Level Of Image Quality Research Articles

Low-Dose Cone-Beam Computed Tomography in Swedish Pediatric Patients With Alveolar Clefts Following Alveolar Bone Grafting-A Clinical Study.

The aim of this study was to investigate whether a low-dose cone-beam computed tomography (CBCT) protocol provides diagnostically acceptable image quality for assessing bone healing after alveolar bone grafting. The study cohort comprised 11 patients (aged 7-14 years) with orofacial clefts who had undergone alveolar bone grafting at Skåne University Hospital in Malmö, Sweden. During the postsurgical follow-up at 6 months, each patient was assessed twice: once with a standard-dose CBCT protocol and once with a low-dose CBCT protocol, which in total corresponds to one CBCT examination made with the exposure settings recommended by the manufacturer. Among others, the assessed parameters included subjective image quality, as well as bone graft height, thickness, and integration. No significant differences were found between the standard- and low-dose protocols for most parameters (p > 0.05). Exceptions included subjective image quality (one observer, p = 0.05) and confidence levels during the assessment (three observers, p = 0.01, 0.01, 0.02). The low-dose protocol yielded adequate image quality for postoperative CBCT healing assessment in patients who have undergone alveolar bone grafting. However, the confidence level of observers during the assessment with the low-dose protocol was reduced. This study is registered on ClinicalTrials.gov (NCT06395077). This study is registered on ClinicalTrials.gov (NCT06395077).

Diagnostic performance of high and ultra-high-resolution photon counting CT for detection of coronary artery disease in patients evaluated for transcatheter aortic valve implantation.

We assessed the diagnostic performance of both ultra-high-resolution (UHR) and high-resolution (HR) modes of photon-counting detector (PCD)-CT within the confines of standard pre-TAVI CT scans, as well as the performance of UHR mode adjusted specifically for coronary imaging, using quantitative coronary angiography (QCA) as the reference. We included 60 patients undergoing pre-TAVI planning CT scans. Patients were divided into 3 groups: 20 scanned in HR mode, 20 in UHR mode, and 20 in adjusted UHR mode, on a dual-source PCD-CT. The adjusted UHR mode employed a lower tube voltage (90kV vs. 120kV) and a higher image quality level (65 vs. 34) to enhance coronary artery visualization. Patients underwent invasive coronary angiography as part of clinical routine. CCTA and QCA were reviewed to assess CAD presence defined as stenosis ≥ 50% in proximal and middle coronary segments. We included 60 patients (mean age 79 ± 7 years; 39(65%) men). Mean heart rate during scanning was 72 ± 13bpm. Median coronary calcium score was 973[379-2007]. QCA identified significant CAD in 24 patients (40%): 9 patients scanned with HR mode, 10 patients with the UHR mode, and 5 patients with the UHR adjusted mode. Per-patient area under the curves were 0.57 for HR, 0.80 for UHR, and 0.80 for adjusted UHR, with no significant differences between the scan modes, and per-vessel the area under the curves were 0.73 for HR, 0.69 for UHR, and 0.87 for adjusted UHR, with significant differences between UHR and adjusted UHR (p = 0.04). UHR and adjusted UHR modes of dual source PCD-CT show potential for improved sensitivity and negative predictive value for detecting CAD in patients undergoing pre-TAVI scans, however, no statistically significant difference from HR mode was observed.

Enhancement of Image Quality in Low-Field Knee MR Imaging Using Deep Learning.

