Computed Tomography Medical Images Research Articles

Reservoir Computing Based on Oxygen-Vacancy-Mediated X-ray Optical Synaptic Device for Medical CT Bone Diagnosis.

Recognition and judgment of X-ray computed tomography (CT) images play a crucial role in medical diagnosis and disease prevention. However, the storage and calculation of the X-ray imaging system applied in the traditional CT diagnosis is separate, and the pathological judgment is based on doctors' experience, which will affect the timeliness and accuracy of decision-making. In this paper, a simple-structured reservoir computing network (RC) is proposed based on Ga2O3 X-ray optical synaptic devices to recognize medical skeletal CT images with high accuracy. Through oxygen vacancy engineering, Ga2O3 X-ray optical synaptic devices with adjustable photocurrent gain and a persistent photoconductivity effect were obtained. By using the Ga2O3 X-ray optical synaptic device as a reservoir, we constructed an RC network for medical skeletal CT diagnosis and verified its image recognition capability using the MNIST data set with an accuracy of 78.08%. In the elbow skeletal CT image recognition task, the recognition rate is as high as 100%. This work constructs a simple-structured RC network for X-ray image recognition, which is of great significance in applications in medical fields.

Comparing Radiation Doses in CBCT and Medical CT Imaging for Dental Applications

ABSTRACT Background: Dental imaging plays a crucial role in diagnosis and treatment planning, with cone-beam computed tomography (CBCT) and medical computed tomography (CT) being two common modalities. This study aims to compare the radiation doses associated with CBCT and medical CT imaging in dental applications to assess their relative safety and efficacy. Materials and Methods: We conducted a retrospective study using data from 100 patients who underwent both CBCT and medical CT scans for dental purposes. The radiation doses were measured in terms of dose-length product (DLP) for medical CT and dose-area product (DAP) for CBCT. The effective dose (ED) was calculated using appropriate conversion factors. Patient demographics, scan parameters, and radiation doses were recorded and analyzed. Results: The results indicated that the mean DLP for medical CT scans was 220 mGycm, whereas the mean DAP for CBCT scans was 150 mGycm². The corresponding mean effective doses for medical CT and CBCT were 2.5 mSv and 1.8 mSv, respectively. The radiation dose from CBCT was found to be approximately 28% lower than that from medical CT. Conclusion: This study demonstrates that CBCT imaging for dental applications results in significantly lower radiation doses compared to medical CT. While both modalities provide valuable diagnostic information, the choice of imaging technique should consider the balance between diagnostic quality and radiation exposure, especially for pediatric and high-risk patients. Dental practitioners should be aware of the potential dose reduction benefits associated with CBCT when appropriate for the clinical scenario.

Comparing Radiation Doses in CBCT and Medical CT Imaging for Dental Applications.

We are always concerned about radiation exposure during dental imaging procedures. We explore the crucial differences in radiation doses between Cone Beam Computed Tomography (CBCT) and Medical computed tomography (CT) imaging, aiming to shed light on the safety and efficiency of these techniques. In this study, we conducted a comprehensive analysis using state-of-the-art dental imaging equipment. We employed phantoms that simulated real dental scenarios, ensuring accuracy in our measurements. The radiation doses were measured with precision dosimeters, and various exposure settings were tested to obtain a comprehensive dataset. Our findings reveal substantial differences in radiation doses between CBCT and Medical CT for dental applications. In the case of CBCT, the average effective dose was found to be approximately 100 microsieverts (μSv), making it a preferable choice for routine dental imaging. Medical CT, on the other hand, yielded significantly higher radiation exposure, with an average effective dose exceeding 500 μSv, emphasizing its need for specific clinical scenarios. In conclusion, the choice between CBCT and Medical CT for dental applications should be made with careful consideration of radiation dose implications. CBCT emerges as the safer and more efficient option for routine dental imaging, offering a lower radiation burden to patients while still delivering high-quality diagnostic images. However, Medical CT may be necessary for specialized cases where the additional radiation risk is justified by diagnostic requirements.

