Contrast Enhancement Research Articles

Improving laryngeal cancer detection using chaotic metaheuristics integration with squeeze-and-excitation resnet model.

Laryngeal cancer (LC) represents a substantial world health problem, with diminished survival rates attributed to late-stage diagnoses. Correct treatment for LC is complex, particularly in the final stages. This kind of cancer is a complex malignancy inside the head and neck region of patients. Recently, researchers serving medical consultants to recognize LC efficiently develop different analysis methods and tools. However, these existing tools and techniques have various problems regarding performance constraints, like lesser accuracy in detecting LC at the early stages, additional computational complexity, and colossal time utilization in patient screening. Deep learning (DL) approaches have been established that are effective in the recognition of LC. Therefore, this study develops an efficient LC Detection using the Chaotic Metaheuristics Integration with the DL (LCD-CMDL) technique. The LCD-CMDL technique mainly focuses on detecting and classifying LC utilizing throat region images. In the LCD-CMDL technique, the contrast enhancement process uses the CLAHE approach. For feature extraction, the LCD-CMDL technique applies the Squeeze-and-Excitation ResNet (SE-ResNet) model to learn the complex and intrinsic features from the image preprocessing. Moreover, the hyperparameter tuning of the SE-ResNet approach is performed using a chaotic adaptive sparrow search algorithm (CSSA). Finally, the extreme learning machine (ELM) model was applied to detect and classify the LC. The performance evaluation of the LCD-CMDL approach occurs utilizing a benchmark throat region image database. The experimental values implied the superior performance of the LCD-CMDL approach over recent state-of-the-art approaches.

Rice quality classification system using convolutional neural network and an adaptive neuro-fuzzy inference system

In the food sector, rice processing and classification are essential operations that help maintain strict quality and safety standards, satisfy various consumer preferences, and satisfy particular market demands. Artificial intelligence (AI) and machine learning techniques are used in automated systems to reliably and effectively classify rice quality. This research compares a rice quality classification system using a convolutional neural network (CNN) and an adaptive neuro-fuzzy inference system (ANFIS). Both methods are evaluated for their ability to classify rice based on quality, utilizing a dataset encompassing various physical characteristics. The comparative analysis results reveal the strengths and weaknesses of each approach in addressing this classification task. In this research, two classification systems for different varieties of rice-medium and premium—are compared. CNN and ANFIS are the techniques applied. The CNN accuracy on the rice picture is 62.5%. Thus, a contrast enhancement procedure was applied and had better accuracy at 75%. However, when contrasted with the classification made using the ANFIS approach, the ANFIS method continued to yield the best accuracy, 82.25%.

Towards a Flexible Semantic Guided Model for Single Image Enhancement and Restoration.

Low-light image enhancement (LLIE) investigates how to improve the brightness of an image captured in illumination-insufficient environments. The majority of existing methods enhance low-light images in a global and uniform manner, without taking into account the semantic information of different regions. Consequently, a network may easily deviate from the original color of local regions. To address this issue, we propose a semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that adaptively integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in the LLIE task. We further present a refined framework SKF++ with two new techniques: (a) Extra convolutional branch for intra-class illumination and color recovery through extracting local information and (b) Equalization-based histogram transformation for contrast enhancement and high dynamic range adjustment. Extensive experiments on various benchmarks of LLIE task and other image processing tasks show that models equipped with the SKF/SKF++ significantly outperform the baselines and our SKF/SKF++ generalizes to different models and scenes well. Besides, the potential benefits of our method in face detection and semantic segmentation in low-light conditions are discussed.

Added prognostic value of DCE blood volume imaging in patients with suspected recurrent or residual glioblastoma – a hybrid [18F]FET PET/MRI study

