Medical Imaging Testing Research Articles

Computed tomography employing sensing of high energy particle current

Objective. We demonstrate High Energy Current Computed Tomography (HEC-CT) employing megavoltage linac x-rays.Approach. Using deterministic radiation transport we simulate two-parameter HEC-CT projections and using inverse Fourier transform we reconstruct two distinct material parameters for water phantoms with ICRP tissue inserts and materials of different atomic number Z. The HEC-CT projections are obtained by beam scanning and rotating the object.Main Results. The first HEC-CT material parameter is alike the standard attenuation coefficient with dependence on atomic number and material density similar to the Hounsfield Units. The second material parameter has opposite trends and does not find any analogy in the standard CT framework.Significance. New CT method has been invented for medical imaging or non-destructive testing. The key feature of the technique is a two-value CT reconstruction based on particle current instead of transmitted dose.

Iatrogenic tracheobronchial rupture in a fragile patient: A case report

An acute injury of the tracheobronchial system is a rare but potentially life-threatening condition that can be caused by medical procedures. Diagnosis requires a thorough evaluation of the patient’s medical history, physical examination, and imaging tests. Identification of a tracheal rupture in patients under sedation or general anesthesia may be difficult, and a strong clinical suspicion is essential. Treatment can be conservative or surgical, depending on the severity of the injury and the patients’ clinical status. The key prognostic determinant is a prompt identification and appropriate management.

Ultrasound Imaging in Diagnosis and Management of Lower Limb Injuries: A Comprehensive Review.

Medical imaging tests are widely used to diagnose a broad spectrum of lower-limb injuries. Among these modalities, ultrasound (US) imaging has gained significant traction as a valuable diagnostic instrument for assessing conditions primarily affecting muscles, tendons, ligaments, and other soft tissues. However, there are important dilemmas related to the indications and possibilities of US in lower-limb injuries. Conflicting findings and approaches raise questions regarding the validity, accuracy, and usefulness of the US in that area. This narrative review attempts to summarize the current state of knowledge regarding US imaging of lower-limb injuries. The study provides a detailed discussion of the existing literature and contemporary insights on the diagnosis of lower-limb injuries using US examination, and draws attention to the role of the US in interventional procedures and monitoring of the healing process. The characteristics of normal muscles, tendons, and ligaments in US imaging are presented, along with the most commonly documented conditions affecting these tissues. Furthermore, the benefits and justifications for employing US in interventional procedures are discussed, ranging from platelet-rich plasma injections to physiotherapeutic treatments like percutaneous electrolysis. The study was further augmented with US pictures depicting various lower-limb injuries, mainly affecting young athletes. This article aims to review the role of US imaging in the diagnosis and management of common lower-limb injuries.

Intervertebral Cervical Disc Intensity (IVCDI) Detection and Classification on MRI Scans Using Deep Learning Methods

ABSTRACTRadiologists manually interpret magnetic resonance imaging (MRI) scans for the detection of intervertebral cervical disc degeneration, which are often obtained in a primary care or emergency hospital context. The ability of computer models to work with pathological findings and aid in the first interpretation of medical imaging tests is widely acknowledged. Deep learning methods, which are commonly employed today in the diagnosis or detection of many diseases, show great promise in this area. For the detection and segmentation of intervertebral cervical disc intensity, we propose a Mask‐RCNN‐based deep learning algorithm in this study. The provided approach begins by creating an original dataset using MRI scans that were collected from Yozgat Bozok University. The senior radiologist labels the data, and three classes of intensity are chosen for the classification (low, intermediate, and high). Two alternative network backbones are used in the study, and as a consequence of the training for the Mask R‐CNN algorithm, 98.14% and 96.72% mean average precision (mAP) values are obtained with the ResNet50 and ResNet101 architectures, respectively. Utilizing the five‐fold cross‐validation approach, the study is conducted. This study also applied the Faster R‐CNN method, achieving a mAP value of 85.2%. According to the author's knowledge, no study has yet been conducted to apply deep learning algorithms to detect intervertebral cervical disc intensity in a patient population with cervical intervertebral disc degeneration. By ensuring accurate MRI image interpretation and effectively supplying supplementary diagnostic information to provide accuracy and consistency in radiological diagnosis, the proposed method is proving to be a highly useful tool for radiologists.