Langerhans cell histiocytosis of the thyroid mimicking thyroiditis in a boy: a case report and literature review

BackgroundLangerhans cell histiocytosis affecting the thyroid commonly presents with nonspecific clinical and radiological manifestations. Thyroid Langerhans cell histiocytosis is typically characterized by non-enhancing hypodense lesions with an enlarged thyroid on computed tomography medical images. Thyroid involvement in LCH is uncommon and typically encountered in adults, as is salivary gland involvement. Therefore, we present a unique pediatric case featuring simultaneous salivary and thyroid involvement in LCH.Case presentationA 3-year-old boy with complaints of an anterior neck mass persisting for 1 to 2 months, accompanied by mild pain, dysphagia, and hoarseness. A physical examination revealed a 2.5 cm firm and tender mass in the left anterior neck. Laboratory examinations revealed normal thyroid function test levels. Ultrasonography revealed multiple heterogeneous hypoechoic nodules with unclear and irregular margins in both lobes of the thyroid. Contrast-enhanced neck computed tomography revealed an enlarged thyroid gland and bilateral submandibular glands with non-enhancing hypointense nodular lesions, and multiple confluent thin-walled small (< 1.5 cm) cysts scattered bilaterally in the lungs. Subsequently, a left thyroid excisional biopsy was performed, leading to a histopathological diagnosis of LCH. Immunohistochemical analysis of the specimen demonstrated diffuse positivity for S-100, CD1a, and Langerin and focal positivity for CD68. The patient received standard therapy with vinblastine and steroid, and showed disease regression during regular follow-up of neck ultrasonography.ConclusionsInvolvement of the thyroid and submandibular gland as initial diagnosis of Langerhans cell histiocytosis is extremely rare. It is important to investigate the involvement of affected systems. A comprehensive survey and biopsy are required to establish a definitive diagnosis.

Unsupervised metal artifacts reduction network for CT images based on efficient transformer

In computed tomography (CT) imaging, artifacts caused by metals can seriously interfere with the accurate judgment, evaluation, and analysis of object structures. Deep learning presented new opportunities for minimizing metal artifacts. The currently proposed supervised methods mainly resort to simulated data for network training, which is difficult to be applied in real scenarios. Unsupervised methods do not completely suppress artifacts and are prone to instability in the corrected results. Therefore, this paper proposes an unsupervised CycleGAN model based on the efficient Transformer for metal artifact reduction in CT images. The encoder and decoder based on the efficient Transformer achieve the extraction and processing of global image content features and artifact features. By correlating global features through attention mechanisms, vital features are generated to enhance the consistency of recovery information. The efficient Transformer framework reduces the computational complexity of the attention mechanism, allowing efficient processing of standard medical CT images with 512 × 512 pixels. Projection preserving loss uses the metal trace outer projection as a fidelity constraint to overcome the problem that unsupervised networks are difficult to train and prone to spurious information. Experimental results indicate that the proposed method outperforms the original CycleGAN and ADN networks in suppressing metal artifacts while avoiding introducing spurious structures. Specifically, the proposed method improved the PSNR of the correction results by 9.23% and reduced the NMAD by 13.21% compared to the original CycleGAN, and improved the PSNR by 5.17% and reduced the NMAD by 5.57% compared to the ADN.

Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting.

Ensemble learning aims to improve prediction performance by combining several models or forecasts. However, how much and which ensemble learning techniques are useful in deep learning-based pipelines for pancreas computed tomography (CT) image classification is a challenge. Ensemble approaches are the most advanced solution to many machine learning problems. These techniques entail training multiple models and combining their predictions to improve the predictive performance of a single model. This article introduces the idea of Stacked Ensemble Deep Learning (SEDL), a pipeline for classifying pancreas CT medical images. The weak learners are Inception V3, VGG16, and ResNet34, and we employed a stacking ensemble. By combining the first-level predictions, an input train set for XGBoost, the ensemble model at the second level of prediction, is created. Extreme Gradient Boosting (XGBoost), employed as a strong learner, will make the final classification. Our findings showed that SEDL performed better, with a 98.8% ensemble accuracy, after some adjustments to the hyperparameters. The Cancer Imaging Archive (TCIA) public access dataset consists of 80 pancreas CT scans with a resolution of 512 * 512 pixels, from 53 male and 27 female subjects. A sample of two hundred and twenty-two images was used for training and testing data. We concluded that implementing the SEDL technique is an effective way to strengthen the robustness and increase the performance of the pipeline for classifying pancreas CT medical images. Interestingly, grouping like-minded or talented learners does not make a difference.

