Image Quality Evaluation Research Articles

Image enhancement with art design: a visual feature approach with a CNN-transformer fusion model

Graphic design, as a product of the burgeoning new media era, has seen its users’ requirements for images continuously evolve. However, external factors such as light and noise often cause graphic design images to become distorted during acquisition. To enhance the definition of these images, this paper introduces a novel image enhancement model based on visual features. Initially, a histogram equalization (HE) algorithm is applied to enhance the graphic design images. Subsequently, image feature extraction is performed using a dual-flow network comprising convolutional neural network (CNN) and Transformer architectures. The CNN employs a residual dense block (RDB) to embed spatial local structure information with varying receptive fields. An improved attention mechanism module, attention feature fusion (AFF), is then introduced to integrate the image features extracted from the dual-flow network. Finally, through image perception quality guided adversarial learning, the model adjusts the initial enhanced image’s color and recovers more details. Experimental results demonstrate that the proposed algorithm model achieves enhancement effects exceeding 90% on two large image datasets, which represents a 5%–10% improvement over other models. Furthermore, the algorithm exhibits superior performance in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) image quality evaluation metrics. Our findings indicate that the fusion model significantly enhances image quality, thereby advancing the field of graphic design and showcasing its potential in cultural and creative product design.

A Low-Cost Remotely Configurable Electronic Trap for Insect Pest Dataset Generation

The precise monitoring of insect pest populations is the foundation of Integrated Pest Management (IPM) for pests of plants, humans, and animals. Digital technologies can be employed to address one of the main challenges, such as reducing the IPM workload and enhancing decision-making accuracy. In this study, digital technologies are used to deploy an automated trap for capturing images of insects and generating centralized repositories on a server. Subsequently, advanced computational models can be applied to analyze the collected data. The study provides a detailed description of the prototype, designed with a particular focus on its remote reconfigurability to optimize repository quality; and the server, accessible via an API interface to enhance system interoperability and scalability. Quality metrics are presented through an experimental study conducted on the constructed demonstrator, emphasizing trap reliability, stability, performance, and energy consumption, along with an objective analysis of image quality using metrics such as RMS contrast, Image Entropy, Image sharpness metric, Natural Image Quality Evaluator (NIQE), and Modulation Transfer Function (MFT). This study contributes to the optimization of the current knowledge regarding automated insect pest monitoring techniques and offers advanced solutions for the current systems.

Diagnostic accuracy and image quality evaluation of ultrashort echo time MRI in the lungs

This study evaluates the diagnostic accuracy of ultrashort echo time (UTE)-MRI for detecting pulmonary nodules and image quality. A total of 46 patients at our hospital underwent unenhanced computed tomography (CT) and UTE-MRI. The image quality and number of nodules detected using CT were used as the gold standards. Three diagnostic radiologists independently recorded the image quality (visibility and sharpness of normal anatomical structures) of the CT and UTE images and the number of pulmonary nodules detected. The diagnostic accuracy, subjective image quality, and consistency between observations were statistically analyzed. Among 46 patients, 36 (78.2%) had pulmonary nodules on CT images, whereas 10 patients (21.7%) had no pulmonary nodules. A total of 48 lung nodules were detected, 3 of which were ground-glass opacities. UTE-MRI revealed 46 lung nodules. Compared with CT, the sensitivity of all MRI readers for detecting lung lesions was 95.8%, and the 3-observer agreement was nearly perfect (P < .001, Kendall Wa [Kender Harmonious Coefficient] = 0.913). The overall image quality score of the observers was high, ranging from good to excellent, and the consistency of the subjective UTE-MRI image quality was good (Kendall Wa = 0.877, P < .001). For tracheal display, the subsegment of the bronchus was displayed, and the wall of the tube was clearly displayed. The difference in the Wa values between the observers was 0.804 (P < .001), indicating strong consistency. For blood vessels, subsegment blood vessels could also be displayed with clear walls and uniform signals (Kendal Wa = 0.823, P < .001), indicating strong consistency. Compared to CT, UTE-MRI can detect pulmonary nodules with a high detection rate, relatively good image quality, and strong consistency between observers. The development of UTE-MRI can provide a novel imaging method for the detection and follow-up of pulmonary nodules and diagnosis of pneumonia by reducing ionizing radiation.