The purpose of this study is to investigate the potential of deep learning (DL) techniques to enhance the image quality of low-field knee MR images, with the ultimate goal of approximating the standards ofhigh-field knee MR imaging. We analyzed knee MR images collected from 45 patients with knee disorders and six normal subjects using a 3T MR scannerand those collected from 25 patients with knee disorders using a 0.4T MR scanner. Two DL models were developed: a fat-suppression contrast-generation model and a super-resolution model. These DL models were trained using 3T knee MR imaging data and applied to 0.4T knee MR imaging data. Visual assessments of anatomical structures and image noise and abnormality detection with diagnostic confidence levels on the original 0.4T MR images and those afterDL enhancement were conducted by two board-certified radiologists. Statistical analyses were performed using McNemar's test and the Wilcoxon signed-rank test. DL-enhanced MR images significantly improved the depiction of anatomical structures and reduced image noise compared to the original MR images. The number of abnormal findings detected and the diagnostic confidence levels were higher in the DL-enhanced MR images, indicating the potential for more accurate diagnoses. DL techniques effectively enhance the image quality of low-field knee MR images by leveraging 3T MR imaging data. This enhancement significantly improves image quality and diagnostic confidence levels, making low-field MR images much more reliable for detecting abnormalities. This advancement offers a useful alternative for clinical settings, especially in resource-limited environments, without compromising diagnostic accuracy.

Vector quantization-driven image compression through multi-objective evolutionary algorithms

In recent years, information consumption among the population has increased, especially through the widespread use of digital images and videos. Consequently, the demand for efficient compression methods has escalated, aiming to facilitate both data storage and transmission while adapting to the unique requirements of diverse problems. The image compression problem has been explored in several works through the lens of vector quantization (VQ) by adopting a single-objective approach to achieve optimal quality results but with a fixed compression level. In contrast, this investigation introduces a groundbreaking multi-objective compression approach that simultaneously optimizes image quality and compression level. This is achieved by selecting the most representative information to construct an adequate codebook while minimizing its size. The proposed method was evaluated on a curated selection of well-known public domain images, using different types of multi-objective evolutionary algorithms, including Pareto-, indicator- and decomposition-based approaches. The compressed images produced were assessed through the application of established quality indicators drawn from the expansive realm of image processing literature, ensuring the reliability and validity of our findings. The results demonstrate the proposed method’s ability to adapt to the intrinsic characteristics of each image, removing the maximum amount of redundant information presented. Furthermore, the multi-objective nature of the proposed approach allows us to obtain solutions with different levels of trade-off between quality and compression, opening up new possibilities in the field and demonstrating the potential to address the evolving challenges posed by the escalating volume of information in the digital landscape.

Efficacy of compressed sensing and deep learning reconstruction for adult female pelvic MRI at 1.5 T

BackgroundWe aimed to determine the capabilities of compressed sensing (CS) and deep learning reconstruction (DLR) with those of conventional parallel imaging (PI) for improving image quality while reducing examination time on female pelvic 1.5-T magnetic resonance imaging (MRI).MethodsFifty-two consecutive female patients with various pelvic diseases underwent MRI with T1- and T2-weighted sequences using CS and PI. All CS data was reconstructed with and without DLR. Signal-to-noise ratio (SNR) of muscle and contrast-to-noise ratio (CNR) between fat tissue and iliac muscle on T1-weighted images (T1WI) and between myometrium and straight muscle on T2-weighted images (T2WI) were determined through region-of-interest measurements. Overall image quality (OIQ) and diagnostic confidence level (DCL) were evaluated on 5-point scales. SNRs and CNRs were compared using Tukey’s test, and qualitative indexes using the Wilcoxon signed-rank test.ResultsSNRs of T1WI and T2WI obtained using CS with DLR were higher than those using CS without DLR or conventional PI (p < 0.010). CNRs of T1WI and T2WI obtained using CS with DLR were higher than those using CS without DLR or conventional PI (p < 0.003). OIQ of T1WI and T2WI obtained using CS with DLR were higher than that using CS without DLR or conventional PI (p < 0.001). DCL of T2WI obtained using CS with DLR was higher than that using conventional PI or CS without DLR (p < 0.001).ConclusionCS with DLR provided better image quality and shorter examination time than those obtainable with PI for female pelvic 1.5-T MRI.Relevance statementCS with DLR can be considered effective for attaining better image quality and shorter examination time for female pelvic MRI at 1.5 T compared with those obtainable with PI.Key PointsPatients underwent MRI with T1- and T2-weighted sequences using CS and PI.All CS data was reconstructed with and without DLR.CS with DLR allowed for examination times significantly shorter than those of PI and provided significantly higher signal- and CNRs, as well as OIQ.Graphical

BrainAgeNeXt: Advancing Brain Age Modeling for Individuals with Multiple Sclerosis.