Multi-class Tissue Segmentation of CT images using an Ensemble Deep Learning method.

Microwave ablation (MWA) therapy is a well-known technique for locally destroying lung tumors with the help of computed tomography (CT) images. However, tumor recurrence occurs because of insufficient ablation of the tumor. In order to perform an accurate treatment of lung cancer, there is a demand to determine the tumor area precisely. To address the problem at hand, which involves accurately segmenting organs and tumors in CT images obtained during MWA therapy, physicians could benefit from a semantic segmentation method. However, such a method typically requires a large number of images to achieve optimal results through deep learning techniques. To overcome this challenge, our team developed four different (multiple) U-Net based semantic segmentation models that work in conjunction with one another to produce a more precise segmented image, even when working with a relatively small dataset. By combining the highest weight value of segmentation from multiple methods into a single output, we can achieve a more reliable and accurate segmentation outcome. Our approach proved successful in segmenting four different tissue structures, including lungs, lung tumors, and ablated tissues in CT medical images. The Intersection over Union (IoU) is employed to quantitatively evaluate the proposed method. The method shows the highest average IoU, with 0.99 for the background, 0.98 for the lung, 0.77 for the ablated, and 0.54 for the tumor tissue. The results show that employing multiple DL methods is superior to that of individual base-learner models for all four different tissue structures, even in the presence of the relatively small dataset.Clinical relevance- An essential issue of tumor ablation therapy is to know when the entire tumor tissue has completely been destroyed. However, as it is difficult to distinguish between destroyed and living tumor, this is hardly reliable in clinical practice during MWA therapy, especially when working with a small dataset. Improved AI segmentation methods can help to improve performance to reduce recurrence.

A histogram-driven generative adversarial network for brain MRI to CT synthesis

Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are commonly used tools for medical diagnostic assessment. Considering the ionizing radiation of CT imaging, estimating CT images from radiation-free MRI would be beneficial for medical diagnosis. Although state-of-art generative adversarial networks or end-to-end architecture models can generate realistic natural images, it is challenging to generate medical CT images with high signal-to-noise ratios and are paired with MRI. We propose a histogram-driven generative adversarial network (HisGAN) to address this issue, estimate CT images paired with MR, and develop a histogram-based dynamic scaling factor to facilitate learning different image styles. By employing an adversarial learning strategy to train the end-to-end generator, the generator better simulate the nonlinear mapping from source to target. For the generator, the proposed method applies multiple learnable parameters to adjust the overall weights of the dilated convolution layers to ensure sufficient expansive receptive fields for improved performance. Additionally, the method utilizes deep residual networks to train randomly smoothed generated images and employs adversarial loss to enhance the generation of the discriminator, achieving a balance between the generator and the discriminator. Our approach can synthesize image details at the pixel level in the target domain and has been evaluated using two datasets for MR to CT, T1 to T2, and Flair to T1ce modality synthesis tasks. The proposed method outperforms existing generative adversarial models applied to medical image synthesis.