The Morbid Impact of Environmental Toxins on the Human Nervous System: Peripheral Neuropathy Nexus with Organic Solvents, Pesticides, and Heavy Metals

Peripheral neuropathy is a complex disorder characterized by damage to the peripheral nerves, resulting in various sensory, motor, and autonomic symptoms. This review offers a detailed examination of peripheral neuropathy, covering its prevalence, effects on individuals and society, causes, diagnosis, mechanisms, treatment, and management, focusing on its association with environmental toxins. The etiology of peripheral neuropathy is multifactorial, encompassing diverse causes such as diabetes mellitus, autoimmune diseases, infections, vitamin deficiencies, toxic exposures, and genetic factors. Notably, environmental toxins, including organic solvents, pesticides, and heavy metals, have been implicated in the pathogenesis of peripheral neuropathy. Environmental toxins exert their neurotoxic effects through various mechanisms, including disruption of neuronal membrane integrity, interference with neurotransmission, induction of oxidative stress, and promotion of inflammatory responses. Accurate diagnosis of peripheral neuropathy involves a comprehensive medical history, neurological examination, electrophysiological studies, imaging tests, and laboratory investigations to identify underlying causes, including environmental toxin exposure. Treatment strategies for environmental toxin-induced peripheral neuropathy focus on eliminating exposure, managing symptoms, and preventing further nerve damage. Pharmacological interventions, adjunctive therapies, nutritional support, regular monitoring, and patient education are integral to management. A multidisciplinary approach is fundamental for diagnosing and managing peripheral neuropathy effectively, emphasizing identifying and mitigating environmental toxin exposure to alleviate symptoms and improve quality of life.

Iterative enhancement fusion-based cascaded model for detection and localization of multiple disease from CXR-Images

The lungs are a vital organ of the human body. Malfunctioning of the lungs caused a direct threat to life. In recent years the world has witnessed massive medical insufficiency to handle the lung diseases caused by numerous agents including COVID-19. According to the recommended course of treatment, medical imaging tests including X-rays and CT scans have been very helpful in identifying multiple chest infections. Automatic detection of chest disease is the need of the modern time as it will speed up patient care and reduce doctors’ workload. An Iterative Enhancement Fusion-based Cascaded (IEFCM) model to identify multiple diseases from chest X-ray images is suggested in the present paper. If a chest infection is discovered in the imaging, the suggested model additionally localizes the precise infected area on the CXR image. Experimental outcome clearly demonstrates that the performance of suggested model is significantly superior to the pre-trained model, that is the Golden standard dataset and data from the local population. In terms of sensitivity and specificity, IEFCM achieved 95.62 % sensitivity, which indicates an accurate diagnosis of lung disease, reducing the risk of missing any instances. Similarly, the specificity is 96.23 %, which denotes, the IEFCM model correctly identified the healthy people. It resulted decrease of misdiagnosis and unnecessary follow-up testings.

Illumination pattern optimization in tomography based on the Kalman estimation filter and optimal experiment design

Tomography is widely used in medical imaging or industrial non-destructive testing applications. One costly and time consuming operation in any form of tomography is the process of data acquisition where a large number of measurements are made and collected data is used for image reconstruction. Data acquisition can slow down tomography to the point that the scanner cannot catch up with the speed of changes in the medium under test. By optimizing the information content of each measurement, we can reduce the number of measurements needed to achieve the target precision. Development of algorithms to optimize the information content of tomography measurements is the main goal of this article. Here, the dynamics of the medium and tomography measurements are formulated in the form of a Kalman estimation filter. A mathematical algorithm is developed to compute the optimal measurement matrix which minimizes the uncertainty left in the estimation of the distribution the tomography scanner is reconstructing. Results, as presented in the paper, show noticeable improvement is the quality of generated images when the medium is scanned by optimal measurements instead of traditional raster or random scanning protocols.

