Current Medical Imaging - Volume 19, Issue 2, 2023

Alveolar echinococcosis is a parasitic infection caused by Echinococcus multilocularis in the metacestode stage. The disease involves the liver in almost all cases, and additionally, lung involvement is accompanied in approximately 13% of the cases. Due to the low incidence of pulmonary alveolar echinococci, there are few case reports of lung involvement in the literature. In this study, computed tomography (CT) findings related to pulmonary involvement in alveolar echinococcosis cases, which have increased worldwide in recent years, were evaluated.

Rapid test developments and increasing technological knowledge have changed perspectives on ultrasonography. Previously, ultrasonography was used to evaluate chest wall pathologies, to distinguish between pleural effusion and consolidation, to evacuate pleural effusion, or to evaluate diaphragm movement. Today, it is also used in a wide range of pleural and parenchymal diseases. Ultrasonography is not just used in the emergency department and the intensive care units, it is also utilized in many clinical branches dealing with the respiratory system-due to its ease of use, fast access, price advantage, non-radiation exposure, higher diagnostic sensitivity, and specificity in many clinical situations-and has become a part of the examination. In this review, we have assessed not just transthoracic ultrasonography but also more focal and targeted sonographic applications, such as the endobronchial ultrasound and esophageal ultrasound.

Background: The importance of identifying the structural and functional abnormalities in the brain in the early prediction and diagnosis of schizophrenia has attracted the attention of neuroimaging scientists and clinicians. Objective: The purpose of this study is to structure a review paper that recognizes specific biomarkers of the schizophrenic brain. Methods: Neuroimaging can be used to characterize brain structure, function, and chemistry by different non-invasive techniques such as computed tomography, magnetic resonance imaging, magnetic resonance spectroscopy, and positron emission tomography. The abnormalities in the brain can be used to discriminate psychic disorder like schizophrenia from others. To find disease-related brain alterations in neuroimaging, structural neuroimaging studies provide the most consistent evidence in most of the studies. The review discusses the major issues and findings in structural neuroimaging studies of schizophrenia. In particular, the data is collected from different papers that concentrated on the brain affected regions of different subjects and made a conclusion out of it. Results: In this work, a detailed survey has been done to find structural abnormalities in the brain from different neuroimaging techniques. Several image processing methods are used to acquire brain images. Different Machine learning techniques, Optimization methods, and Pattern recognition methods are used to predict the disease with specific biomarkers, and their results are emphasized. Thus, in this work, deep learning is also highlighted, which shows a promising role in obtaining neuroimaging data to characterize disease-related alterations in brain structure.

Background: Alzheimer’s disease (AD) is associated with Dementia, and it is also a memory syndrome in the brain. It affects the brain tissues and causes major changes in day-to-day activities. Aging is a major cause of Alzheimer’s disease. AD is characterized by two pathological hallmarks, Amyloid β protein and neurofibrillary tangles of hyperphosphorylated tau protein. The imaging hallmarks for Alzheimer’s disease are swelling, shrinkage of brain tissues due to cell loss, and atrophy in the brain due to protein dissemination. Based on the survey, 60% to 80% of dementia patients belong to Alzheimer’s disease. Introduction: AD is now becoming an important brain disease. The goal of AD pathology is to cause changes/damage in brain tissues. Alzheimer’s disease is thought to begin 20 years or more before symptoms appear, with tiny changes in the brain that are undetectable to the person affected. The changes in a person’s brain after a few years are noticeable through symptoms such as language difficulties and memory loss. Neurons in different parts of the brain have detected symptoms such as cognitive impairments and learning disabilities. In this case, neuroimaging tools are necessary to identify the development of pathology which relates to the clinical symptoms. Methods: Several approaches have been tried during the last two decades for brain screening to analyse AD using pre-processing, segmentation, and classification. Different individuals, such as Grey Wolf optimization, Lion Optimization, Ant Lion Optimization, etc., have been attempted in the proposed study. Similarly, hybrid optimization techniques are also attempted to segment the brain sub-regions, which helps in identifying the biomarkers to analyse AD. Conclusion: This study discusses a review of neuroimaging technologies for diagnosing Alzheimer’s disease, as well as the discovery of hallmarks for the disease and the methodologies for finding hallmarks from brain images to evaluate AD. According to the literature review, most of the techniques predicted higher accuracy (more than 90%), which is beneficial for assessing and screening neurodegenerative disease, particularly Alzheimer’s disease.

