjective evaluations than the comparison algorithms in predicting image quality. [ABSTRACT FROM AUTHOR]

To evaluate the effect of the deep learning model reconstruction (DLM) method in terms of image quality and diagnostic agreement in low-dose computed tomography (LDCT) for interstitial lung disease (ILD), 193 patients who underwent LDCT for suspected ILD were retrospectively reviewed. Datasets were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction Veo (ASiR-V), and DLM. For image quality analysis, the signal, noise, signal-to-noise ratio (SNR), blind/referenceless image spatial quality evaluator (BRISQUE), and visual scoring were evaluated. Also, CT patterns of usual interstitial pneumonia (UIP) were classified according to the 2022 idiopathic pulmonary fibrosis (IPF) diagnostic criteria. The differences between CT images subjected to FBP, ASiR-V 30%, and DLM were evaluated. The image noise and BRISQUE scores of DLM images was lower and SNR was higher than that of the ASiR-V and FBP images (ASiR-V vs. DLM, p < 0.001 and FBP vs. DLR-M, p < 0.001, respectively). The agreement of the diagnostic categorization of IPF between the three reconstruction methods was almost perfect (κ = 0.992, CI 0.990–0.994). Image quality was improved with DLM compared to ASiR-V and FBP. [ABSTRACT FROM AUTHOR]

Background: To evaluate if coronary CT angiography (CCTA) monoenergetic reconstructions, obtained with a dual-layer spectral detector computed tomography (DLCT) system, offer improved image quality compared with 120 kVp conventional images without affecting the quantitative assessment of coronary stenoses. Methods: Fifty CCTA datasets (30 men; mean age: 61.6 ± 12.3 years) acquired with a DLCT system were reconstructed using virtual monoenergetic images (VMI) from 40 to 100 keV with 10 keV increment and compared with conventional images. An analysis of objective image quality was performed, evaluating the signal- and contrast-to-noise ratio. For the subjective assessment, two readers used a 5-point Likert scoring system to evaluate sharpness, noise, demarcation of coronary plaques, vascular contrast, and an overall score. Furthermore, coronary stenoses were analyzed for each vessel to describe the diagnostic agreement between monoenergetic images and conventional images. Results: The objective image analysis showed that all reconstructions from 70 keV to 40 keV show higher SNR (from 61.33 ± 12.46 to 154.22 ± 42.91, respectively) and CNR (from 51.45 ± 11.19 to 135.63 ± 39.38, respectively) compared with conventional images (all p < 0.001). The 40 keV monoenergetic images obtained the best average score for sharpness, vascular contrast, and for the overall impression (all with p < 0.001). The detection and grading of stenoses of the coronary arteries with conventional and monoenergetic images at 70 keV and 40 keV showed an overall excellent interobserver agreement (k= 0.81 [0.72–0.91]). Conclusions: The 40 keV virtual monoenergetic images obtained with a DLCT system allow the objective and subjective image quality of coronary CT angiography to be improved. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate image quality, diagnostic acceptability, and lesion conspicuity in abdominal dual-energy CT (DECT) using deep learning image reconstruction (DLIR) compared to those using adaptive statistical iterative reconstruction-V (Asir-V) at 50% blending (AV-50), and to identify potential factors impacting lesion conspicuity. Methods: The portal-venous phase scans in abdominal DECT of 47 participants with 84 lesions were prospectively included. The raw data were reconstructed to virtual monoenergetic image (VMI) at 50 keV using filtered back-projection (FBP), AV-50, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H). A noise power spectrum (NPS) was generated. CT number and standard deviation values of eight anatomical sites were measured. Signal-to-noise (SNR), and contrast-to-noise ratio (CNR) values were calculated. Five radiologists assessed image quality in terms of image contrast, image noise, image sharpness, artificial sensation, and diagnostic acceptability, and evaluated the lesion conspicuity. Results: DLIR further reduced image noise (p < 0.001) compared to AV-50 while better preserved the average NPS frequency (p < 0.001). DLIR maintained CT number values (p > 0.99) and improved SNR and CNR values compared to AV-50 (p < 0.001). DLIR-H and DLIR-M showed higher ratings in all image quality analyses than AV-50 (p < 0.001). DLIR-H provided significantly better lesion conspicuity than AV-50 and DLIR-M regardless of lesion size, relative CT attenuation to surrounding tissue, or clinical purpose (p < 0.05). Conclusions: DLIR-H could be safely recommended for routine low-keV VMI reconstruction in daily contrast-enhanced abdominal DECT to improve image quality, diagnostic acceptability, and lesion conspicuity. Key Points: • DLIR is superior to AV-50 in noise reduction, with less shifts of the average spatial frequency of NPS towards low frequency, and larger improvements of NPS noise, noise peak, SNR, and CNR values. • DLIR-M and DLIR-H generate better image quality in terms of image contrast, noise, sharpness, artificial sensation, and diagnostic acceptability than AV-50, while DLIR-H provides better lesion conspicuity than AV-50 and DLIR-M. • DLIR-H could be safely recommended as a new standard for routine low-keV VMI reconstruction in contrast-enhanced abdominal DECT to provide better lesion conspicuity and better image quality than the standard AV-50. [ABSTRACT FROM AUTHOR]