Performance Characteristics of a New Generation 148-cm Axial Field-of-View uMI Panorama GS PET/CT System with Extended NEMA NU 2-2018 and EARL Standards.

The uMI Panorama GS PET/CT system is a new long-axial-field-of-view scanner featuring high sensitivity, time-of-flight (TOF) resolution, spatial resolution, and count rate performance. The aim of this study is to assess the PET system on the basis of the National Electrical Manufacturers Association (NEMA) NU 2-2018 and European Association of Nuclear Medicine Research Limited (EARL) standards. Methods: Spatial resolution, count rate performance, sensitivity, accuracy, image quality, TOF resolution, and coregistration accuracy were evaluated following the NEMA NU 2-2018 standard. Additional experiments included energy resolution, 200-cm-long line sources for sensitivity, a 175-cm-long scatter phantom for count rate and TOF resolution, as well as the compliance with the EARL guideline. Moreover, an 18F-FDG PET patient study was reconstructed with various frame durations. Results: The PET system achieved sub-3-mm transaxial and axial spatial resolutions at a 1-cm radial offset. The sensitivities with the 70-cm-long and 200-cm-long line sources were observed to be 176.3 and 90.8 kcps/MBq, respectively, at the center of the field of view. The noise-equivalent count rates (NECRs) of the 70-cm-long and 175-cm-long scatter phantoms were measured to be 3.35 Mcps at 57.57 kBq/mL and 2.24 Mcps at 33.27 kBq/mL, respectively. The TOF resolutions for both phantoms were approximately 189 ps at 5.3 kBq/mL and lower than 200 ps below the NECR peaks. The absolute count rate errors of all 34 acquisitions were less than 3% below the NECR peak for the 70-cm-long scatter phantom. With the standard NEMA image quality phantom experiment, the contrast recovery coefficient varied from 68.17% to 94.20% and the background variabilities were all below 2%. The maximum PET/CT coregistration error was 1.33 mm. Regarding EARL compliance, the gaussian filter of 5-mm full width at half maximum could produce acceptable images. The patient data demonstrate visually satisfactory image quality with short frames (less than 1 min). Conclusion: The uMI Panorama GS exhibits spatial resolution and TOF resolution similar to those of the uMI Panorama system (35-cm axial field of view), despite the extended axial field of view. The 148-cm axial coverage, sub-200-ps TOF resolution, high sensitivity, and count rate performances are expected to yield superior image quality and offer new opportunities for various clinical applications.

Super-resolution imaging quality enhancement method for distributed array infrared camera

Abstract To address issues related to low resolution and high noise in infrared cameras, a distributed array infrared camera imaging system utilizing four cameras is proposed. The four cameras are arranged in an unconstrained array, and the combination algorithm of Projections onto Convex Sets (POCS) and Real-Enhanced Super-Resolution Generative Adversarial Networks (Real-ESRGAN) is applied to achieve high-quality super-resolution infrared imaging. The wavelet fusion algorithm is used to preprocess four low-resolution infrared images to reduce noise. Then, the POCS algorithm is used to reconstruct the preprocessed image. Finally, the Real-ESRGAN is employed for image reconstruction, resulting in an ultra-high-resolution infrared image. The results show that compared to single infrared camera imaging, the resolution of images reconstructed using the distributed infrared camera array is increased by 0.58 times, with the Modulation Transfer Function (MTF) increased by 1.2 times. Additionally, the entropy is increased by 18.87%, the standard deviation is increased by 13.51%, and the Naturalness Image Quality Evaluator (NIQE) is reduced by 18.87%. This demonstrates a significant enhancement in the super-resolution imaging quality of the distributed infrared camera array.