Hemodynamic simulation in the aortic arch with the lattice Boltzmann method

Aortic diseases have high mortality rates, wherein wall shear stress (WSS) and oscillatory shear index play important roles. Previous studies focused on describing the WSS distribution; however, no report has investigated how hemodynamic parameters determine the distribution of WSS. This study investigates the parameters affecting the WSS distribution and determines the variations of these parameters. A realistic healthy aortic geometry is reconstructed from computed tomography medical images, and a flow simulation is performed using the lattice Boltzmann method. The inlet velocity waveform from the Doppler ultrasound measurement is imposed as the inlet boundary condition, whereas the three-element Windkessel model is used as the outlet boundary condition. The measured outlet flow rate waveforms are used to validate the simulation. A good agreement is found between the outlet flow rate waveform obtained from the measurement and that from the simulation: the descending artery, innominate artery, left common carotid, and left subclavian artery receive 63.42%, 24.01%, 4.14%, and 8.46%, respectively, of the total inlet flow rate over the cardiac cycle in the measurements and 62.17%, 24.61%, 4.7%, and 8.44%, respectively, in the simulation. The simulation shows that the temporal and spatial distributions of the WSS are separately determined by the flow rate and impacting angle. The flow rate ratio between the inlet and outlet decreases with an increase in Re. This relation can be fitted well by the exponential function. Moreover, the impacting angle between the blood flow and the vessel centerline is determined by the vessel geometry only.

Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets.

Recently, various methods have been developed to identify COVID-19 cases, such as PCR testing and non-contact procedures such as chest X-rays and computed tomography (CT) scans. Deep learning (DL) and artificial intelligence (AI) are critical tools for early and accurate detection of COVID-19. This research explores the different DL techniques for identifying COVID-19 and pneumonia on medical CT and radiography images using ResNet152, VGG16, ResNet50, and DenseNet121. The ResNet framework uses CT scan images with accuracy and precision. This research automates optimum model architecture and training parameters. Transfer learning approaches are also employed to solve content gaps and shorten training duration. An upgraded VGG16 deep transfer learning architecture is applied to perform multi-class classification for X-ray imaging tasks. Enhanced VGG16 has been proven to recognize three types of radiographic images with 99% accuracy, typical for COVID-19 and pneumonia. The validity and performance metrics of the proposed model were validated using publicly available X-ray and CT scan data sets. The suggested model outperforms competing approaches in diagnosing COVID-19 and pneumonia. The primary outcomes of this research result in an average F-score (95%, 97%). In the event of healthy viral infections, this research is more efficient than existing methodologies for coronavirus detection. The created model is appropriate for recognition and classification pre-training. The suggested model outperforms traditional strategies for multi-class categorization of various illnesses.

Medical Image Classifications for 6G IoT-Enabled Smart Health Systems

As day-to-day-generated data become massive in the 6G-enabled Internet of medical things (IoMT), the process of medical diagnosis becomes critical in the healthcare system. This paper presents a framework incorporated into the 6G-enabled IoMT to improve prediction accuracy and provide a real-time medical diagnosis. The proposed framework integrates deep learning and optimization techniques to render accurate and precise results. The medical computed tomography images are preprocessed and fed into an efficient neural network designed for learning image representations and converting each image to a feature vector. The extracted features from each image are then learned using a MobileNetV3 architecture. Furthermore, we enhanced the performance of the arithmetic optimization algorithm (AOA) based on the hunger games search (HGS). In the developed method, named AOAHG, the operators of the HGS are applied to enhance the AOA's exploitation ability while allocating the feasible region. The developed AOAG selects the most relevant features and ensures the overall model classification improvement. To assess the validity of our framework, we conducted evaluation experiments on four datasets, including ISIC-2016 and PH2 for skin cancer detection, white blood cell (WBC) detection, and optical coherence tomography (OCT) classification, using different evaluation metrics. The framework showed remarkable performance compared to currently existing methods in the literature. In addition, the developed AOAHG provided results better than other FS approaches according to the obtained accuracy, precision, recall, and F1-score as performance measures. For example, AOAHG had 87.30%, 96.40%, 88.60%, and 99.69% for the ISIC, PH2, WBC, and OCT datasets, respectively.

Metal Artifact Reduction in Dental CBCT Images Using Direct Sinogram Correction Combined with Metal Path-Length Weighting.