Aging is associated with structural brain changes, cognitive decline, and neurodegenerative diseases. Brain age, an imaging biomarker sensitive to deviations from healthy aging, offers insights into structural aging variations and is a potential prognostic biomarker in neurodegenerative conditions. This study introduces BrainAgeNeXt, a novel convolutional neural network inspired by the MedNeXt framework, designed to predict brain age from T1-weighted magnetic resonance imaging (MRI) scans. BrainAgeNeXt was trained and validated on 11,574 MRI scans from 33 private and publicly available datasets of healthy volunteers, aged 5 to 95 years, imaged with 3T and 7T MRI. Performance was compared against three state-of-the-art brain age prediction methods. BrainAgeNeXt achieved a mean absolute error (MAE) of 2.78 ± 3.64 years, lower than the compared methods (MAE = 3.55, 3.59, and 4.16 years, respectively). We tested all methods also across different levels of image quality, and BrainAgeNeXt performed well even with motion artifacts and less common 7T MRI data. In three longitudinal multiple sclerosis (MS) cohorts (273 individuals), brain age was, on average, 4.21 ± 6.51 years greater than chronological age. Longitudinal analysis indicated that brain age increased by 1.15 years per chronological year in individuals with MS (95% CI = [1.05, 1.26]). Moreover, in early MS, individuals with worsening disability had a higher annual increase in brain age compared to those with stable clinical assessments (1.24 vs. 0.75, p < 0.01). These findings suggest that brain age is a promising prognostic biomarker for MS progression and potentially a valuable endpoint for clinical trials.

Quantification accuracy in photon-countingdetector CT for coronary artery calcium score: a pilot study.

To validate the accuracy of coronary artery calcium score (CACS) using photon-counting detector (PCD) CT under various scanning settings and explore the optimized scanning settings considering both the accuracy and the radiation dose. A CACS phantom containing six hollow cylindrical hydroxyapatite calcifications of two sizes with three densities and 12 patients underwent CACS scans. For PCD-CT, two scanning modes (sequence and flash [high-pitch spiral mode]) and five tube voltages (90kV, 120kV, 140kV, Sn100kV, and Sn140kV) at different image quality (IQ) levels were set for phantom, and patients were scanned with 120kV at IQ19 using flash mode. All acquisitions from PCD-CT were reconstructed at 70keV. Acquisitions in sequence mode at 120kV on an energy-integrating detector CT (EID-CT) was used as thereference. Agatston, mass, and volume scores were calculated. The CACS from PCD-CT exhibited excellent agreements with the reference (all intraclass correlation coefficient [ICC] > 0.99). The root mean square error (RMSE) between the Agatston score acquired from PCD-CT and the reference (5.4-11.5) was small. A radiation dose reduction (16-75%) from PCD-CT compared with the reference was obtained in all protocols using flash mode, albeit with IQ20 only at sequence mode (22-44%). For the patients, ICC ( all ICC > 0.98) and Bland-Altman analysis of CACS all showed high agreements between PCD-CT and the reference, without reclassifying CACS categories(P = 0.317). PCD-CT yields repeatable and accurate CACS across diverse scanning protocols according to our pilot study. Sn100kV, 90kV, and 120kV using flash mode at IQ20 are recommended for clinical applications considering both accuracy and radiation dose.