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

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

Characterization of X-ray Beams: Determination of K\(_{a,e}\) for Comparison with DRLs

Aims: The aim of this experimental study was to quantify the entrance surface air kerma (Ka,e) of X-ray beams in radiographic examinations, and the obtained values were compared with national and international Diagnostic Reference Levels (DRLs). Place and Duration of Study: Sample: Department of Medical Physics and Radiology, Franciscan University (UFN), between June 2023 and August 2024. Methodology: Initially, the main quality control (QC) tests were applied to characterize the radiographic equipment. During the evaluation of the X-ray beam, a dosimetric set was used, and for the same QC geometry, Air Kerma Rate (KAIR) measurements were obtained to determine the output of the X-ray tube. Under the same conditions, a phantom without water and the same filled with water were used to determine the backscatter factor (BSF), which was then used to estimate the Ka,e for the main radiographic examinations. Results: The results of this study are consistent with other studies and identified the region/anatomy with the highest radiation exposure, highlighting the importance of its optimization and verifying that continued professional training should be integrated into best practices to ensure greater safety in procedures. The results obtained evidence the necessity for adjustments in image acquisition protocols, as the Ka,e values found in this study, although within the limits established by the national DRLs, exceed the reference values of countries such as the United Kingdom and Japan in the areas of the skull, abdomen, and lumbar spine. This discrepancy highlights the importance of the periodic review and update of DRLs, in accordance with international recommendations and the technological evolution of equipment. Dose optimization is an ongoing process that involves the evaluation of image quality, the appropriate selection of technical parameters, and the implementation of specific dose protocols. The continuous training of radiology professionals is essential to ensure the correct application of imaging techniques and the minimization of patient exposure to ionizing radiation. Conclusion: This study shows the importance of evaluating radiation dose in diagnostic imaging and the need for a multidisciplinary approach to dose optimization. Implementing quality assurance programs, updating DRLs, and providing ongoing training for professionals are essential to ensure patient safety and high-quality diagnostic imaging. The methodology adopted in this study can contribute to the ongoing education of radiology professionals, promoting the correct application of imaging techniques and reducing patient exposure to ionizing radiation.

AI upscaling Supporting Image Alignment in Photogrammetric Reconstruction

Abstract. This research aims to investigate and analyze the contribution provided by preventive image processing using AI in the SfM data acquisition process. Specifically, the objective is to observe qualities and defects of "AI upscaling" integrated into the normal workflow of digital restitution, with the hypothesis that greater sharpness and resolution can lead to better alignments and better model generations. Other similar experiments have been carried out previously on the AI intervention in the photos to improve the alignments, but a generic procedure and with untrained public AI has not yet been experimented. In the first phase of the research, the most suitable AI upscaling method for the purpose is selected, processing example images and comparing them with the original and various settings of the AI parameters to maintain likeness. In the second phase, the procedure efficiency was evaluated in several ways: through the automatic evaluation of Agisoft Metashape's Image Quality, by directly comparing the resulting model with the standard digital reconstruction of the same objects, comparing with a model produced with the native AI upscaling of the return program, and finally comparing with a high-level survey without defects. This test was repeated for four case studies specifically diversified by several factors, including subject scale, level of detail, lighting, color, blur, and type of camera used. Clear improvements emerge in all cases of AI upscaling, even reaching successful reconstructions that would not have started with the canonical method. It can therefore be stated that this preventive upscaling procedure is decisive in cases of low quality or damaged datasets, while in cases of already qualitatively sufficient photos it can be a useful support for increasing the level of detail.

Performance evaluation of a small-animal PET scanner with 213 mm axis using NEMA NU 4-2008.