Abstract Background MRI cerebral blood volume (CBV) measurements improves diagnosis of recurrent gliomas. The study investigated the prognostic value of dynamic contrast enhanced (DCE) CBV imaging in treated IDH wildtype glioblastoma when added to MRI or amino acid PET. Methods Hybrid [18F]FET PET/MRI with 2CXM (2-compartment exchange model) DCE from 86 adult patients with suspected recurrent or residual glioblastoma were retrospectively analysed. High CBV tumour volume (VOLCBV), and contrast enhancing (VOLCE) and [18F]FET active tumour (VOLFET) volumes were delineated. Absolute and fractional high CBV subvolumes within VOLCE and VOLFET were determined. Associations with overall survival (OS) were assessed by Cox analysis. Results Adjusted for MGMT status and steroid all total tumour volumes were individually associated with shorter OS. Adding VOLCBV to VOLCE, or VOLFET only the effect of VOLCBV was prognostic of OS (HR 1.327, p=0.042 and 1.352, p=0.011, respectively). High CBV subvolume within both VOLCE and VOLFET, were associated with shorter survival (HR 1.448, p=0.042 and 1.416, p=0011, respectively), and the low CBV subvolumes with longer survival (HR 0.504, p=0.002 and 0.365, p=0.001, respectively). The fraction of VOLCE and VOLFET with high CBV was a strong predictor of OS with shorter median OS in upper vs lower tertiles (8.3 vs 14.5 months and 7.1 vs 15.6 months, respectively, both p<0.001). Conclusions The high CBV tumour volume was a strong prognosticator of survival and allowed for separation of high and low risk subvolumes underlining the heterogeneous physiological environment represented in the contrast enhancing or metabolically active tumour volumes of treated glioblastoma.

Concurrent spinal schwannoma and meningioma without neurofibromatosis: case presentation and review of literature

Abstract Background Concurrent multiple tumors developing in the spinal cord are rare, except for in genetic disorders, such as neurofibromatosis and von Hippel–Lindau disease. The presence of multiple histologically different spinal tumors in the absence of NF-2 is extremely rare. Schwannoma and meningioma can be explained partly by their common origin from the same mesenchymal cell, there is currently no explanation for the simultaneous existence of different spinal tumors other than simple coincidence. Case presentation After review of literature, there are thirteen such cases reported in the literature in addition to the present case. The current patient presents with lower limb weakness, and the patient radiology revealed intradural mass opposite the fourth lumbar vertebra with contrast enhancement and intradural extramedullary mass opposite the tenth dorsal vertebra with contrast enhancement in MRI spine. The coexistence of these tumors may be coincidental or might be due to a common histological origin of the Schwann cells and the meningocytes. Conclusion Schwannoma and meningioma have different radiological characteristics and require different managing procedures such as dural base coagulation in meningiomas and rootlet sacrifice in schwannomas.

The overlooked first intercostal ligament: Does it help to stabilize the Weberian apparatus?

AbstractThe Weberian apparatus is a novel hearing adaptation that facilitates increased hearing sensitivity in otophysan fishes. The apparatus is a complex system composed of modifications to anterior vertebral elements, the inner ear, and the swim bladder. A critical piece of the system that often receives minor attention are the various ligaments that bridge these three regions. The most famous of the ligaments is the interossicular ligament, which connects the Weberian ossicle chain (scaphium–intercalarium–tripus). Several other ligaments are present, including the suspensor (tripus to parapophysis 4) and the triple ligament (tripus–os suspensorium–tunica externa). Here, by combining diffusible iodine‐based contrast enhancement (DICE) and propagation phase‐contrast synchrotron radiation micro‐computed tomography (PPC‐SRμCT) with classic histological methods, we shine new light on the first intercostal ligament (ICL1) and discuss its potential function in relation to the Weberian apparatus. ICL1 is nearly absent from the cypriniform literature, typically only mentioned in a general discussion together with other intercostal ligaments. This study examines the development and structure of ICL1 comparatively with the other definitive Weberian ligaments in the zebrafish (Danio rerio). We provide a comprehensive view of three‐dimensional shape, development, and composition to generate hypotheses regarding potential functions of ICL1 within the greater Weberian apparatus. Given new detail presented herein regarding the structure of ICL1, modifications to rib 5 and parapophysis 4 for ICL1 attachment, and the alignment of ICL1 with the os suspensorium, we propose a supportive (anchoring) role of ICL1 to aid in minimizing non‐optimal movement of the structures of the fourth vertebra. This addition would focus vibrations anteriorly through the ossicle chain with minimal signal loss in zebrafish and other species with similar Weberian apparatus morphologies. We conclude that ICL1 should be included in future analyses of Weberian apparatus function where ligaments are addressed.

