SPECT/CT and PET/CT, related radiopharmaceuticals, and areas of application and comparison.

The paper begins by identifying the key historical elements in the development of nuclear medicine imaging, focusing on the Anger camera and single photon emission computed tomography (SPECT) technologies. In this context, key reference is made to the physics of detection in Anger camera systems, especially key components such as the sodium iodide crystal, the function and performance of photomultiplier tubes, and the collimator design. It is discovered that within each component of technology, there are fundamental physical relationships that govern the performance of each component, and that overall image quality is the result of the complex interaction of all such elements. The increasing use of SPECT/CT imaging is described and illustrated with a range of typical clinical applications, which include brain, spinal, cardiac, and cancer studies. The use of CT imaging functionality allows for SPECT image correction based on compensation for absorption within tissue. Reference is also made to the basics of positron emission tomography (PET) imaging and, in particular, to the integration of PET/CT systems where the anatomy profile of the CT image is used to provide correction for photon absorption. A summary is provided of the radionuclides and radiopharmaceuticals commonly used in PET/CT imaging and a range of image studies referenced includes those of nasopharyngeal carcinoma, lung cancer investigation, brain investigation (cancer detection and dementia) and cardiac function. Reference is made to the development of "time of flight" (TOF) technology for improving of image resolution in PET/CT systems. Furthermore, SPECT/CT and PET/CT imaging systems are compared, where a key factor identified is the significantly higher number of photons detected with PET/CT technology and improved image resolution.

Effects of COVID-19 on Synaptic and Neuronal Degeneration.

Neurons are the basic building blocks of the human body's neurological system. Atrophy is defined by the disintegration of the connections between cells that enable them to communicate. Peripheral neuropathy and demyelinating disorders, as well as cerebrovascular illnesses and central nervous system (CNS) inflammatory diseases, have all been linked to brain damage, including Parkinson's disease (PD). It turns out that these diseases have a direct impact on brain atrophy. However, it may take some time after the onset of one of these diseases for this atrophy to be clearly diagnosed. With the emergence of the Coronavirus disease 2019 (COVID-19) pandemic, there were several clinical observations of COVID-19 patients. Among those observations is that the virus can cause any of the diseases that can lead to brain atrophy. Here we shed light on the research that tracked the relationship of these diseases to the COVID-19 virus. The importance of this review is that it is the first to link the relationship between the Coronavirus and diseases that cause brain atrophy. It also indicates the indirect role of the virus in dystrophy.

The Effect of ZnO, MgO, TiO2, and Na2O Modifiers on the Physical, Optical, and Radiation Shielding Properties of a TeTaNb Glass System.

Novel glass samples with the composition 75TeO2-5Ta2O5-15Nb2O5-5x (where x = ZnO, MgO, TiO2, or Na2O) in mole percent were prepared. The physical, optical, and gamma radiation shielding properties of the glass samples were studied over a wide energy spectrum ranging between 0.015 and 20 MeV. The glasses' UV-vis spectra were utilized to evaluate the optical energy gap and refractive index. Glass samples had a refractive index ranging from 2.2005 to 2.0967. The results showed that the sample doped with zinc oxide (ZnO) recorded the highest density (ρglass), molar polarizability (αm), molar refraction (Rm), refractive index (n), and third-order nonlinear optical susceptibility (χ3) and the lowest optical energy gap (Eopt) among the samples under investigation. When comparing the current glass system with various standard glass shielding materials, the prepared glass system showed superior shielding performance at energies ranging between 40 and 85 keV. These findings indicate that the prepared glass systems can be used in diagnostic X-rays, especially in dental applications.

Enhancement of Mammographic Images Using Histogram-Based Techniques for Their Classification Using CNN.