Long-axis positron emission tomography (PET) has emerged as one of the recent research directions in PET due to its ability to significantly enhance sensitivity and counting performance for low-dose imaging, rapid imaging, and whole-body dynamicimaging. The PET system presented in this study is a long-axis animal PET based on lutetium-yttrium orthosilicate and silicon photomultiplier, designed for whole-body imaging in rats. It features a diameter of 143 mm and an axial length of 213.3 mm. This study evaluated the performance of this PET system in accordance with the National Electrical Manufacturers Association (NEMA) NU 4-2008standards. The performance evaluation was conducted according to the NEMA NU 4-2008 standards in terms of spatial resolution, sensitivity, counting rate performance, scatter fraction (SF) and image quality. In addition, a rat imaging study was conducted to assess the imaging capability of this PETsystem. The average energy resolution of the PET system was 12.87%, the average coincidence timing resolution was 751 ps. The FWHM of spatial resolution reconstructed by filtered back projection and 3D-OSEM-PSF algorithm at 5 mm radial offset from the axial center were 1.65 and 0.88 mm. The peak absolute sensitivity measured by a point source at the center of the field of view was evaluated as 6.71% (361-661keV) and 10.31% (250-750 keV). For the mouse-like phantom, the SF was 11.0% and the peak noise equivalent counting rate (NECR) was 1193 kcps at 94.2 MBq (2.54 mCi). For the rat-like phantom, the SF was 26.8% and the NECR was 682.5 kcps at 78.6 MBq (2.12 mCi). The performance measurement results demonstrate that this PET system exhibits high sensitivity and count rate performance, making it potential for high-quality whole-body dynamic imaging ofrats.

Brain Tumor Detection through Image Fusion Using Cross Guided Filter and Convolutional Neural Network

This Data fusion has become a significant issue in diagnostic imaging, particularly in medical applications like radiation and guided image surgery. Medical image fusion aims to enhance the precision of tumor diagnosis, by preserving the salient information and characteristics of the original images in the fused image. It has been shown that guided filters are capable of maintaining edges well. In this paper, we propose a novel cross-guided filter-based fusion approach for multimodal medical images utilizing convolutional neural networks. The cross-guided filter is used in the proposed algorithm to extract the detailed features from the source images. Convolutional neural networks are used to generate the feature weights of source images derived from the detail layers. The weighted average rule is used to merge the source images based on these weights. We used thirty distinct types of medical images from diverse sources to compare the effectiveness of the proposed strategy to that of existing methods, both numerically and visually. The experimental findings demonstrated that, in terms of both objective evaluation and qualitative image quality, the suggested system performs better than other standard methods already in use. The quantitative results show that compared to existing methods under consideration for comparison, the proposed algorithm improves mutual information by 25%, image entropy by 9.5%, spatial frequency by 21%, standard deviation by 18.1%, structural similarity index by 30%, and edge strength of the fused image by 39%.

Fully automated measurement of noise, signal-to-noise ratio, and contrast-to-noise ratio on chest CT images: feasibility and efficiency.

Rapid and accurate measurement of computed tomography (CT) image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) is a clinical challenge. To explore the feasibility of intelligent measurement of chest CT image noise, SNR, and CNR. A total of 300 chest CT scans were included in the study, which was divided into research dataset, internal test dataset, and external test dataset. Based on the research dataset, automatically segment and measure the average CT values and standard deviation (SD) of CT values for background air and lung field under different thresholds to obtain noise, SNR, and CNR results. Using the results of manual measurements as the reference standard, we determine the optimal threshold with the highest consistency. Using internal and external test datasets, validate the consistency of automated measurements of noise, SNR, and CNR at the optimal CT threshold with reference standards. With background air set at -900 HU and lung field at -800 HU as thresholds, the automated measurements of noise, SNR, and CNR demonstrate the highest consistency with the reference standards. At the optimal threshold, the noise, SNR, and CNR measured automatically on both the internal (intraclass correlation coefficient [ICC] = 0.85-0.96) and external (ICC = 0.75-0.85) test datasets exhibit high consistency with their respective reference standards. The method we explored can intelligently measure the noise, SNR, and CNR of chest CT images, exhibits high consistency with radiologists, and offers a novel tool for image quality evaluation and analysis.