Underwater Image Enhancement Methods Using Biovision and Type-II Fuzzy Set

Accurately extracting underwater images has never been more challenging, as the lack of clarity of detail due to issues such as scattering and light absorption is more noticeable than ever before. This research method addresses these problems while clarifying the limitations of existing methods and proposes a comprehensive approach to underwater image processing. Current methods tend to focus only on the effects of individual factors, such as color shifts, visibility, or contrast enhancement, and do not take into account biological vision applications. In contrast, the method proposed in this paper applies a color correction module that takes into account the effects of biological vision in LAB color space, and an enhanced Type-II Fuzzy set visibility enhancement module. This synchronized approach overcomes the limitations of the previous methods in that the contrast enhancement utilizes a curve transform and a multi-scale fusion strategy that preserves the essential image details. The framework not only adjusts the overall image features, but also finely handles the local details, resulting in a significant enhancement of both the overall quality and the local detail clarity of underwater images. The experimental results demonstrate that the application of the method of this study on two datasets gives results that are better than those of the top 10 existing algorithms. By explicitly addressing the limitations of existing methods, the method becomes an advantageous solution in underwater image processing, providing enhancements in image quality and task-specific applications in a concise and efficient manner.

Predictive value of contrast-enhanced MRI for the regrowth of residual uterine fibroids after high-intensity focused ultrasound treatment.

To investigate whether the signal intensity (SI) ratio of residual fibroid (RF) to myometrium using Contrast-Enhanced Magnetic Resonance Imaging (CE-MRI) could predict fibroid regrowth after high-intensity focused ultrasound (HIFU) treatment. A retrospective analysis was conducted among 164 patients with uterine fibroids who underwent HIFU. To predict the RF regrowth, the SI perfusion parameters were quantified using the RF-myometrium SI ratio on CE-MRI on day 1 post-HIFU and then compared with the fibroid-myometrium SI ratio on the T2-weighted image (T2WI) and Funaki classification 1 year later. Thirty cases from another center were used as an external validation set to evaluate the performance of RF-myometrium SI ratio. The predictive performance of the RF-myometrium SI ratio on CE-MRI on day 1 post-HIFU (Area Under Curve, AUC: 0.869) was superior to that of the preoperative and postoperative fibroid-myometrium SI ratios on the T2WI (AUC: 0.724, 0.696) and Funaki classification (AUC: 0.663, 0.623). Multivariate analysis showed that the RF- myometrium SI ratio and RF thickness were independent factors. The RF-myometrium SI ratio reflects the long-term rate of re-intervention (r = 0.455, p < 0.001). The RF-myometrium SI ratio on CE-MRI exhibits greater accuracy in predicting RF regrowth compared to the SI classification and the SI ratio on T2WI. The ratio of residual uterine fibroid to myometrial signal intensity on contrast-enhanced (CE)-MRI can reflect residual blood supply, predict regrowth of fibroids, and thus reflect long-term re-intervention rate and recovery situation of clinical high-intensity focused ultrasound (HIFU) treatment. Contrast-enhanced (CE)-MRI can indicate the blood supply of residual uterine fibroids after high-intensity focused ultrasound (HIFU) treatment. The predictive capability of CE-MRI ratio surpasses T2WI ratio and the Funaki. Residual fibroids can serve as a measure of the long-term efficacy of HIFU.

An intelligent magnetic resonance imagining-based multistage Alzheimer's disease classification using swish-convolutional neural networks.

Alzheimer's disease (AD) refers to a neurological disorder that causes damage to brain cells and results in decreasing cognitive abilities and memory. In brain scans, this degeneration can be seen in different ways. The disease can be classified into four stages: Non-demented (ND), moderate demented (MoD), mild demented (MiD), and very mild demented (VMD). To prepare the raw dataset for analysis, the collected magnetic resonance imaging (MRI) images are subjected to several pre-processing techniques in order to improve the performance accuracy of the proposed model. Medical images generally have poor contrast and get affected by noise, which ends up with inaccurate diagnosis. For the different phases of AD to be detected, a clear image is necessary. To address this issue, the influence of the artefacts must be reduced, enhance the contrast, and reduce the loss of information. A novel framework for image enhancement is suggested to increase the accuracy in the detection and identification of AD. In this study, the raw MRI dataset from the Alzheimer's disease neuroimaging initiative (ADNI) database is subjected to skull stripping, contrast enhancement, and image filtering followed by data augmentation to balance the dataset with four types of Alzheimer's classes. The pre-processed data are subjected to five different pre-trained models such as AlexNet, ResNet, VGG 16, EfficientNet, and Inceptionv3 achieving a testing accuracy rate of 91.2%, 88.21%, 92.34%, 93.45%, and 85.12%, respectively. These pre-trained models are compared with the proposed CNN (convolutional neural network) model designed with Adam optimizer and Flatten Swish activation function which reaches the highest accuracy of 96.5% with a learning rate of 0.000001. The five pre-trained CNNmodels along with the proposed swish-based AD-CNN were tested using various performance metrics to evaluate the model efficiency in classifying and identifying the AD classes. From the result analysis, it is evident that the proposed AD-CNN model outperforms all the other models.