In the world, one in eight women will develop breast cancer. Men can also develop it, but less frequently. This condition starts with uncontrolled cell division brought on by a change in the genes that regulate cell division and growth, which leads to the development of a nodule or tumour. These tumours can be either benign, which poses no health risk, or malignant, also known as cancerous, which puts patients' lives in jeopardy and has the potential to spread. The most common way to diagnose this problem is via mammograms. This kind of examination enables the detection of abnormalities in breast tissue, such as masses and microcalcifications, which are thought to be indicators of the presence of disease. This study aims to determine how histogram-based image enhancement methods affect the classification of mammograms into five groups: benign calcifications, benign masses, malignant calcifications, malignant masses, and healthy tissue, as determined by a CAD system of automatic mammography classification using convolutional neural networks. Both Contrast-limited Adaptive Histogram Equalization (CAHE) and Histogram Intensity Windowing (HIW) will be used (CLAHE). By improving the contrast between the image's background, fibrous tissue, dense tissue, and sick tissue, which includes microcalcifications and masses, the mammography histogram is modified using these procedures. In order to help neural networks, learn, the contrast has been increased to make it easier to distinguish between various types of tissue. The proportion of correctly classified images could rise with this technique. Using Deep Convolutional Neural Networks, a model was developed that allows classifying different types of lesions. The model achieved an accuracy of 62%, based on mini-MIAS data. The final goal of the project is the creation of an update algorithm that will be incorporated into the CAD system and will enhance the automatic identification and categorization of microcalcifications and masses. As a result, it would be possible to increase the possibility of early disease identification, which is important because early discovery increases the likelihood of a cure to almost 100%.

The Potential Contribution of Biopolymeric Particles in Lung Tissue Regeneration of COVID-19 Patients.

The lung is a vital organ that houses the alveoli, which is where gas exchange takes place. The COVID-19 illness attacks lung cells directly, creating significant inflammation and resulting in their inability to function. To return to the nature of their job, it may be essential to rejuvenate the afflicted lung cells. This is difficult because lung cells need a long time to rebuild and resume their function. Biopolymeric particles are the most effective means to transfer developing treatments to airway epithelial cells and then regenerate infected lung cells, which is one of the most significant symptoms connected with COVID-19. Delivering biocompatible and degradable natural biological materials, chemotherapeutic drugs, vaccines, proteins, antibodies, nucleic acids, and diagnostic agents are all examples of these molecules' usage. Furthermore, they are created by using several structural components, which allows them to effectively connect with these cells. We highlight their most recent uses in lung tissue regeneration in this review. These particles are classified into three groups: biopolymeric nanoparticles, biopolymeric stem cell materials, and biopolymeric scaffolds. The techniques and processes for regenerating lung tissue will be thoroughly explored.

Diagnostic performance of morphometric vertebral fracture analysis (MXA) in children using a 33-point software program.

BACKGROUND: There is significant inter and intraobserver variability in diagnosing vertebral fractures in children. PURPOSE: We aimed to evaluate the diagnostic accuracy of morphometric vertebral fracture analysis (MXA) using a 33-point software program designed for adults, on dual-energy x-ray absorptiometry (DXA) images of children. MATERIALS AND METHODS: Lateral spine DXA images of 420 children aged between 5 and 18 years were retrospectively reviewed. Vertebral fracture assessment (VFA) by an expert pediatric radiologist using Genant's semiquantitative scoring system served as the gold standard. All 420 DXA scans were analyzed by a trained radiographer, using semi-automated software (33-point morphometry). VFA of a random sample of 100 DXA was performed by an experienced pediatric clinical scientist. MXA of a random sample of 30 DXA images were analyzed by three pediatric radiologists and the pediatric clinical scientist. Diagnostic accuracy and inter and intraobserver agreement (kappa statistics) were calculated. RESULTS: Overall sensitivity, specificity, false positive (FP) and false negative (FN) rates for the radiographer using the MXA software were 80%, 90%, 10%, and 20% respectively and for mild fractures alone were 46%, 92%, 8%, and 54% respectively. Overall sensitivity, specificity, FP, and FN rates for the four additional observers using MXA were 89%, 79%, 21%, and 11% respectively and for mild fractures alone were 36%, 86%, 14%, and 64% respectively. Agreement between two expert observers was fair to good for VFA and MXA [kappa = 0·29 to 0·76 (95% CI: 0·17-0·88) and 0·29 to 0·69 (95% CI: 0·17-0·83)] respectively. CONCLUSION: MXA using a 33-point technique developed for adults is not a reliable method for the identification of mild vertebral fractures in children. A pediatric standard is required which not only incorporates specific vertebral body height ratios but also the age-related physiological changes in vertebral shape that occur throughout childhood.