ALZHEIMER’S DISEASE DETECTION USING DEEP LEARNING

Image processing is a technique for applying various procedures to a picture in order to improve it or extract some relevant information from it. It is a kind of signal processing where the input is an image and the output may either be another picture or features or characteristics related to that image. Image processing is one of the technologies that is currently expanding quickly. It is a primary subject of research in both the engineering and computer science fields. For dementia, Alzheimer's disease (AD) is the most prevalent kind. It often shows itself as a steadily declining memory and cognitive function, which makes it difficult for the affected person to live independently and has a significant negative influence on both the affected personand society. At the moment, AD diagnosis depends on medical history analysis, blood testing, behavior analysis, brain imaging, and cognitive testing. But because these processes are arbitrary and uneven, it is difficult to provide a precise forecast for the early phases of AD challenging. A dynamic dual-graph fusion convolutional network is suggested in this paper to enhance the accuracy of Alzheimer's disease (AD) diagnosis. The key contributions of the paper are as follows: The proposed architecture can dynamically adjust the graph structure for GCN to produce better diagnosis outcomes by learning the optimal underlying latent graph, propose a novel dynamic GCN architecture, which is an end-to-end pipeline for diagnosing AD, incorporate feature graph learning and dynamic graph learning, giving those useful features of subjects more weight while reducing the weights of other noise features. Experiments show that our approach achieves great classification results in AD diagnosis while offering flexibility and stability. This project is implemented for the Alzheimer disease classification using Graph convolutional network architecture like CNN with LSTM layer and fit the model in the training and testing and deploy the model for getting the test image output and then compare the accuracy of the model for getting the most perfect model for medical image classification and get theaccuracy of CNN- LSTM layer with 98%.

Enhanced high-frequency piezoelectric ultrasonic transducer based on integrated AlN honeycomb structure

Ultrasonic transducers are vital components for transmitting and receiving sound waves, which are customized to specific applications based on their operating frequencies.Small-sized high-frequency ultrasonic transducers have gained considerable attention and adoption in medical ultrasound imaging and small pipe flow testing. However, the significant impact of attenuation on high-frequency ultrasound restricts detection distance and degrades the signal-to-noise ratio. Additionally, thin-film high-frequency ultrasound transducers exhibit low output sound pressure, due to their low resonance displacement. In this study, we present an innovative approach to address these challenges through the design of an integrated small-size honeycomb-type top, two-electrode aluminum nitride (AIN)-based high-frequency ultrasonic transducer. A comprehensive investigation of the relationship between transducer size, sensitivity, and transmitted sound pressure levels is conducted. Leveraging microelectromechanical systems (MEMS) micro-nano-machining technology, we fabricate a 14 × 13 array with a unit diameter of 50 µm, and a matching circuit is employed to enhance the transducer's amplitude. Experimental results demonstrate remarkable transmit sound pressure levels (SPL) exceeding 210 dB (re:1 µPa/V) at a distance of 2 cm, with a maximum transmit SPL of 233 dB, and the receive sensitivity surpasses − 175 dB (re:1 V/µPa) and, at the 5 MHz resonance, the sensitivity reaches − 143 dB (re:1 V/µPa). Moreover, the transmit and receive linearity values demonstrate 99% and 98%, respectively. Our findings highlight the significant potential of the proposed high-frequency ultrasonic transducer for a diverse range of applications.

Deep Convolutional Curvelet Transform- Based MRI Approach for Early Detection of ALZHEIMER' S Disease

