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.

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.

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 (ρ_glass), molar polarizability (α_m), molar refraction (Rm), refractive index (n), and third-order nonlinear optical susceptibility (χ³) and the lowest optical energy gap (E_opt) 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.

Medical imaging techniques are the most popular non-invasive methods to diagnose chest diseases. Chest X-ray scans are employed commonly to detect chronic obstructive pulmonary diseases and other respiratory diseases. Despite the significance of these diagnostic methods, the process of disease detection and the subsequent task of CXR report writing is tedious for radiologists. Therefore, Automated radiological report generation is a highly desirable task for radiologists. Previous studies were focused on the automated generation of medical reports to achieve greater quantitative scores rather than focusing on the quality of reports. Such approaches suffer from the problem of generating normal reports for CXR with diseases. Additionally, the absence of clear segregation between normal and abnormal samples in publicly available datasets its impossible to evaluate the performance of models in generating rare abnormal reports. To address these issues, we propose CNX-B2 which is a Convolutional Neural Network (CNN) combined with a Transformer approach to generate medical reports. The proposed encoder is designed to be both hybrid and efficient, capturing meaningful spatial features through inherent convolution biases. This enables the transformer-based decoder to robustly convert these features into coherent medical reports. Secondly, we also introduce a new radiological report dataset to evaluate model performances on abnormal reports separately. Our proposed model is further evaluated on the IU-Xray dataset, achieving competitive scores of 0.479 BLEU-1, 0.188 METEOR, and 0.586 CIDER.

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%.

To assess observer reliability and diagnostic accuracy in children, of a semi-automated six-point technique developed for vertebral fracture (VF) diagnosis in adults, which records percentage loss of vertebral body height. Using a semi-automated software program, five observers independently assessed T4 to L4 from the lateral spine radiographs of 137 children and adolescents for VF. A previous consensus read by three paediatric radiologists using a simplified algorithm-based qualitative technique (i.e., no software involved) served as the reference standard. Of a total of 1,781 vertebrae, 1,187 (67%) were adequately visualised according to three or more observers. Interobserver agreement in vertebral readability for each vertebral level for five observers ranged from 0.05 to 0.47 (95% CI: -0.19, 0.76). Intra-observer agreement using the intraclass correlation coefficient (ICC) ranged from 0.25 to 0.61. The overall sensitivity and specificity were 18% (95% CI: 14-22) and 97% (95% CI: 97-98), respectively. In contrast to adults, the six-point technique assessing anterior, middle, and posterior vertebral height ratios is neither satisfactorily reliable nor sensitive for VF diagnosis in children. Training of the software on paediatric images is required in order to develop a paediatric standard that incorporates not only specific vertebral body height ratios but also the age-related physiological changes in vertebral shape that occur throughout childhood.