Multiexposed Image-Fusion Strategy Using Mutual Image Translation Learning with Multiscale Surround Switching Maps

The dynamic range of an image represents the difference between its darkest and brightest areas, a crucial concept in digital image processing and computer vision. Despite display technology advancements, replicating the broad dynamic range of the human visual system remains challenging, necessitating high dynamic range (HDR) synthesis, combining multiple low dynamic range images captured at contrasting exposure levels to generate a single HDR image that integrates the optimal exposure regions. Recent deep learning advancements have introduced innovative approaches to HDR generation, with the cycle-consistent generative adversarial network (CycleGAN) gaining attention due to its robustness against domain shifts and ability to preserve content style while enhancing image quality. However, traditional CycleGAN methods often rely on unpaired datasets, limiting their capacity for detail preservation. This study proposes an improved model by incorporating a switching map (SMap) as an additional channel in the CycleGAN generator using paired datasets. The SMap focuses on essential regions, guiding weighted learning to minimize the loss of detail during synthesis. Using translated images to estimate the middle exposure integrates these images into HDR synthesis, reducing unnatural transitions and halo artifacts that could occur at boundaries between various exposures. The multilayered application of the retinex algorithm captures exposure variations, achieving natural and detailed tone mapping. The proposed mutual image translation module extends CycleGAN, demonstrating superior performance in multiexposure fusion and image translation, significantly enhancing HDR image quality. The image quality evaluation indices used are CPBDM, JNBM, LPC-SI, S3, JPEG_2000, and SSEQ, and the proposed model exhibits superior performance compared to existing methods, recording average scores of 0.6196, 15.4142, 0.9642, 0.2838, 80.239, and 25.054, respectively. Therefore, based on qualitative and quantitative results, this study demonstrates the superiority of the proposed model.

Do we need an image quality evaluation system in conventional radiography?

Cilj: Ovim radom nastoji se ukazati na važnost uspostave sustava za ocjenu kvalitete radiološke slike u konvencionalnoj radiografiji. Metode: Studija je provedena retrospektivno, usporedbom kvalitete radiološke slike i načina dobivanja radiološke slike. Ocijenjeno je 20 radioloških slika zdjelice, a ocjena je provedena prema europskim kriterijima za kvalitetu radiološke slike zdjelice. Kvalitetu slike su ocjenjivali radiolog, radiološki tehnolog i student radiološke tehnologije. Usporedbom s istim kriterijem analiziran je i način rada pri oslikavanju zdjelice u 3654 pacijenta te srca i pluća u 57758 pacijenata. Analizirana je i praksa sužavanja rendgenskog snopa na područje od interesa za 50 radioloških slika srca i pluća. Rezultati: Samo jedna slika zdjelice ocijenjena je najvećom mogućom ocjenom od strane sva tri ocjenitelja. Subjektivne procjene ocjenitelja vezano uz odabir parametara ekspozicije razlikovale su se u većini ocjenjivanih slika zdjelice. Naponi na rendgenskoj cijevi korišteni pri oslikavanju zdjelice većinom su bili u preporučenom rasponu, dok su za srce i pluća u većini slučajeva bili manji od preporučene vrijednosti. Zdjelice su većinom oslikavane korištenjem udaljenosti žarišta rendgenske cijevi od prijemnika slike koje su bile u prihvatljivom rasponu osim u slučaju onih pacijenata koji su za vrijeme oslikavanja stajali. U slučaju oslikavanja srca i pluća ove su udaljenosti uglavnom bile u prihvatljivom rasponu osim u slučaju ležećih pacijenata kada je veći broj pacijenata oslikan korištenjem manjih udaljenosti. Svih 50 analiziranih radioloških slika srca i pluća bilo je uređeno na način da su po provedenom oslikavanju uklonjeni rubni dijelovi slike. Zaključci: Rezultati provedene analize ukazuju na potrebu uspostave i provedbe sustava za ocjenu kvalitete radiološke slike koji bi uključivao i analizu načina rada.