Optimizing computed tomography image reconstruction for focal hepatic lesions: Deep learning image reconstruction vs iterative reconstruction

BackgroundDeep learning image reconstruction (DLIR) is a novel computed tomography (CT) reconstruction technique that minimizes image noise, enhances image quality, and enables radiation dose reduction. This study aims to compare the diagnostic performance of DLIR and iterative reconstruction (IR) in the evaluation of focal hepatic lesions. MethodsWe conducted a retrospective study of 216 focal hepatic lesions in 109 adult participants who underwent abdominal CT scanning at our institution. We used DLIR (low, medium, and high strength) and IR (0 %, 10 %, 20 %, and 30 %) techniques for image reconstruction. Four experienced abdominal radiologists independently evaluated focal hepatic lesions based on five qualitative aspects (lesion detectability, lesion border, diagnostic confidence level, image artifact, and overall image quality). Quantitatively, we measured and compared the level of image noise for each technique at the liver and aorta. ResultsThere were significant differences (p < 0.001) among the seven reconstruction techniques in terms of lesion borders, image artifacts, and overall image quality. Low-strength DLIR (DLIR-L) exhibited the best overall image quality. Although high-strength DLIR (DLIR-H) had the least image noise and fewest artifacts, it also had the lowest scores for lesion borders and overall image quality. Image noise showed a weak to moderate positive correlation with participants’ body mass index and waist circumference. ConclusionsThe optimal-strength DLIR significantly improved overall image quality for evaluating focal hepatic lesions compared to the IR technique. DLIR-L achieved the best overall image quality while maintaining acceptable levels of image noise and quality of lesion borders.

Optimal hadamard single-pixel imaging based on fourier spectrum of pattern

A Hadamard single-pixel imaging method is proposed, which rearranges the order of Hadamard patterns by comparing their energy values of selected regions in the Fourier spectrum of the patterns, thereby optimizing the sampling times required when a certain level of image quality needed to be obtained. The relationship between the Fourier spectrum of the reconstructed image and the adopted projection patterns is explored, and we argue that the reconstructed object is actually a weighted superposition of the applied patterns. Simulation and experiment are carried out for the proposed method. The results show that the selection of pattern is crucial to the reconstruction of the object. We believe that this method may be helpful to the optimal design of single-pixel imaging pattern in the future.

Facial recognition for disaster victim identification

Mass disaster events can result in high levels of casualties that need to be identified. Whilst disaster victim identification (DVI) relies on primary identifiers of DNA, fingerprints, and dental, these require ante-mortem data that may not exist or be easily obtainable. Facial recognition technology may be able to assist. Automated facial recognition has advanced considerably and access to ante-mortem facial images are readily available. Facial recognition could therefore be used to expedite the DVI process by narrowing down leads before primary identifiers are made available. This research explores the feasibility of using automated facial recognition technology to support DVI. We evaluated the performance of a commercial-off-the-self facial recognition algorithm on post-mortem images (representing images taken after a mass disaster) against ante-mortem images (representing a database that may exist within agencies who hold face databases for identity documents (such as passports or driver's licenses). We explored facial recognition performance for different operational scenarios, with different levels of face image quality, and by cause of death. Our research is the largest facial recognition evaluation of post-mortem and ante-mortem images to date. We demonstrated that facial recognition technology would be valuable for DVI and that the performance varies by image quality and cause of death. We provide recommendations for future research.

Deep Learning Reconstructed New-Generation 0.55 T MRI of the Knee-A Prospective Comparison With Conventional 3 T MRI.