Image processing is a technique for applying various procedures to a picture in order to improve it or extract some relevant information from it. It is a kind of signal processing where the input is an image and the output may either be another picture or features or characteristics related to that image. Image processing is one of the technologies that is currently expanding quickly. It is a primary subject of research in both the engineering and computer science fields. For dementia, Alzheimer's disease (AD) is the most prevalent kind. It often shows itself as a steadily declining memory and cognitive function, which makes it difficult for the affected person to live independently and has a significant negative influence on both the affected person and society. At the moment, AD diagnosis depends on medical history analysis, blood testing, behavior analysis, brain imaging, and cognitive testing. But because these processes are arbitrary and uneven, it is difficult to provide a precise forecast for the early phases of AD challenging. A dynamic dual-graph fusion convolutional network is suggested in this paper to enhance the accuracy of Alzheimer's disease (AD) diagnosis. The key contributions of the paper are as follows: The proposed architecture can dynamically adjust the graph structure for GCN to produce better diagnosis outcomes by learning the optimal underlying latent graph, propose a novel dynamic GCN architecture, which is an end-to-end pipeline for diagnosing AD, incorporate feature graph learning and dynamic graph learning, giving those useful features of subjects more weight while reducing the weights of other noise features. Experiments show that our approach achieves great classification results in AD diagnosis while offering flexibility and stability. This project is implemented for the Alzheimer disease classification using Graph convolutional network architecture like CNN with LSTM layer and fit the model in the training and testing and deploy the model for getting the test image output and then compare the accuracy of the model for getting the most perfect model for medical image classification and get the accuracy of CNN- LSTM layer with 98%.

High-sensitivity fiber-tip acoustic sensor with ultrathin gold diaphragm

Miniature acoustic sensors with high sensitivity are highly desired for applications in medical photoacoustic imaging, acoustic communications and industrial nondestructive testing. However, conventional acoustic sensors based on piezoelectric, piezoresistive and capacitive detectors usually require a large element size on a millimeter to centimeter scale to achieve a high sensitivity, greatly limiting their spatial resolution and the application in space-confined sensing scenarios. Herein, by using single-crystal two-dimensional gold flakes (2DGFs) as the sensing diaphragm of an extrinsic Fabry-Perot interferometer on a fiber tip, we demonstrate a miniature optical acoustic sensor with high sensitivity. Benefiting from the ultrathin thickness (∼8 nm) and high reflectivity of the 2DGF, the fiber-tip acoustic sensor gives an acoustic pressure sensitivity of ∼300 mV/Pa in the frequency range from 100 Hz to 20 kHz. The noise-equivalent pressure of the fiber-tip acoustic sensor at the frequency of 13 kHz is as low as 62.8 µPa/Hz1/2, which is one or two orders of magnitude lower than that of reported optical acoustic sensors with the same size.

Breast Cancer Detection System

Breast cancer "is a major global health concern is breast cancer, for which early detection is essential to effective treatment and higher survival rates. In this work, we suggest a unique method for detecting breast cancer by using cutting-edge machine learning techniques on data from medical imaging tests, especially mammograms. Our approach combines feature extraction and deep learning methods to improve detection efficiency and accuracy. We performed tests on a sizable sample of mammography pictures, and the overall performance, sensitivity, and specificity showed promise. The suggested solution has the potential to improve patient outcomes and healthcare management by supporting radiologists in the early diagnosis of breast cancer..

Local Diagnostic Reference Levels for Adult Computed Tomography Urography Exams.

A Computed Tomography Urography (CTU) scan is a medical imaging test that examines the urinary tract, including the bladder, kidneys, and ureters. It helps diagnose various urinary tract diseases with precision. However, patients undergoing CTU imaging receive a relatively high dose of radiation, which can be a concern. In our research paper, we analyzed the Computed Tomography Dose Index (CTDIvol) and Dose-Length Product (DLP) for 203 adult patients who underwent CTU at one of the most important regional centers in Bosnia and Herzegovina that sees a large number of patients. Our study included the distribution of age and sex, the number of phases within one examination, and different clinical indications. We compared our findings with the results available in the scientific literature, particularly the recently published results from 20 European countries. Furthermore, we established the local diagnostic reference levels (LDRLs) that can help set the national diagnostic reference levels (NDRLs). We believe our research is a significant step towards optimizing the protocols used in different hospitals in our country.

Characterizing the burden of biliary tract cancers across 28 hospitals in Ontario, Canada.