Chemical shift-encoded multishot EPI for navigator-free prostate DWI.

DWI is an important contrast for prostate MRI to enable early and accurate detection of cancer. This study introduces a Dixon 3-shot-EPI protocol with structured low-rank reconstruction for navigator-free DWI. The aim is to overcome the limitations of single-shot EPI (ssh-EPI), such as geometric distortions and fat signal interference, while addressing the motion-induced phase variations of multishot EPI and simultaneously allowing water/fat separation. DWI data were acquired from 7 healthy volunteers using both Dixon 3-shot EPI and standard fat-suppressed ssh-EPI with similar scan times for comparison. Two readers evaluated image quality using a 5-point Likert scale regarding different aspects. The ADC values were quantitatively compared between protocols. To show feasibility in a clinical setting, the protocol was applied to two patients. From the reader scores, Dixon 3-shot EPI significantly reduced geometric distortion compared with ssh-EPI (p < 0.01), with no significant differences in edge definition, SNR, or overall image quality. There was no significant difference in ADC values between the two protocols. However, the Dixon multishot-EPI protocol offered advantages such as self-referenced B0 map-driven distortion correction, greater flexibility in imaging parameters, and superior fat suppression. In the patient data, the lesion could be clearly identified in both protocols and on the associated ADC maps. The proposed Dixon 3-shot-EPI protocol shows promise as an alternative to ssh-EPI for prostate DWI, providing reduced geometric distortions and improved fat suppression. It addresses common DWI issues based on EPI and enhances scanning flexibility, indicating potential for optimized imaging.

Autonomous Scanning Tunneling Microscopy Imaging via Deep Learning.

Scanning tunneling microscopy (STM) is a powerful technique that provides the ability to manipulate and characterize individual atoms and molecules with atomic-level precision. However, the processes of scanning samples, operating the probe, and analyzing data are typically labor-intensive and subjective. Deep learning (DL) techniques have shown immense potential in automating complex tasks and solving high-dimensional problems. In this study, we developed an autonomous STM framework powered by DL to enable autonomous operations of the STM without human interventions. Our framework employs a convolutional neural network (CNN) for real-time evaluation of STM image quality, a U-net model for identifying bare surfaces, and a deep Q-learning network (DQN) agent for autonomous probe conditioning. Additionally, we integrated an object recognition model for the automated recognition of different adsorbates. This autonomous framework enables the acquisition of space-averaging information using STM techniques without compromising the high-resolution molecular imaging. We achieved measuring an area of approximately 1.9 μm2 within 48 h of continuous measurement and automatedly generated the statistics on the molecular species present within the mesoscopic area. We demonstrate the high robustness of the framework by conducting measurements at the liquid nitrogen temperature (∼78 K). We envision that the integration of DL techniques and high-resolution microscopy will not only extend the functionality and capability of scanning probe microscopes but also accelerate the understanding and discovery of new materials.

Color restoration of mural images based on a reversible neural network: leveraging reversible residual networks for structure and texture preservation

AbstractThe Mogao Grottoes in Dunhuang, a treasure of China's and the world's cultural heritage, contains rich historical and cultural deposits and has left precious relics of the history of human art. Over centuries, the Mogao Caves have been affected by natural and human factors, resulting in irreversible fading and discoloration of many murals. In recent years, deep learning technology has shown great potential in the field of virtual mural color restoration. Therefore, this paper proposes a mural image color restoration method based on a reversible neural network. The method first employs an automatic reference selection module based on structural and texture similarity to choose suitable reference mural images for the faded murals. Then, it utilizes a reversible residual network to extract deep features of the mural images without information loss. Next, a channel refinement module is used to eliminate redundant information in the network channels. Finally, an unbiased color transfer module restores the color of the faded mural images. Compared to other image color restoration methods, the proposed method achieves superior color restoration effects while effectively preserving the original structure and texture details of the mural images. Compared to baseline methods, the Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), and Perception-based Image Quality Evaluator (PIQE) values are improved by 7.97%, 3.46%, and 13.98%, respectively. The color restoration of the Dunhuang Mural holds significant historical, artistic, cultural, and economic values, and plays a positive role in the preservation and inheritance of Chinese culture, as well as in the promotion of cultural exchange and mutual understanding.