Computed Tomography and Magnetic Resonance Imaging Features of Gracilis Myopathy in Dogs.

Gracilis myopathy is a well-recognized syndrome of weight-bearing gait abnormality; however, detailed description of computed tomography (CT) and magnetic resonance imaging (MRI) features of gracilis myopathy is lacking in the literature. This study aims to describe in detail CT and MRI features of gracilis myopathy. This is a retrospective study. Medical records of dogs with characteristic gait and available pelvic limb MRI or CT, presenting to four referral institutions, were reviewed. Seven cases with MRI (3/7) or CT (4/7) were included, all German Shepherd dogs. MRI findings in all cases included a well-defined, T1-/T2-/proton density-weighted hypointense band along the axial margin of the gracilis muscle, shorter affected muscles, thickening and T2W/STIR hyperintensity with contrast enhancement at the symphyseal tendon, and concurrent focal indentation of the axial border of the gracilis muscle. CT findings included a hyperattenuating band (4/4), single instances of contrast enhancement at the origin of the muscle, thickening of the tendon at the origin and insertion, and concurrent thickening at the musculotendinous muscle. CT and MRI can present advantages in correctly identifying the specific muscles affected within the gracilis-semitendinosus muscle complex. Both modalities can localize anatomically lesions in detail, for instance at the origin or insertion of the muscle, which could present advantages in novel treatment strategies for this condition.

Reduction in Synovitis Following Genicular Artery Embolization in Knee Osteoarthritis: A Prospective Ultrasound and MRI Study

Background/Objectives: Genicular artery embolization (GAE) has demonstrated potential as a treatment for knee osteoarthritis by targeting inflammation and pain, although current evidence remains limited. This study used imaging biomarkers to objectively assess synovitis and possible ischemic complications following GAE. Methods: This was a prospective, single-center trial including participants with mild-to-moderate knee osteoarthritis. Ultrasound, contrast-enhanced (CE), and non-CE-MRI were performed two days before and one and six months after GAE. Ultrasound biomarkers included synovial hypertrophy, effusion, and Doppler activity. A combined effusion-synovitis score was assessed on non-CE-MRI, while CE-MRI allowed differentiation between synovium and effusion and was used to calculate whole-joint and local synovitis scores. The post-GAE MRIs were reviewed for ischemic complications.Results: Seventeen participants (aged 43–71) were treated. Significant reductions were observed in ultrasound-assessed synovial hypertrophy and Doppler activity, as well as in CE-MRI local and whole-joint synovitis scores. While reductions in effusion were noted in both ultrasound and MRI, these changes did not reach statistical significance. At one month, MRI revealed three cases of nonspecific osteonecrosis-like areas, which resolved completely by six months.Conclusions: This study demonstrated a reduction in synovitis and no permanent ischemic complication following GAE in knee osteoarthritis. Larger studies with longer follow-up are needed to confirm the long-term efficacy and safety of the procedure.