Morphological parameters of substrate surfaces determine the adhesion of coatings and pressure-sensitive adhesives. This paper is concerned with the assessment of fractal parameters by means of cross-section images and contact stylus instrument measurements of blast-cleaned substrates. Mild steel samples were prepared with different abrasive materials. Images were taken from a total of 12 surface configurations with an optical microscope. From each of the cross-section, a 2D profile of the steel surface was derived using the ImageJ image manipulation software. The fractal dimensions of these profiles were estimated by means of the box-counting method and then compared fractal values for 2D profiles taken from contact stylus measurements. Design of Experiment (DoE) was applied to statistically analyze the relationships. The factors considered included abrasive type, surface preparation grade, surface roughness, and most importantly for this paper, the surface profile evaluation method. The profile evaluation methods included cross-section analysis and contact stylus instrument measurements. Regardless of the method used to determine the surface profile, identical relative trends resulted for both measurement variants, with normalised values approximately twice as high for the cross-section profiles compared to the ones from contact stylus data. [ABSTRACT FROM AUTHOR]

(1) Purpose: to compare right ventricular (RV) functional parameters in children with surgically repaired congenital heart disease (CHD) using single/double breath hold (BH) and free-breathing (FB) real-time compressed sensing (CS) cine cardiac magnetic resonance (cMRI) with standard retrospective segmented multi breath hold (RMB) cine cMRI. (2) Methods: Twenty patients with CHD underwent BH and FB, as well as RMB cine cMRI, at 3T to obtain a stack of continuous axial images of the RV. Two radiologists independently performed qualitative analysis of the image quality (rated on a 5-point scale; 1 = non-diagnostic to 5 = excellent) and quantitative analysis of the RV volume measurements. (3) Results: The best image quality was provided by RMB (4.5; range 2–5) compared to BH (3.9; range 3–5; p = 0.04) and FB (3.6; range 3–5; p < 0.01). The RV functional parameters were comparable among BH, FB, and RMB with a difference of less than 5%. The scan times for BH (44 ± 38 s, p < 0.01) and FB (24 ± 7 s, p < 0.01) were significantly reduced compared to for RMB (261 ± 68 s). (4) Conclusions: CS-FB and CS-BH real-time cine cMRI in children with CHD provides diagnostic image quality with excellent accuracy for measuring RV function with a significantly reduced scan time compared to RMB. [ABSTRACT FROM AUTHOR]

A novel unsupervised blind image quality assessment (BIQA) method, which requires no mean opinion scores for model training is presented in this paper. The method employs joint spatial and transform features as quality degradation metrics, specifically, phase congruency, gradient magnitude (GM), and GM and Laplacian of Gaussian response and local normalized coefficient are extracted as spatial features, and Karhunen–Loéve transform coefficient and discrete cosine transform coefficient are modeled as transform features. Both spatial and transform features are well analyzed to remove the redundancy, and then fitted to the multivariate Gaussian model for no-reference image quality assessment. Extensive experiments conducted on seven IQA databases demonstrate the superiority of the proposed method over the state-of-the-art both supervised and unsupervised BIQA methods. [ABSTRACT FROM AUTHOR]

Objectives: To compare high-pitch spiral (HPS) and prospectively-gated step-and-shoot (SAS) coronary CT angiography (CCTA) using third generation dual-source CT regarding objective and subjective image quality parameters and radiation exposure. Methods: Eighty pairs of patients matched for gender, age, heart rate and BMI were enrolled in this retrospective study. High-pitch spiral and prospectively ECG-gated sequential CCTA were performed using third generation dual-source CT. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in the left ventricle were calculated for each group. Image quality were also scored using four-point scale. Student t-test was used to compare SNR, CNR and mean effective dose values (ED) and Wilcoxon test was used to compare image quality scores. Interrater aggreement were evaluated using Cohen's kappa statistics. Results: Between-group differences in terms of age, gender, BMI, heart rate, and Agatston score were statistically not significant. Mean SNR and CNR was higher in prospective SAS protocol (16.5 ± 6.2 vs. 14.7 ± 4.9, p = 0.047 and 13.0 ± 5.2 vs. 11.2 ± 4.3, p = 0.02). Image quality scores showed no significant difference between two scan protocols (p > 0.05). Regarding radiation exposure, CT dose index (CTDIvol), dose length product (DLP) and ED was significantly lower for high-pitch group (p < 0.0001). Conclusions: HPS CCTA using DSCT enables > 70% dose reduction while maintaining the image quality compared to prospectively ECG-gated SAS protocol. Therefore, HPS CCTA protocol can be preferred in patients appropriate for prospective ECG-triggered protocol. [ABSTRACT FROM AUTHOR]