The aim of this study was to compare deep learning reconstructed (DLR) 0.55 T magnetic resonance imaging (MRI) quality, identification, and grading of structural anomalies and reader confidence levels with conventional 3 T knee MRI in patients with knee pain following trauma. This prospective study of 26 symptomatic patients (5 women) includes 52 paired DLR 0.55 T and conventional 3 T MRI examinations obtained in 1 setting. A novel, commercially available DLR algorithm was employed for 0.55 T image reconstruction. Four board-certified radiologists reviewed all images independently and graded image quality, noted structural anomalies and their respective reporting confidence levels for the presence or absence, as well as grading of bone, cartilage, meniscus, ligament, and tendon lesions. Image quality and reader confidence levels were compared ( P < 0.05, significant), and MRI findings were correlated between 0.55 T and 3 T MRI using Cohen kappa (κ). In reader's consensus, good image quality was found for DLR 0.55 T MRI and 3 T MRI (3.8 vs 4.1/5 points, P = 0.06). There was near-perfect agreement between 0.55 T DLR and 3 T MRI regarding the identification of structural anomalies for all readers (each κ ≥ 0.80). Substantial to near-perfection agreement between 0.55 T and 3 T MRI was reported for grading of cartilage (κ = 0.65-0.86) and meniscus lesions (κ = 0.71-1.0). High confidence levels were found for all readers for DLR 0.55 T and 3 T MRI, with 3 readers showing higher confidence levels for reporting cartilage lesions on 3 T MRI. In conclusion, new-generation 0.55 T DLR MRI provides good image quality, comparable to conventional 3 T MRI, and allows for reliable identification of internal derangement of the knee with high reader confidence.

Stability and accuracy of fat quantification on photon-counting detector CT with various scan settings: A phantom study

ObjectiveFat deposition is an important marker of many metabolic diseases. As a noninvasive and convenient examination method, CT has been widely used for fat quantification. With the clinical application of photon-counting detector (PCD)-CT, we aimed to investigate the accuracy, stability, and dose level of PCD-CT using various scan settings for fat quantification. Materials and MethodsEleven agar-based lipid-containing phantoms (vials with different fat fractions [FFs]; range: 0 %–100 %) were scanned using PCD-CT. Three scanning types (sequence scan, regular spiral scan with a pitch of 0.8, and high-pitch spiral scan with a pitch of 3.2), four tube voltages (90, 120, 140, and 100 kV with a tin filter), and three image quality (IQ) levels (IQ levels of 20, 40, and 80) were alternated, and each scan setting was used twice. For each scan, a 70-keV image was generated using the same reconstruction parameters. A regular spiral scan at 120 kV with IQ80 was used to transfer the CT numbers of all scans to the FF. Intraclass correlation coefficient (ICC) and Bland–Altman analysis were implemented for accuracy and agreement evaluation, and group differences were compared using analysis of variance. ResultsExcellent agreement and accuracy of FF derived by PCD-CT with all scan settings was demonstrated by high ICCs (>0.9; range: 0.929–0.998, p < 0.017) and low bias (<5% range: −2.9 %–5%). The root mean square error (RMSE) between the PCD-CT-acquired FF and the reference standard ranged from 1.0 % to 5.0 %, among which the high-pitch scan at 120 kV with IQ20 accounted for the lowest RMSE (1.0 %). The spiral scan at 120 kV with IQ20 and IQ80 yielded the lowest bias (mean value: 1.19 % and 1.23 %, respectively). ConclusionFat quantification using PCD-CT reconstructed at 70 keV was accurate and stable under various scan settings. PCD-CT has great potential for fat quantification using ultralow radiation doses.

47‐3: Novel Visually Lossless Display Interface Technique using Adaptive Sub‐color Optimization and DCT (Discrete Cosine Transform)

A novel bandwidth‐reduced display interface technique using adaptive sub‐color optimization with DCT (AS‐DCT) has been proposed to be able to lower both implementation cost and power consumption with visually lossless image quality for high resolution display products. A DCT conversion is applied to 1‐D line pixel data, and 50% of the redundant data has been compressed adaptively based on both sub‐color priority and the distribution of AC data components. A PSNR of about 45.86dB level was achieved for a given set of test images, and it was implemented using RTL coding in a FPGA to verify image quality and logic size level on an actual display system. Through the proposed technique, it is expected that it can easily reduce display data bandwidth without latency and lower its implementation cost for future display devices, such as 8K/16K resolutions, 240Hz/480Hz refresh rates, immersive AR/VR, Light field displays and beyond.