Background: To evaluate the perfusion status of patients with acute stroke, different imaging tools are used depending on the condition. CT-CT Angiography and MRI are indispensable imaging tools to diagnose and manage stroke patients. Susceptibility-weighted imaging (SWI) also has been used lately to evaluate vascular structures and consequences of stroke in the brain. We aimed to compare CE-MRI, SWI, and CTA with DSC-MRP in terms of perfusion. Methods: Stroke cases of CE-MRI, SWI, CTA and DSC-MRP of 44 patients were included. Collateralization was assessed on CTA; leptomeningeal-pial collateralization (LPC) and parenchymal enhancement (PE) on CE-MRI; prominent vessel sign (PVS) and hemorrhagic transformation on SWI. Results were compared with MRP maps and the ratio of penumbra/infarct core. Results: LPC was correlated with increased CBV (p<0,001), decreased CBF (p=0,026), and prolonged MTT and TTP (p=0,001 and p=0,003). LPC was observed more often in cases with infarct zones with penumbra compared to those without penumbra (p=0,024). PE was positively correlated with prolonged MTT and TTP (p=0,015 and p=0,031). Moreover, there was a positive relationship between PE and increased penumbra ratio over the infarct core (p=0,037). Ipsilateral PVS was associated with increased CBV (p=0,004) and decreased CBF (p=0,002). No relationship was found between collateralization grading on CTA and perfusion metrics or penumbra ratio. Conclusion: In conclusion; ipsilateral PVS can be a measure of CBV and CBF. LPC on CE-MRI can be a sign of an increase in CBV. PE can show larger penumbra. CE-MRI with SWI can be used to evaluate perfusion status.

Objectives: The purpose of this study was to investigate the surgical efficacy and risk factors of cervical spondylotic myelopathy (CSM) patients with increased signal intensity (ISI) on T2-weighted magnetic resonance imaging (MRI-T2WI). Methods: We compared the surgical outcomes of CSM patients with and without ISI. In addition, we compared the efficacy of anterior and posterior cervical decompression in CSM patients with ISI. We also analyzed the risk factors of MRI-T2WI ISI in CSM patients. Results: The incidence of ISI among 153 CSM patients was 71.89 %. The JOA score and JOA remission rate were better in the ISI-free than in the ISI group. The postoperative JOA score and JOA remission rate were better in the posterior than the anterior approach surgery group. The disease duration and vertebral canal volume were found to be risk factors for ISI in CSM patients. Conclusion: Among patients with CSM, the prognosis is worse for those with ISI than those without ISI. Posterior cervical decompression surgery produces a better curative effect than anterior cervical decompression surgery in CSM patients with ISI. CSM patients with longer disease duration and small vertebral canal volume should undergo surgical treatment as early as possible.

Background: Ultrasound is one of the preferred choices for early screening of dense breast cancer. Clinically, doctors have to manually write the screening report, which is time-consuming and laborious, and it is easy to miss and miswrite. Aim: We proposed a new pipeline to automatically generate AI breast ultrasound screening reports based on ultrasound images, aiming to assist doctors in improving the efficiency of clinical screening and reducing repetitive report writing. Methods: AI efficiently generated personalized breast ultrasound screening preliminary reports, especially for benign and normal cases, which account for the majority. Doctors then make simple adjustments or corrections based on the preliminary AI report to generate the final report quickly. The approach has been trained and tested using a database of 4809 breast tumor instances. Results: Experimental results indicate that this pipeline improves doctors' work efficiency by up to 90%, greatly reducing repetitive work. Conclusion: Personalized report generation is more widely recognized by doctors in clinical practice than non-intelligent reports based on fixed templates or options to fill in the blanks.

Background: Universally, the most predominant cause of female mortality is mainly due to breast cancer. Owing to numerous constraints in the existing imaging technique, researchers are trying out an alternative tool to detect the tumor before going to the miserable stage. Methods: This article presents a novel method to detect the mean value system for detecting the location of the tumor in different depths by shifting the antenna anywhere in the breast tissue. In addition, an algorithm to reconstruct the breast image, namely Delay-Multiply-and-Sum (DMAS) is followed to identify the tumor implanted in the breast tissue. Results: The analysis shows that the maximum mean value occurs while the antenna moves very near to the tumor while the mean value reduces while the antenna shifts apart from the tumor location. The mean value in different locations is converted into a microwave image. The high intensity in the image exhibits the precise position of the tumor. This technique can identify the location of early-stage tumor of size 3mm. Multiple tumors of sizes 6mm and 7mm can identify at a depth of 12mm and 18mm in the homogeneous breast phantom. DMAS can provide better imaging results in the early stage tumor of size 3mm embedded in the breast phantom. Conclusion: Microwave imaging is an efficient technique to differentiate healthy and malignant tissue in the breast. Antenna plays a major role in identifying tumors in the breast in the early stage. Hence a high-performance Ultra Wideband Dielectric Resonator Antenna (DRA-UWB) is used to identify the tumor in the breast. An antenna is sketched in different locations of the breast phantom. On account of the hemispherical structure, the mean value of the reflected signal is high at the center than at the edge. Hence, the difference in mean value is calculated with and without breast phantom for identifying the tumor location. The overall efficiency of this technique can be improved by using a high-performance UWB antenna. The image of the breast is reformed by the DMAS beamforming algorithm.