The incidence of biliary tract cancers (BTC) appears to be increasing worldwide. We analyzed the characteristics of BTC-related hospitalizations under medical services across 28 hospitals in Ontario, Canada. This study uses data collected by GEMINI, a hospital research data network. BTC-related hospitalizations from 2015 to 2021 under the Department of Medicine or intensive care unit were captured using the International Classification of Diseases, 10th revision, codes for intrahepatic cholangiocarcinoma (iCCA), extrahepatic cholangiocarcinoma, and gallbladder cancers. A total of 4596 BTC-related hospitalizations (2720 iCCA, 1269 extrahepatic cholangiocarcinoma, 607 gallbladder cancers) were analyzed. The number of unique patients with BTC-related hospitalizations increased over time. For iCCA-related hospitalizations, the total number of hospitalizations increased (from 385 in 2016 to 420 in 2021, p=.005), the hospital length of stay decreased over the study period (mean 10days [SD, 12] in 2016 to 9days [SD, 8] in 2021, p=.04), and the number of in-hospital deaths was stable (from 68 [18%] in 2016 to 55 [13%] in 2021, p=.62). Other outcomes such as 30-day readmissions, medical imaging tests, intensive care unit-specific hospitalizations, and length of stay were stable over time for all cohorts. The cost of hospitalization for the BTC cohort increased from median $8203 CAD (interquartile range, 5063-15,543) in 2017 to $8507 CAD (interquartile range, 5345-14,755) in 2021. This real-world data analysis showed a rising number of patients with BTC-related hospitalizations and rising number of iCCA-related hospitalizations across 28 hospitals in Ontario between 2015 and 2021.

A Hybrid of Fuzzy C-Means for the segmentation in CT scan and X-ray images for screening the COVID-19 patients

In this paper, using CT scan and X-ray images, we present a hybrid approach, based on combining fuzzy C-means with k-means clustering, to evaluate and determine pneumonia infection caused by the coronavirus disease (COVID-19). To achieve this objective, we introduce a hybrid method that combines fuzzy C-means clustering with K-means clustering. This hybrid approach is designed to effectively segment object boundaries within medical images, enabling the precise identification of pneumonia-related features. In addition to our hybrid method, we compare its performance with two other segmentation approaches: the Expectation Maximization (EM) algorithm and 2D Entropy segmentation. Which, the method we propose uses a comparison between the performances of the based on a database of medical imaging test. Experimental results showed that the proposed approach outperforms, it was found that the hybrid fuzzy C-means algorithm segmentation images methods give better performance in terms of accuracy, precision, and F-measure, which is effective in boundaries segmentation. Comparative results of the accuracy and image quality index demonstrate the robustness of AI. It also helps to improve work efficiency with accurate analysis of COVID-19 infection on CT scan and X-rays. In addition, the approach helps radiologists make clinical decisions for diagnosis, follow-up, and prognosis.

Klasifikasi Citra Histologi Kanker Payudara Menggunakan Metode Ensemble CNN

Breast cancer is a serious disease and the leading cause of death in women. Breast cancer can be diagnosed through medical imaging tests, such as radiological and hispathological images. However, because histological images have complexity and diversity, the manual examination has disadvantages, namely, requiring high expertise, time consuming, and prone to errors. Deep learning has been widely applied to histological classification including breast cancer, with automation to help overcome the shortcomings of manual diagnostic methods. In this study, Deep learning was developed using CNN with the ensemble method. The MobileNet, MobileNetV2 and VGG16 models were trained on the dataset and averaged the results of the two models. The experimental results show an increase in the ensemble compared to each model with an balanced accuracy 0.8689 dan F1-score 0.8682 in VGG16 + MobileNet combination, these results provide an increase compared to previous studies.

Cumulative radiation dose from medical imaging in paediatric congenital heart disease patients with epicardial cardiac implantable electronic devices.