Consistency of Monoenergetic Attenuation Measurements for a Clinical Dual-Source Photon-Counting Detector CT System Across Scanning Paradigms: A Phantom Study.

BACKGROUND. Use of virtual monoenergetic images (VMIs) from multienergy CT scans can mitigate inconsistencies in traditional attenuation measurements that result from variation in scan-related factors. Photon-counting detector (PCD) CT systems produce VMIs as standard image output under flexible scanning conditions. OBJECTIVE. The purpose of this article was to evaluate the consistency of monoenergetic attenuation measurements obtained from a clinical PCD CT scanner across a spectrum of scanning paradigms. METHODS. A phantom with 10 tissue-simulating inserts was imaged using a clinical dual-source PCD CT scanner. Nine scanning paradigms were obtained across combinations of tube voltages (90, 120, and 140 kVp) and image quality (IQ) levels (80, 145, and 180). Images were reconstructed at VMI levels of 50, 60, 70, and 80 keV. Consistency of attenuation measurements was assessed, using the 120 kVp with IQ level of 145 scanning paradigm as the reference scan. RESULTS. For all scanning paradigms, attenuation measurements showed intra-class correlation of 0.999 and higher with respect to the reference scan. Across inserts, mean bias relative to the reference scan ranged from -14.9 to 13.6 HU, -2.7 to 1.7 HU, and -3.9 to 3.8 HU at tube voltages of 90, 120, and 140 kVp, respectively; and from -14.9 to 13.6 HU, -6.4 to 3.8 HU, -3.7 to 1.4 HU, and -7.2 to 4.3 HU at VMI levels of 50, 60, 70, and 80 keV, respectively. Thus, mean bias did not exceed 5 HU for any insert at tube potentials of 120 kVp and 140 kVp, nor for any insert at a VMI level of 70 keV. At a VMI level of 50 keV and tube potential of 90 kVp, mean bias exceeded 5 HU for 14 of 30 possible combinations of inserts and scanning paradigms and exceeded 10 HU for four of 30 such combinations. At VMI levels of both 60 and 80 keV, mean bias exceeded 5 HU for only two combinations of inserts and scanning paradigms, all at a tube potential of 90 kVp. CONCLUSION. PCD CT generally provided consistent attenuation measurements across combinations of scanning paradigms and VMI levels. CLINICAL IMPACT. PCD CT may facilitate quantitative applications of CT data in clinical practice.

Gated cardiac CT in infants: What can we expect from deep learning image reconstruction algorithm?

BackgroundECG-gated cardiac CT is now widely used in infants with congenital heart disease (CHD). Deep Learning Image Reconstruction (DLIR) could improve image quality while minimizing the radiation dose. ObjectivesTo define the potential dose reduction using DLIR with an anthropomorphic phantom. MethodAn anthropomorphic pediatric phantom was scanned with an ECG-gated cardiac CT at four dose levels. Images were reconstructed with an iterative and a deep-learning reconstruction algorithm (ASIR-V and DLIR). Detectability of high-contrast vessels were computed using a mathematical observer. Discrimination between two vessels was assessed by measuring the CT spatial resolution. The potential dose reduction while keeping a similar level of image quality was assessed. ResultsDLIR-H enhances detectability by 2.4% and discrimination performances by 20.9% in comparison with ASIR-V 50. To maintain a similar level of detection, the dose could be reduced by 64% using high-strength DLIR in comparison with ASIR-V50. ConclusionDLIR offers the potential for a substantial dose reduction while preserving image quality compared to ASIR-V.

Allowable movement of wavefront-guided contact lens corrections in normal and keratoconic eyes.