Metal artifacts in dental computed tomography (CT) images, caused by highly X-ray absorbing objects, such as dental implants or crowns, often more severely compromise image readability than in medical CT images. Since lower tube voltages are used for dental CTs in spite of the more frequent presence of metallic objects in the patient, metal artifacts appear more severely in dental CT images, and the artifacts often persist even after metal artifact correction. The direct sinogram correction (DSC) method, which directly corrects the sinogram using the mapping function derived by minimizing the sinogram inconsistency, works well in the case of mild metal artifacts, but it often fails to correct severe metal artifacts. We propose a modified DSC method to reduce severe metal artifacts, and we have tested it on human dental images. We first segment the metallic objects in the CT image, and then we forward-project the segmented metal mask to identify the metal traces in the projection data with computing the metal path length for the rays penetrating the metal mask. In the sinogram correction with the DSC mapping function, we apply the weighting proportional to the metal path length. We have applied the proposed method to the phantom and patient images taken at the X-ray tube voltage of 90 kVp. We observed that the proposed method outperforms the original DSC method when metal artifacts were severe. However, we need further extensive studies to verify the proposed method for various CT scan conditions with many more patient images.

Lightweight Classification Network for Pulmonary Tuberculosis Based on CT Images

With the continuous development of medical informatics and digital diagnosis, the classification of tuberculosis cases from computed tomography (CT) images of the lung based on deep learning is an important guiding aid in clinical diagnosis and treatment. Due to its potential application in medical image classification, this task has received extensive research attention. Existing related neural network techniques are still challenging in terms of feature extraction of global contextual information of images and network complexity in achieving image classification. To address these issues, this paper proposes a lightweight medical image classification network based on a combination of Transformer and convolutional neural network (CNN) for the classification of tuberculosis cases from lung CT. The method mainly consists of a fusion of the CNN module and the Transformer module, exploiting the advantages of both in order to accomplish a more accurate classification task. On the one hand, the CNN branch supplements the Transformer branch with basic local feature information in the low level; on the other hand, in the middle and high levels of the model, the CNN branch can also provide the Transformer architecture with different On the other hand, in the middle and high levels of the model, the CNN branches can also provide different local and global feature information to the Transformer architecture to enhance the ability of the model to obtain feature information and improve the accuracy of image classification. A shortcut is used in each module of the network to solve the problem of poor model results due to gradient divergence and to optimise the effectiveness of TB classification. The proposed lightweight model can well solve the problem of long training time in the process of TB classification of lung CT and improve the speed of classification. The proposed method was validated on a computed tomography (CT) image dataset provided by the First Hospital of Lanzhou University. The experimental results show that the proposed lightweight classification network for tuberculosis based on CT medical images of lungs can fully extract the feature information of the input images and obtain high accuracy classification results.

Coronavirus covid-19 detection by means of explainable deep learning

The coronavirus is caused by the infection of the SARS-CoV-2 virus: it represents a complex and new condition, considering that until the end of December 2019 this virus was totally unknown to the international scientific community. The clinical management of patients with the coronavirus disease has undergone an evolution over the months, thanks to the increasing knowledge of the virus, symptoms and efficacy of the various therapies. Currently, however, there is no specific therapy for SARS-CoV-2 virus, know also as Coronavirus disease 19, and treatment is based on the symptoms of the patient taking into account the overall clinical picture. Furthermore, the test to identify whether a patient is affected by the virus is generally performed on sputum and the result is generally available within a few hours or days. Researches previously found that the biomedical imaging analysis is able to show signs of pneumonia. For this reason in this paper, with the aim of providing a fully automatic and faster diagnosis, we design and implement a method adopting deep learning for the novel coronavirus disease detection, starting from computed tomography medical images. The proposed approach is aimed to detect whether a computed tomography medical images is related to an healthy patient, to a patient with a pulmonary disease or to a patient affected with Coronavirus disease 19. In case the patient is marked by the proposed method as affected by the Coronavirus disease 19, the areas symptomatic of the Coronavirus disease 19 infection are automatically highlighted in the computed tomography medical images. We perform an experimental analysis to empirically demonstrate the effectiveness of the proposed approach, by considering medical images belonging from different institutions, with an average time for Coronavirus disease 19 detection of approximately 8.9 s and an accuracy equal to 0.95.