Infrared Multiobject Contrast Enhancement and Detection Based on Layered Visual Transformer Network for Autonomous Driving

Infrared Multiobject Contrast Enhancement and Detection Based on Layered Visual Transformer Network for Autonomous Driving

NOT THE USUAL SUSPECTS: GLIOBLASTOMA OF THE CEREBELLOPONTINE ANGLE MASQUERADING AS A VESTIBULAR SCHWANNOMA

Introduction Most tumoral processes involving the cerebellopontine angle (CPA) are extraneuraxial and usually comprise of vestibular schwannomas and meningiomas, followed by less common lesions such as epidermoid cysts, metastases and arachnoid cysts. Glioblastomas located in the CPA are rarely diagnosed. Uncommonly, intraneuraxial tumors in the CPA may grow in an exophytic manner compressing the surrounding cranial nerves, resembling the clinical picture of a more common extraneuraxial tumour. Clinical Case A 47 year old male presented to our department accusing gait imbalance, vertigo, vomiting and nausea, symptoms that started 2 months prior to admission. Upon neurological examination, the patient additionally presented right facial paresis and hypoesthesia, right sided hypoacusis, hypogeusia and diplopia. A contrast enhanced cerebral MRI revealed a right cerebellopontine angle mass with inhomogenous contrast enhancement, relatively well delineated, exerting mass effect on the cerebellum and pons with reduced perilesional edema but with involvement of the internal acoustic meatus. The indication for surgical removal of the tumour was made. The patient was placed in sitting position and a right retrosigmoid approach was performed using the operating microscope. Upon retraction of the cerebellar hemisphere, a greyish, highly vascularized and soft lesion was seen, exhibiting a spread growth, engulfing the V, VII and VIII nerves, without infiltrating them, resembling anything but a schwannoma. A frozen section of the tumour revealed a surprising result: high grade glioma. The surgical excision continued in a piece-meal fashion, using the ultrasonic aspirator with preservation of the cranial nerves until an almost complete removal was achieved. The immediate postop evolution of the patient was favourable. Nevertheless, the final histopathological diagnosis confirmed a grade IV glioblastoma. Oncological treatmend followed, but the tumour relapsed in 2 months after surgery, the control MRI revealing a multilocular tumoral dissemination in the cerebellum and brainstem which was surgically unsalvageable. Conclusion Glioblastoma of the CPA, manifesting both imagistically and clinically as a vestibular schwannoma is a rare entity but nevertheless extant, that dramatically changes the prognosis and postop evolution of a patient. Physicians involved in the diagnosis and treatment of such tumours must always be suspicious of the usual suspects in the CPA.

Pancreatic Neuroendocrine Neoplasms: Classification and Novel Role of Endoscopic Ultrasound in Diagnosis and Treatment Personalization.

The incidence and prevalence of pancreatic neuroendocrine neoplasms are steadily increasing. These tumors are highly heterogeneous, with treatment options ranging from observation to surgery, and various medical therapies. The choice of treatment is influenced by factors such as tumor stage, grade (proliferative activity), and the presence of hormone-related syndromes. Endoscopic ultrasound (EUS) is becoming increasingly valuable for assessing pancreatic neuroendocrine neoplasms, offering detailed morphological, vascular, and functional information through techniques such as contrast enhancement and elastography. It also allows biopsies that are useful for both histopathological and molecular analyses. These tumors are highly heterogeneous, with treatment options ranging from observation to various medical therapies and surgery. Recent data suggest that small, non-functioning PanNENs with low proliferation rates may be safely monitored, whereas more aggressive or functioning tumors typically require surgery. EUS-guided ablation is a promising alternative for patients with functional pancreatic neuroendocrine neoplasms who are unsuitable for surgery, although randomized trials are needed. In non-resectable pancreatic neuroendocrine neoplasms, treatment options include somatostatin analogs, targeted therapies (e.g., everolimus, sunitinib), chemotherapy, and radioligand therapy. This review discusses key factors in planning personalized treatment strategies for pancreatic neuroendocrine neoplasms.

DEALING WITH TWO SYNCHRONOUS LESIONS

Introduction Multiple synchronous intracranial lesions present a significant diagnostic and therapeutic challenge, in most cases appearing as metastatic tumors or same-cell type tumors. Case Report We report the case of a 73-year-old patient admitted in our department with light left hemiparesis (MRC MMT 4/5), light left hypoesthesia and transitory and remitted state of temporo-spatial confusion that debuted around six weeks prior to presentation. The neurological exam confirmed the sensorimotor deficit and displayed the existence of left neglect syndrome. The patient’s MRI revealed a giant temporo-parieto-occipital mass almost 7 cm in diameter that appeared to have an intraventricular starting point and showed a relatively homogeneous contrast enhancement with significant mass effect. A concomitant superficial parieto-temporal lesion was also noted. Both lesions were excised in a successive manner from superficial to deep, using a right parieto-temporal craniotomy. While the giant mass had a yellowish color and an extremely hard consistency, the superficial one was easily suctionable and had a grey-reddish color and was extremely well vascularized, raising the suspicion of concomitant brain lesions of different histology. Postoperatively the patient underwent a routine thoracic CT scan in the context of a position related soft tissue injury that revealed the existence of two pulmonary lesions, further deepening our suspicion of concomitant brain lesions of different histology. Conclusions Although extremely rare, the appearance of two histologically distinct brain lesions in patients with no evidence of a genetic condition should be taken into consideration especially in the context of intraoperative differences in order to successfully guide the patients’ follow-up treatment.