Are semi-automated software program designed for adults accurate for the identification of vertebral fractures in children?

OBJECTIVES: To assess whether diagnostic accuracy of morphometric vertebral fracture (VF) diagnosis in children can be improved using AVERT™ (a 33-point semi-automated program developed for VF diagnosis in adults) compared with SpineAnalyzer™ (a 6-point program), which has previously been shown to be of insufficient accuracy. MATERIALS AND METHODS: Lateral spine radiographs (XR) and dual-energy X-ray absorptiometry (DXA) scans of 50 children and young people were analysed by two observers using two different programs (AVERT™ and SpineAnalyzer™). Diagnostic accuracy (sensitivity, specificity, false-negative (FN) and false-positive rates (FP)) was calculated by comparing with a previously established consensus arrived at by three experienced paediatric musculoskeletal radiologists, using a simplified algorithm-based qualitative scoring system. Observer agreement was calculated using Cohen's kappa. RESULTS: For XR, overall sensitivity, specificity, FP and FN rates using AVERT™ were 36%, 95%, 5% and 64% respectively and 26%, 98%, 2% and 75% respectively, using SpineAnalyzer™. For DXA, overall sensitivity, specificity, FP and FN rates using AVERT™ were 41%, 91%, 9% and 59% respectively and 31%, 96%, 4% and 69% respectively, using SpineAnalyzer. Reliability (kappa) ranged from 0.34 to 0.37 (95%CI, 0.26-0.46) for AVERT™ and from 0.26 to 0.31 (95%CI, 0.16-0.44) for SpineAnalyzer™. Inter- and intra-observer agreement ranged from 0.41 to 0.47 for AVERT™ and from 0.50 to 0.79 for SpineAnalyzer™. CONCLUSION: AVERT™ has slightly higher accuracy but lower observer reliability for the representation of vertebral morphometry in children when compared with SpineAnalyzer™. However, neither software program is satisfactorily reliable for VF diagnosis in children. KEY POINTS: • SpineAnalyzer™ and AVERT™ have low diagnostic accuracy and observer agreement when compared to three paediatric radiologists' readings for the diagnosis of vertebral fractures (VF) in children. • Neither AVERT™ nor SpineAnalyzer™ is satisfactorily reliable for VF diagnosis in children. • Development of specific paediatric software and normative values (incorporating age-related physiological variation in children) is required.

Diagnosis of osteoporotic vertebral fractures in children.

Osteoporosis is a generalised disorder of the skeleton with reduced bone density and abnormal bone architecture. It increases bone fragility and renders the individual susceptible to fractures. Fractures of the vertebrae are common osteoporotic fractures. Vertebral fractures may result in scoliosis or kyphosis and, because they may be clinically silent, it is imperative that vertebral fractures are diagnosed in children accurately and at an early stage, so the necessary medical care can be implemented. Traditionally, diagnosis of osteoporotic vertebral fractures has been from lateral spine radiographs; however, a small number of studies have shown that dual energy x-ray absorptiometry is comparable to radiographs for identifying vertebral fractures in children, while allowing reduced radiation exposure. The diagnosis of vertebral fractures from dual energy x-ray absorptiometry is termed vertebral fracture assessment. Existing scoring systems for vertebral fracture assessment in adults have been assessed for use in children, but there is no standardisation and observer reliability is variable. This literature review suggests the need for a semiautomated tool that (compared to the subjective and semiquantitative methods available) will allow more reliable and precise detection of vertebral fractures in children.