The goal was to use SyntEyes modelling to estimate the allowable alignment error of wavefront-guided rigid contact lens corrections for a range of normal and keratoconic eye aberration structures to keep objectively measured visual image quality at or above average levels of well-corrected normal eyes. Secondary purposes included determining the required radial order of correction, whether increased radial order of the corrections further constrained the allowable alignment error and how alignment constraints vary with keratoconus severity. Building on previous work, 20 normal SyntEyes and 20 keratoconic SyntEyes were fitted with optimised wavefront-guided rigid contact lens corrections targeting between three and eight radial orders that drove visual image quality, as measured objectively by the visual Strehl ratio, to near 1 (best possible) over a 5-mm pupil for the aligned position. The resulting wavefront-guided contact lens was then allowed to translate up to ±1 mm in the x- and y-directions and rotate up ±15°. Allowable alignment error changed as a function of the magnitude of aberration structure to be corrected, which depends on keratoconus severity. This alignment error varied only slightly with the radial order of correction above the fourth radial order. To return the keratoconic SyntEyes to average levels of visual image quality depended on maximum anterior corneal curvature (Kmax). Acceptable tolerances for misalignment that returned keratoconic visual image quality to average normal levels varied between 0.29 and 0.63 mm for translation and approximately ±6.5° for rotation, depending on the magnitude of the aberration structure being corrected. Allowable alignment errors vary as a function of the aberration structure being corrected, the desired goal for visual image quality and as a function of keratoconus severity.

Adapting to the Dark: A Novel Adaptive Low Light Illumination Correction Algorithm for Video Sequences in Wireless Communications

The lower image quality and higher noise levels of low light images can have a substantial effect on wireless communication. Images typically have less brightness, less clarity, and more picture noise in low light situations. The precision and dependability of picture-based communication may be impacted by this decline in image quality. Low light levels can be detrimental to video chats since it becomes difficult for the camera to get clear pictures of the user's face. This may lead to decreased video quality, trouble with facial recognition, and a generally worse user experience when communicating over video. Low light illumination correction algorithms are frequently made to work quickly and effectively in order to enable real-time video-based wireless communication. Correcting uneven illumination or low light in images is crucial for various fields; as such images pose challenges for human recognition, computer vision algorithms, and multimedia algorithms. An adaptive illumination correction algorithm based on a simple log transformation approach enhanced by a hyperbolic beta transformation and a logarithmic image processing is presented in this research. It has an adaptive optimization function that adjusts itself based on the image’s illumination and reflectance. Low computation and minimal parameter optimization are the hallmarks of this method. A variety of illuminated images and video sequences from various datasets are used to evaluate the suggested adaptive illumination approach. This adaptive illumination approach's experimental findings are impressive compared to those produced using prior techniques. The results reveal that the proposed system can adapt to various image-based communication and surveillance video sequences taken under various illumination conditions.

An evaluation of CT radiation doses within the Yorkshire Lung Screening Trial.

To evaluate radiation doses for all low-dose CT scans performed during the first year of a lung screening trial. For all lung screening scans that were performed using a CT protocol that delivered image quality meeting the RSNA QIBA criteria, radiation dose metrics, participant height, weight, gender, and age were recorded. Values of volume CT dose index (CTDIvol) and dose length product (DLP) were evaluated as a function of weight in order to assess the performance of the scan protocol across the participant cohort. Calculated effective doses were used to establish the additional lifetime attributable cancer risks arising from trial scans. Median values of CTDIvol, DLP, and effective dose (IQR) from the 3521 scans were 1.1mGy (0.70), 42.4mGycm (24.9), and 1.15mSv (0.67), whilst for 60-80kg participants the values were 1.0mGy (0.30), 35.8mGycm (11.4), and 0.97mSv (0.31). A statistically significant correlation between CTDIvol and weight was identified for males (r = 0.9123, P < .001) and females (r = 0.9052, P < .001), however, the effect of gender on CTDIvol was not statistically significant (P = .2328) despite notable differences existing at the extremes of the weight range. The additional lifetime attributable cancer risks from a single scan were in the range 0.001%-0.006%. Low radiation doses can be achieved across a typical lung screening cohort using scan protocols that have been shown to deliver high levels of image quality. The observed dose levels may be considered as typical values for lung screening scans on similar types of scanners for an equivalent participant cohort. Presentation of typical radiation dose levels for CT lung screening examinations in a large UK trial. Effective radiation doses can be of the order of 1mSv for standard sized participants. Lifetime attributable cancer risks resulting from a single low-dose CT scan did not exceed 0.006%.