Ultra-High-Resolution Photon-Counting Detector CTA of the Head and Neck: Image Quality Assessment and Vascular Kernel Optimization.

Background: Head and neck CTA requires fine detail evaluation, including characterization of potentially very small vessels and intrastent lumens. Blooming artifacts also hinder evaluation. Objectives: To evaluate image quality of ultra-high-resolution (UHR) photon-counting detector (PCD) CTA of the head and neck and to explore variation of such quality across reconstruction kernels. Methods: This prospective study included patients who underwent clinically indicated head and neck CTA from September 2023 to December 2023. Participants underwent PCD CTA in UHR mode. Reconstructions for each examination included a reference reconstruction (reflecting clinical protocols) using 0.8-mm slice thickness and Bv40 kernel, and six UHR reconstructions using 0.2-mm slice thickness and kernels of varying sharpness (Bv48-Bv80). Quantitative measures were recorded. Two radiologists independently evaluated qualitative measures using Likert scales (1=lowest quality; 5=highest quality). Results: The analysis included 103 participants (mean age, 61.3±13.0 years; 56 male, 48 female). Median vessel sharpness (in HU/mm) was 100.9 for reference reconstruction, and for UHR varied from 110.0 for Bv46 to 121.6 for Bv76 and 134.7 for Bv80. Median right internal carotid artery C2 luminal diameter was 3.8 mm for reference reconstruction, and for UHR increased from 4.1 mm for Bv48 to 4.9 mm for Bv80. For both readers, median overall image quality for reference reconstruction was 3, and for UHR was highest for Bv64 (5); calcified plaque blooming artifact for reference reconstruction was 1, and for UHR was highest for Bv72 (5) and Bv76 (5); stent blooming artifact for reference reconstruction was 1, and for UHR was highest for Bv76 (5) and Bv80 (5); soft-plaque delineation for reference reconstruction was 1, and for UHR was highest for Bv76 (5) or Bv80 (5); small-vessel visualization for reference reconstruction was 1, and for UHR was highest for Bv76 (5) or Bv80 (5). Conclusion: UHR-PCD CTA yielded reduced blooming artifact from calcified plaques or stents, and improved softplaque and small-vessel visualization. These advantages were more pronounced for strongest kernels, although subjective image quality was better for a weaker kernel. Clinical impact: The findings indicate benefits from use of UHR-PCD CTA for head and neck evaluation and may help guide such examinations' kernel selection.

Analysing the robustness of finger vein recognition: cross-dataset reliability and vein utility

AbstractFinger vein recognition is an emerging biometric trait known for its privacy features. Despite the remarkable performance of deep learning methods like convolutional neural networks on challenging finger vein datasets, their reliability and robustness need further examination. This study evaluates the robustness of three recognition methods—the traditional Miura Method, a supervised convolutional neural network, and an unsupervised convolutional auto-encoder—through the challenging and more realistic scenario of cross-dataset comparisons. We also analyse the reliability of these methods in terms of sample quality. We introduce a novel vein quality metric to measure vein clarity and complexity and compare it against an existing image quality metric, natural image quality evaluator. Our findings reveal differences in how these recognition methods utilise finger vein images for comparisons, highlighting the need for robust recognition techniques in more realistic scenarios. In addition, our vein quality metric effectively detects defective images, reducing the zero false-match rate from 34.98% to 8.18% on the SDUMLA-HMT dataset. These results indicate the need for metrics more focussing on finger vein image characteristics for effective quality assessment for finger vein images.