Development and Clinical Evaluation of a Contrast Optimizer for Contrast-Enhanced CT Imaging of the Liver

Objective Patient characteristics, iodine injection, and scanning parameters can impact the quality and consistency of contrast enhancement of hepatic parenchyma in CT imaging. Improving the consistency and adequacy of contrast enhancement can enhance diagnostic accuracy and reduce clinical practice variability, with added positive implications for safety and cost-effectiveness in the use of contrast medium. We developed a clinical tool that uses patient attributes (height, weight, sex, age) to predict hepatic enhancement and suggest alternative injection/scanning parameters to optimize the procedure. Methods The tool was based on a previously validated neural network prediction model that suggested adjustments for patients with predicted insufficient enhancement. We conducted a prospective clinical study in which we tested this tool in 24 patients aiming for a target portal-venous parenchyma CT number of 110 HU ± 10 HU. Results Out of the 24 patients, 15 received adjustments to their iodine contrast injection parameters, resulting in median reductions of 8.8% in volume and 9.1% in injection rate. The scan delays were reduced by an average of 42.6%. We compared the results with the patients' previous scans and found that the tool improved consistency and reduced the number of underenhanced patients. The median enhancement remained relatively unchanged, but the number of underenhanced patients was reduced by half, and all previously overenhanced patients received enhancement reductions. Conclusions Our study showed that the proposed patient-informed clinical framework can predict optimal contrast enhancement and suggest empiric injection/scanning parameters to achieve consistent and sufficient contrast enhancement of hepatic parenchyma. The described GUI-based tool can prospectively inform clinical decision-making predicting optimal patient's hepatic parenchyma contrast enhancement. This reduces instances of nondiagnostic/insufficient enhancement in patients.

Infantile Sinonasal Tract Myxomas with Orbital Involvement: Presentation of Two Cases and Comprehensive Literature Review

Objective: Our aim is to present two cases of infantile sinonasal tract myxoma with orbital involvement and conduct a comprehensive literature review of the topic. We aim to provide a summary of the presentation of infantile sinonasal tract myxomas to effectively aid clinicians in considering this rare entity as a potential diagnosis. Methods: We present a case series and a retrospective review of the published literature in the English language. A search was conducted between 1945 and 2023 on sinonasal myxoma. Cases of infantile (&lt;3 years) sinonasal myxoma in the literature were reported as well as two cases of our own. Data was collected from each of the identified articles on the age of presentation, sex, initial presentation, tumor location, imaging, pathology, and treatment. Results: Forty-eight cases of sinonasal myxoma in children &lt;3 years of age were identified. These cases had a slight male preponderance (F:M, 1:1.39) and an average age of diagnosis of sixteen months. Of the cases included, the majority involved the maxilla and maxillary sinuses (83.3%) and commonly presented with a painless slow-growing mass in the region of the tumor. Characteristic CT findings are homogenous solid masses with heterogeneous contrast enhancement. On MRI, these lesions appear hypointense on T1 with highly variable contrast enhancement and hyperintense on T2. Conclusions: We present two cases of infantile sinonasal myxoma with secondary orbital involvement followed by a major review. Treatment of these cases with surgical excision (confirmed clear margins) provided 0% rates of recurrence in the reported cases. Due to the rarity of these tumors and recent histological reclassification, a comprehensive review of this condition will assist clinicians in their management of it.

Automatic face detection based on bidirectional recurrent neural network optimized by improved Ebola optimization search algorithm.