Background: Ovarian cancer is a leading cause of death in gynecological malignancies. Being the most common subtype in OEC, ovarian serous cancer also includes two subtypes: low grade serous ovarian cancer (LGSC) and high grade serous ovarian cancer (HGSC) (1). Purpose: The study aims to assess the capability of apparent diffusion coefficient (ADC) histogram analysis and conventional measurements on magnetic resonance imaging (MRI) in differentiating between LGSC and HGSC. Methods: We retrospectively recruited 38 patients with pathologically proven ovarian serous epithelial cancer. The mean ADC value was measured by one technician using two methods on post-processed workstation. The ADC value and histogram parameter difference between LGSC and HGSC groups were compared. The correlation between the ADC value and the Ki-67 expression was calculated across both groups. Results: The repeatability of ADC measurements across two methods was good; the ROI method (ADC-roi) had better performance repeatability than the area method (ADC-area). The value of ADC-mean , ADC-min, ADC-max, and ADC-area significantly differed between both groups (p < 0.001). The value of ADC-area correlated inversely with ki-67 expression in the whole group (Pearson coefficient = -0.382, p = 0.02). The 3D computerized-diagnostic model had the best discriminative performance in determining HGSC than 2D and conventional ADC measurements. The 3D model yielded a sensitivity of 100%, a specificity of 95.45%, and an accuracy of 97.73%. Conclusion: In the present study, the 3D ADC histogram model help differentiate HGSC from LGSC with a better performance than conventional ADC measurements.

Objectives: Our aim was to assess articles published in the field of radiology, nuclear medicine, and medical imaging in 2020 and analyze the linkage of radiology-related topics with coronavirus disease 2019 (COVID-19) through literature mapping along with a bibliometric analysis for publications. Methods: We performed a search on the Web of Science Core Collection database for articles in the field of radiology, nuclear medicine, and medical imaging published in 2020. We analyzed the included articles using VOS viewer software, where we analyzed the co-occurrence of keywords, representing major topics discussed. Of the resulting topics, a literature map was created and linkage analysis was done. Results: A total of 24,748 articles were published in the field of radiology, nuclear medicine, and medical imaging in 2020. We found a total of 61,267 keywords; only 78 keywords occurred more than 250 times. COVID-19 had 449 occurrences, 29 links, with a total link strength of 271. MRI was the topic most commonly appearing in 2020 radiology publications, while “computed tomography” had the highest linkage strength with COVID-19, with a linkage strength of 149, representing 54.98% of the total COVID-19 linkage strength, followed by “radiotherapy, and “deep and machine learning”. The top cited paper had a total of 1,687 citations. Nine out of the 10 most cited articles discussed COVID-19 and included “COVID-19” or “coronavirus” in their title, including the top cited paper. Conclusion: While MRI was the topic that dominated, CT had the highest linkage strength with COVID-19 and represented the topic of top cited articles in 2020 radiology publications.

Noise in computed tomography (CT) images may occur due to low radiation doses. Hence, the main aim of this paper is to reduce the noise from low-dose CT images so that the risk of high radiation dose can be reduced. Background: The novel coronavirus outbreak has ushered in different new areas of research in medical instrumentation and technology. Medical diagnostics and imaging are one of the ways in which the area and level of infection can be detected. Objective: COVID-19 attacks people with less immunity, so infants, kids, and pregnant women are more vulnerable to the infection. So, they need to undergo CT scanning to find the infection level. But the high radiation diagnostic is also fatal for them, so the intensity of radiation needs to be reduced significantly, which may generate the noise in the CT images. Method: This paper introduces a new denoising technique for low-dose Covid-19 CT images using a convolution neural network (CNN) and noise-based thresholding method. The major concern of the methodology for reducing the risk associated with radiation while diagnosing. Results: The results are evaluated visually and using standard performance metrics. From comparative analysis, it was observed that proposed works give better outcomes. Conclusion: The proposed low-dose COVID-19 CT image denoising model is therefore concluded to have a better potential to be effective in various pragmatic medical image processing applications in noise suppression and clinical edge preservation.

Background: Generalized lymphatic anomaly (GLA) is a rare condition, mainly involving bones, soft tissue, and internal organs. The diagnosis of GLA is often difficult. Case Presentation: We report a case of GLA in a boy who was initially treated for suspected Langerhans cell histiocytosis and discuss the potential imaging features of GLA. The clinical and imaging data of a case of GLA in an 8-year-old boy were analyzed retrospectively, and the literature was reviewed. Results: The case shows the imaging features of GLA with multiskeletal diffuse expansile cystic osteolytic lesions penetrating the cortical surface and extending within the cortex and a pumice-like or rotten wood-like rough appearance on volume-rendered 3D CT images. Soft tissue multi-cystic masses increase diagnostic confidence. Fatty infiltration appears in multiple vertebral bodies and sternum, namely, abnormal T1 and T2 hyperintense and fat-suppressed T2 hypointense on MRI and the corresponding low density similar to that of fat on CT, suggesting that GLA involves the vertebrae and sternum, which may be accompanied by chylothorax. Conclusion: GLA in bone has typical features on CT. MRI reveals its cystic nature, and typical soft tissue lesions and chylothorax increase confidence in the diagnosis.