Background: The current paradigm for evaluating computed tomography (CT) system performance relies on a task‐based approach. As the Hotelling observer (HO) provides an upper bound of observer performances in specific signal detection tasks, the literature advocates HO use for optimization purposes. However, computing the HO requires calculating the inverse of the image covariance matrix, which is often intractable in medical applications. As an alternative, dimensionality reduction has been extensively investigated to extract the task‐relevant features from the raw images. This can be achieved by using channels, which yields the channelized‐HO (CHO). The channels are only considered efficient when the channelized observer (CO) can approximate its unconstrained counterpart. Previous work has demonstrated that supervised learning‐based methods can usually benefit CO design, either for generating efficient channels using partial least squares (PLS) or for replacing the Hotelling detector with machine‐learning (ML) methods. Purpose: Here we investigated the efficiency of a supervised ML‐algorithm used to design a CO for predicting the performance of unconstrained HO. The ML‐algorithm was applied either (1) in the estimator for dimensionality reduction, or (2) in the detector function. Methods: A channelized support vector machine (CSVM) was employed and compared against the CHO in terms of ability to predict HO performances. Both the CSVM and the CHO were estimated with channels derived from the singular value decomposition (SVD) of the system operator, principal component analysis (PCA), and PLS. The huge variety of regularization strategies proposed by CT system vendors for statistical image reconstruction (SIR) make the generalization capability of an observer a key point to consider upfront of implementation in clinical practice. To evaluate the generalization properties of the observers, we adopted a 2‐step testing process: (1) achieved with the same regularization strategy (as in the training phase) and (2) performed using different reconstruction properties. We generated simulated‐ signal‐known‐exactly/background‐known‐exactly (SKE/BKE) tasks in which different noise structures were generated using Markov random field (MRF) regularizations using either a Green or a quadratic, function. Results: The CSVM outperformed the CHO for all types of channels and regularization strategies. Furthermore, even though both COs generalized well to images reconstructed with the same regularization strategy as the images considered in the training phase, the CHO failed to generalize to images reconstructed differently whereas the CSVM managed to successfully generalize. Lastly, the proposed CSVM observer used with PCA channels outperformed the CHO with PLS channels while using a smaller training data set. Conclusion: These results argue for introducing the supervised‐learning paradigm in the detector function rather than in the operator of the channels when designing a CO to provide an accurate estimate of HO performance. The CSVM with PCA channels proposed here could be used as a surrogate for HO in image quality assessment. [ABSTRACT FROM AUTHOR]

Although extensive researches have carried out on visible and infrared images fusion, quality assessment of the fused image is still challenging due to the absence of the reference image. In this paper, a subjective benchmark dataset and a semi-reference objective assessment method based on a Transformer encoder–decoder framework named Semantic-Relation Transformer (SRT), are developed for Visible and Infrared Fused Image Quality Assessment (VIF-IQA). Different with existing Transformers, SRT decoder can extract multi-level source image features and adopts a Multi-Head Self-Evaluation (MHSE) block which is constructed to mine latent relation knowledge between the fused image and source images. The relation knowledge is then injected into 3D-token with deep semantic embedding of receptive regions. Finally, the objective assessment score is obtained from-token through linear mapping of local to global. Moreover, we meticulously select 4,000 fused images from 200 scenes in TNO, MSRS, M3FD and Road Scene datasets and create a Visible and Infrared fuSed qualiTy Assessment (VISTA) dataset, which is guided by Subjective VIF-IQA Specification rigorously. VISTA dataset is utilized to comprehensively validate the proposed SRT. The experimental results demonstrated that the output of SRT is more in keeping with subjective feelings. Moreover, SRT has state-of-the-art performance on quantitative metrics when compared with 12 popular methods and the output of SRT is more in keeping with subjective feelings. VISTA dataset is available at https://github.com/ChangeZH/VISTA-Dataset. • A subjective benchmark dataset called Visible and Infrared fuSed qualiTy Assessment is built. • The subjective fused image quality assessment specification is formulated. • A new Semantic Relation Transformer is proposed for fused image quality assessment. • Fusion quality is assessed in SRT through semantic embedding and relation knowledge. • MHSE blocks are advanced to explore the correlation between source and fused images. [ABSTRACT FROM AUTHOR]

As a fundamental research problem, blind image quality assessment (BIQA) has attracted increasing interest in recent years. Although great progress has been made, BIQA still remains a challenge. To better understand the research progress and challenges in this field, we review BIQA methods in this paper. First, we introduce the BIQA problem definition and related methods. Second, we provide a detailed review of the existing BIQA methods in terms of representative hand-crafted features, learning-based features and quality regressors for two-stage methods, as well as one-stage DNN models with various architectures. Moreover, we also present and analyze the performance of competing BIQA methods on six public IQA datasets. Finally, we conclude our paper with possible future research directions based on a performance analysis of the BIQA methods. This review will provide valuable references for researchers interested in the BIQA problem. [ABSTRACT FROM AUTHOR]