An Intra-Individual Comparison of Low-keV Photon-Counting CT versus Energy-Integrating-Detector CT Angiography of the Aorta.

This retrospective study aims to provide an intra-individual comparison of aortic CT angiographies (CTAs) using first-generation photon-counting-detector CT (PCD-CT) and third-generation energy-integrating-detector CT (EID-CT). High-pitch CTAs were performed with both scanners and equal contrast-agent protocols. EID-CT employed automatic tube voltage selection (90/100 kVp) with reference tube current of 434/350 mAs, whereas multi-energy PCD-CT scans were generated with fixed tube voltage (120 kVp), image quality level of 64, and reconstructed as 55 keV monoenergetic images. For image quality assessment, contrast-to-noise ratios (CNRs) were calculated, and subjective evaluation (overall quality, luminal contrast, vessel sharpness, blooming, and beam hardening) was performed independently by three radiologists. Fifty-seven patients (12 women, 45 men) were included with a median interval between examinations of 12.7 months (interquartile range 11.1 months). Using manufacturer-recommended scan protocols resulted in a substantially lower radiation dose in PCD-CT (size-specific dose estimate: 4.88 ± 0.48 versus 6.28 ± 0.50 mGy, p < 0.001), while CNR was approximately 50% higher (41.11 ± 8.68 versus 27.05 ± 6.73, p < 0.001). Overall image quality and luminal contrast were deemed superior in PCD-CT (p < 0.001). Notably, EID-CT allowed for comparable vessel sharpness (p = 0.439) and less pronounced blooming and beam hardening (p < 0.001). Inter-rater agreement was good to excellent (0.58-0.87). Concluding, aortic PCD-CTAs facilitate increased image quality with significantly lower radiation dose compared to EID-CTAs.

Improved Retinex algorithm for low illumination image enhancement in the chemical plant area

Due to the complexity of the chemical plant area at night and the harsh lighting environment, the images obtained by monitoring equipment have issues such as blurred details and insufficient contrast, which is not conducive to the subsequent target detection work. A low illumination image enhancement model based on an improved Retinex algorithm is proposed to address the above issues. The model consists of a decomposition network, an adjustment network, and a reconstruction network. In the decomposition network, a new decomposition network USD-Net is established based on U-Net, which decomposes the original image into illumination and reflection maps, enhancing the extraction of image details and low-frequency information; Using an adjustment network to enhance the decomposed lighting image, and introducing a Mobilenetv3 lightweight network and residual structure to simplify the network model and improve the contrast of the image; In the reconstruction network, the BM3D method is used for image denoising to enhance the ability to restore image detail features; The enhanced illumination and reflection images were fused based on the Retinex algorithm to achieve low illumination image enhancement in the chemical plant area. This article uses five image quality evaluation indicators, namely Peak Signal-to-Noise Ratio, Structural Similarity Index, Natural Image Quality Evaluator, Interpolation Error, and Level of Effort, to compare eight traditional or modern algorithms and evaluate three different types of datasets. The experimental results show that the improved algorithm enhances the texture details of the image, improves the contrast and saturation of the image, and has good stability and robustness, which can effectively meet the needs of low illumination image enhancement in chemical plant areas.