Novel bio-inspired 3D porous scaffold intended for bone-tissue engineering: Design and in silico characterisation of histomorphometric, mechanical and mass-transport properties

The design of novel biomimetic bone tissue scaffolds (BTS) using computer aided design (CAD) technology is challenging additive manufacturing technologies. At the microstructure level, BTS should mimic bone histomorphometry and assure optimum mass transport and mechanical properties. In this study, a novel BTS has been designed, by using a parametric and variational CAD method, to model a bio-inspired and interconnected porous structure. The mechanical (elastic modulus) and the fluid mass transport (permeability) properties have been computed and compared to other implicit surfaces modelling scaffolds. The results showed that the new BTS could be tuned during the design stage to match the microstructure and the histomorphometry properties of trabecular bone. Those with porosities between 0.7 and 0.9 and highly smooth curvatures were the most appropriates. The new BTS, once appropriately designed, could be made/manufactured by 3D printing technology with an internal microstructure mimicking the local bone properties of the selected bone volumes of interest, for example, those coming from computed tomography medical images.

Hydrocephalus classification in brain computed tomography medical images using deep learning

Recent technological advancements, like big data analytics, is driving the growing adoption of cyber-physical systems and digital twins in the area of healthcare. Congenital hydrocephalus is one important example of recent healthcare data analytics. Congenital hydrocephalus is a buildup of excess cerebrospinal fluid (CSF) in the brain at birth. Congenital hydrocephalus can be lethal without treatment and represents an urgent issue in present-day clinical practice. Congenital hydrocephalus has a significant effect on a human entire life since it causes damage to the brain. It is important to accurately diagnose hydrocephalus early, which will help in the early treatment of the infant by a surgical procedure called ventriculoperitoneal (VP) shunt which will reduce the damage caused by hydrocephalus on the brain. Deep Learning is an evolving technology that is currently actively researched in the field of radiology. Compared to the traditional hydrocephalus diagnosing techniques, automatic diagnosing algorithms in deep learning can save diagnosis time, improve diagnosing accuracy, reduce cost, and reduce the radiologist's workload. In this paper, we have used a novel dataset collected from king Hussein medical center hospital in Jordan that consists of CT scans for hydrocephalus and non-hydrocephalus infants, the dataset has gone through multiple stages in preprocessing which are; cropping and filtering, normalization, segmentation (three segmentation techniques have been applied), and augmentation. These data have been used to build deep learning and machine learning models that will help physicians in the early and accurate diagnosing of congenital hydrocephalus which will lead to a decrease in the death rate and brain damage. The results of our models were impressive with a 98.5% accuracy for congenital hydrocephalus classification in infants' brain CT images.

A novel denoising method for CT images based on U-net and multi-attention

Reducing the radiation dose may lead to increased noise in medical computed tomography (CT), which can adversely affect the radiologists' judgment. Many efforts have been devoted to the denoising of low-dose CT (LDCT) images. However, it is often observed that denoised medical images usually lose some important clinical lesion edge information and may affect doctors’ clinical diagnosis. For a denoising neural network, it is expected that the neural network can well retain the detailed features and make the network more anthropomorphic, and to simulate the attention mechanism of observation, being a valuable feature of the thinking process of human brain. Based on U-network (U-Net) and multi-attention mechanism, a novel denoising method for medical CT images is proposed in this study. To obtain different feature information in CT images, three attention modules are proposed in our method. The local attention module is developed to localize the surrounding information of the feature map and calculate each pixel from the context extracted from the feature map. The multi-feature channel attention module can automatically learn and extract features, suppress some invalid information and add different weights to each channel in the feature map according to different tasks. The hierarchical attention module allows the deep neural network to extract a large amount of feature information. This study also introduces an enhanced learning module to learn and retain the detail information in the image by stacking multi-layer convolution layer, batch normalization (BN) layer and activation function layer to increase the network depth. Experimental studies are conducted, and comparisons with the state-of-the-art networks are made, and the results demonstrate that the developed method can effectively remove the noise in CT images and improve the image quality in the evaluation metrics of peak signal to noise ratio (PSNR) and structural similarity (SSIM). Our method achieved 34.7329 of PSNR and 0.9293 of SSIM for σ = 10 on the QIN_LUNG_CT dataset, and achieved 28.9163 of PSNR and 0.8602 of SSIM on the Mayo Clinic LDCT Grand Challenge dataset.