Groundnut (ARACHIS HYPOGAEA L.) seed defect classification using ensemble deep learning techniques

Groundnut is an oil seed cash crop, that can be consumed directly by humans and animals. It is also used as an intelligence in the industrial production of butter and other products. However, Groundnut seeds can become infected with fungi, viruses, pests, physical damage, and high heat, which can negatively impact crop yield, quality, and economic value, and pose health hazards. To address these challenges, computer vision technology detects and classifies defects. This study introduced an ensemble deep learning defected classification model that combines VGG16 and InceptionV3 using seed images. To enhance model performance, collected images are preprocessed using novel techniques tailored to different image types. Preprocessing techniques are chosen based on image-quality evaluation metrics. Watershed segmentation is applied followed by detecting the region of interest in the image using YOLOv3. The image dataset is augmented and balanced using a Generative Adversarial Network (GAN). The model development involves a combination of classical and deep-based features, comparing features extracted with (HOG and GLCM) to those extracted with InceptionV3 and VGG16. The ensemble model achieves an accuracy of 96.25 % with a split ratio of 10 % for testing, 10 % for validation, and 80 % for training set. This research benefits farmers looking to improve yield, consumers of groundnut products, and researchers studying seed defects. The work provides valid insights for future researchers regarding dataset creation, augmentation, methods, and feature selection for modeling. However, the model experienced misclassifications due to the similar appearance of sample images in different classes. Future researchers should address these challenges and consider factors such as oil content and defect grading.

Evaluating T1-weighted MRI techniques for fetal gastrointestinal diagnostics: A comparative study

PurposeIn clinical practice, fetal gastrointestinal magnetic resonance imaging (MRI) encounters significant challenges. T1-weighted images are particularly susceptible to the effects of fetal and maternal movements compared to other weighted images, complicating the acquisition of satisfactory results. This study aimed to compare three fast 3D-T1 weighted gradient echo (GRE) sequences—free-breathing stack-of-stars VIBE (STAR-VIBE), breath-hold VIBE (BH-VIBE), and free-breathing multi-average VIBE (MA-VIBE)—for fetal gastrointestinal MRI in fetuses with both normal and abnormal gastrointestinal tracts between 21 and 36 weeks of gestation. MethodsThis study enrolled 67 pregnant women who underwent fetal abdominal MRI at our hospital between October 2022 and October 2023, during their gestational period of 21–36 weeks. Among these participants, 22 were suspected of having fetal gastrointestinal anomalies based on ultrasound findings, while the remaining 45 were considered to have normal fetal gastrointestinal development. All subjects underwent True fast imaging with steady-state precession sequence scanning along with three T1-weighted imaging techniques on a Siemens 1.5-T Aera scanner: STAR-VIBE, BH-VIBE, and MA-VIBE. Two radiologists evaluated image quality, intestinal clarity, and lesion conspicuity using a five-point scale where higher scores indicated superior performance for each technique; they were blinded to the acquisition schemes used. Interobserver variability assessments were also conducted. ResultsThe free-breathing MA-VIBE sequence demonstrated significantly better performance than both STAR-VIBE and BH-VIBE in terms of fetal gastrointestinal MRI quality (3.81 ± 0.40 vs. 3.35 ± 0.70 vs. 2.90 ± 0.64; p < .05). The STAR-VIBE and BH-VIBE sequences exhibited moderate consistency (kappa = 0.586 and kappa = 0.527 respectively; P < .05), whereas the MA-VIBE sequence showed higher consistency (kappa = 0.712; P < .05). ConclusionThe free-breathing MA-VIBE sequence provided superior visualization for assessing fetal intestinal conditions compared to other methods employed in this study. On a 1.5 T MRI device, T1-weighted images based on the free-breathing MA-VIBE sequence can effectively overcome motion artifacts and compensate for the reduced signal-to-noise ratio caused by the application of acceleration techniques, thus significantly improving the quality of T1-weighted images.