To determine whether paediatric congenital heart disease (CHD) patients with epicardial cardiac implantable electronic devices (CIEDs) receive high cumulative effective doses (CEDs) of ionizing radiation from medical imaging tests. We compared 28 paediatric CHD patients with epicardial CIEDs (cases) against 40 patients with no CIED matched by age at operation, sex, surgical era, and CHD diagnosis (controls). We performed a retrospective review of radiation exposure from medical imaging exams between 2006 and 2022. Radiation dose from computed tomography (CT) and X-ray radiography was calculated using the National Cancer Institute Radiation Dosimetry Tool. We performed univariate analysis to compare the CED between the two groups. In the case subgroup, we convened experts' review to adjudicate the prevalence of CT exams that should have been performed with magnetic resonance imaging (MRI) in the absence of a CIED. Children (median age 2.5 years at implant) with CIEDs received significantly higher median CED compared with matched controls (6.90 vs. 1.72 mSv, P = 0.0018). In cases, expert adjudication showed that 80% of the CT exams would have been performed with MRI in the absence of a CIED. This resulted, on average, a five-fold increase in the effective dose (ED) from post-lead implant CTs. Paediatric CHD patients with CIED received four times higher CED than matched controls. Improved access to medical imaging tests without ionizing radiation, such as MRI, could potentially reduce the ED in CIED patients by up to five times.

Diagnóstico precoce de histiocitose das células de Langerhans: relato de caso

Langerhans' cells histiocytosis is a disease that belongs to the group of histiocytic disorders and is considered a rare pathology. It results from a proliferation and accumulation of cells of the phagocytic mononuclear system, including Langerhans' cells. This paper reports a case of a 4-year-old patient who presented a growing, swollen, nodular, depressible and mobile lesion in the right retroauricular region, without pain or phlogistic signs for 6 months, besides erosive and necrotic lesions in the first and second right molars. The confirmatory diagnosis was made by biopsy and immunohistochemistry. This is a study of the medical record, histological evaluations, laboratory and imaging tests of the patient, with the aim of discussing the pathology, making the diagnosis and treatment earlier.

Review of Radiation Dose Metric Tracking for Patients: Ethical Implications of the “Do Not Disclose” Standard

Medical diagnostic imaging tests that produce ionizing radiation now deploy technology that captures an individual patient’s cumulative radiation dose. This raises the question of whether there is an imperative for regional health authorities to disclose this information to physicians who may then engage their patients in decisions about whether the potential harms are worth the benefits of subsequent diagnostic imaging. Currently, the advice of the professional bodies providing standards of practice for medical diagnostic imaging is to withhold this information from physicians. Their concern is that cumulative dose information is difficult to evaluate in terms of risk to individual patients; it is not easily applicable to clinical decision making about the appropriateness of a subsequent imaging exam; and referring clinicians will feel compelled to offer a patient a less efficacious non-ionizing test, which could negatively affect patient care. We present a critical analysis of several assumptions underlying the stance of non-disclosure. Working at the intersection of medical physics, medical anthropology, and clinical ethics, we offer an alternative framing of the discourse of risk that has shaped the recent scholarly debate on disclosure of individual cumulative radiation dose. We posit that a persuasive argument can be made against the stance of the professional bodies and for a policy of disclosure – provided that such a policy prioritizes patient-centred shared decision making, radiologists as risk-interpretation experts, and the authority of the prescribing physician.

Non-compliance with medical recommendations results in delayed hospitalization and poorer prognosis in patients with cerebral ischemic stroke in Poland: Non-compliance effects on post-ischemic stroke prognosis

ObjectivesThis study aimed to reveal and analyze the causes of delays in reaching the hospital of patients with cerebral ischemic stroke and to assess their clinical picture. Material and methodsThe study group included 161 patients with stroke, who reported to the hospital beyond the thrombolytic treatment therapeutic window. The control group consisted of 85 patients recruited consecutively with stroke who received thrombolytic treatment per eligibility criteria. Laboratory and medical imaging tests essential for neurological condition assessment were conducted in the study group. Control group research was based on retrospective analysis of medical records. ResultsThe rate of deaths during hospitalization was lower in the control group (4.7%) compared to the study group (14.9%). In the study group, more patients (16.8%) admitted to non-compliance with medical recommendations than in the control group (5.9%). There were no statistically significant differences in nicotinism and alcohol dependence syndrome frequency between both groups. ConclusionsBased on each group inclusion criteria, a lower mortality rate in the control group indicates a crucial role of the therapeutic window in cerebral stroke treatment. Analysis of reasons for delay points out that efficient prophylaxis is the education of patients with stroke risk factors and their families.