Face detection is a multidisciplinary research subject that employs fundamental computer algorithms, image processing, and patterning. Neural networks, on the other hand, have been widely developed to solve challenges in the domains of feature extraction, pattern detection, and the like in general. The presented study investigates the DNN (deep neural networks) use in the creation of facial detection operating systems. In this study, a novel optimized deep network has been presented to face detection. In this paper, after using some preprocessing stages for contrast enhancement and increasing the data number for the next deep tool, they fed to a bidirectional recurrent neural network (BRNN). The network is optimized via a novel enhanced version of Ebola optimization algorithm to provide far greater accuracy. The suggested procedure is examined on GTFD (Georgia Tech Face Database) and the results indicate that the proposed technique significantly outperforms other comparative methods, attaining an accuracy of 94.3%, a precision of 93.51%, a recall of 94.53%, and an F1-score of 92.47%. Furthermore, the method exhibits resilience against various challenges, achieving an accuracy of 95.6% under occlusions, 96.3% under lighting variations, 94.8% under pose variations, and 92.4% under low resolution conditions. Simulation results depict that the suggested technique gives far greater accuracy in comparison with the other comparative approaches.

Single-point mutated lanmodulin as a high-performance MRI contrast agent for vascular and kidney imaging.

Magnetic resonance imaging contrast agents can enhance diagnostic precision but often face limitations such as short imaging windows, low tissue specificity, suboptimal contrast enhancement, or potential toxicity, which affect resolution and long-term monitoring. Here, we present a protein contrast agent based on lanmodulin, engineered with a single-point mutation at position 108 from N to D to yield maximum gadolinium binding sites. After loading with Gd3+ ions, the resulting protein complex, LanND-Gd, exhibits efficient renal clearance, high relaxivity, and prolonged renal retention compared to commercial agents. LanND-Gd enables high-performance visualization of whole-body structures and brain vasculature in male mice at a resolution finer than one hundred micrometers. In male ischemia mouse models, LanND-Gd also improves kidney dysfunction monitoring while minimizing risks of neural toxicity or immunogenic reactions. This protein-based contrast agent offers superior image quality, improved biocompatibility, and extended imaging timeframes, promising significant advancements in magnetic resonance-based diagnostics and patient outcomes.

RADIOMICS APPLICATIONS IN NON-CONTRAST CINE CARDIAC MAGNETIC RESONANCE IMAGING IN THE DETECTION OF LEFT VENTRICULAR POSTINFARCTION CARDIOSCLEROSIS

BACKGROUND: Size and localization, as well as clear differentiation between intact tissue and infarcted area, are important for clinical diagnosis and precision medicine. This work is based on the study of radiomics features capable of differentiating areas of infarcted and distant tissue from the infarct area using data from non-contrast cine cardiac MRI imaging. AIM: Evaluation of the possibilities and informativeness of radiomics analysis in detection of postinfarction areas of the left ventricular myocardium in patients with ischemic cardiomyopathy by contrast-free cine cardiac MRI images. MATHERIALS AND METHODS: The results of contrast enhancement cardiac MRI with of 33 patients undergoing surgical treatment for ischemic cardiomyopathy were analyzed. Texture analysis was performed on 66 sections of cine cardiac MRI images, and 105 texture characteristics were determined for each of them. Cardiac MRI was performed according to the standard technique on a Vantage Titan (Toshiba) 1.5 T. Texture analysis was performed using 3D slicer-version 5.2.2 software, SlicerRadiomics RESULTS: Collinearity diagrams of the features were constructed. We also identified the features with zero importance and established the importance of the features using the gradient boosting algorithm. In addition, the cumulative importance of the features as a function of their total number was estimated. Using the method of identifying features with low importance, we identified the parameters with the lowest importance that do not affect the indicated total level. Using the method of identifying features with a single value, we did not find features with a single unique value. In addition, ROC curve for Lasso logistic regression (Se =57.14% Sp=71.43%, AUC=0.76) was generated based on the results of the analysis. The main result of this study is the identification of radiomics features able to characterize the areas corresponding to postinfarction cardiosclerosis and intact LV wall on the basis of cine cardiac MRI images. CONCLUSION: This study has shown that the use of radiomics analysis on contrast-free cine cardiac MRI images is a promising approach to identify areas corresponding to myocardial infarction and intact wall and could potentially be used as to identify areas of postinfarction cardiosclerosis in patients with ischemic cardiomyopathy without the use of contrast agents.