A Method Noise-Based Convolutional Neural Network Technique for CT Image Denoising

Medical imaging is a complex process that capitulates images created by X-rays, ultrasound imaging, angiography, etc. During the imaging process, it also captures image noise during image acquisition, some of which are extremely corrosive, creating a disturbance that results in image degradation. The proposed work addresses the challenge to eliminate the corrosive Gaussian additive white noise from computed tomography (CT) images while preserving the fine details. The proposed approach is synthesized by amalgamating the concept of method noise with a deep learning-based framework of a convolutional neural network (CNN). The corrupted images are obtained by explicit addition of Gaussian additive white noise at multiple noise variance levels (σ = 10, 15, 20, 25). The denoised images obtained are then evaluated according to their visual quality and quantitative metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). These metrics for denoised CT images are then compared with their respective values for the reference CT image. The average PSNR value of the proposed method is 25.82, the average SSIM value is 0.85, and the average computational time is 2.8760. To better understand the proposed approach’s effectiveness, an intensity profile of denoised and original medical images is plotted and compared. To further test the performance of the proposed methodology, the results obtained are also compared with that of other non-traditional methods. The critical analysis of the results shows the commendable efficiency of the proposed methodology in denoising the medical CT images corrupted by Gaussian noise. This approach can be utilized in multiple pragmatic areas of application in the field of medical image processing.

Analysis of the Role of Design-Driven Innovation in the Interaction Design of Image Indexing Software under the Background of the Internet of Things

More and more medical images are being produced quickly by modern imaging devices that are connected to the Internet of Things (IoT). For the retrieval of necessary images from a conventional big volume database, the continuous scanning of all image data in an IoT network appears to be ineffective and computationally expensive. Effective image retrieval from a big image database necessitates the use of an effective and scalable image indexing approach. Existing image indexing methods have significant drawbacks, including lower efficiency, constrained scalability, increased computing demands, and longer processing times. To index the medical images obtained by IoT sensors, we suggested a novel, effective Content-Based Cascaded Gabor Wavelet Algorithm (CBCGWA) in this study. Further in this study, the medical images from computed tomography (CT) are employed for medical indexing Totally, the dataset included 168 individually annotated square patches in a subset of 115 high-resolution CT (High-Resolution CT) slices used in this study. An adaptive median filter is used for preprocessing the CT medical images once they are acquired from IoT sensor nodes connected to medical imaging systems. The Gaussian Adaptive Attention Network is then used to cluster together images with comparable attributes (GAAN). The suggested method is used to index the images after image clustering. The cloud database is where the indexed images are eventually maintained. The comparison of the recommended indexing approach to the existing indexing strategies revealed that it is better in terms of processing time, power, and indexing efficiency.

Manifesting viscosity changes in lipid droplets during iodined CT contrast media treatment by the real-time and in situ fluorescence imaging

Computed tomography (CT) is one of the most commonly used non-invasive clinical imaging modalities to predict, diagnose and treat the disease. Iodinated contrast media (ICM) is a form of intravenous radiocontrast agent containing iodine, which enhances the visibility of hollow tissue structures in medical CT imaging. ICM may cause allergic reactions, contrast-induced nephropathy, hyperthyroidism and possibly metformin accumulation. It is significant to find out the risk factors, pathogenesis, diagnosis, prevention, and treatment of adverse reactions caused by ICM. Revealing the change roles of the lipid droplets (LDs) viscosity in pathophysiological processes such as cancer and iodined contrast media induced adverse reaction is not only important for monitoring the occurrence and development of some pathophysiological processes but also vital for the deep insight of the biological effects of LDs in these pathophysiological processes. A lipid droplets targeted fluorescent probe DN-1 was devised to sense cellular viscosity alteration with high selectivity and sensitivity, which was applied to distinguish cancer cells and normal cells and reveal viscosity changes during iodined CT contrast media treatment.