Epidemiology

2016 ◽  
Author(s):  
Nigel Arden ◽  
Michael C. Nevitt
Keyword(s):  
Magnetic Resonance Imaging ◽  
Risk Factors ◽  
Magnetic Resonance ◽  
Natural History ◽  
Health Service ◽  
Resonance Imaging ◽  
Combined Structure ◽  
The Impact

Despite the impact of osteoarthritis (OA) on patients and the health service, OA remains an elusive condition to define and treat. Traditionally, OA has been diagnosed using radiographs and more recently magnetic resonance imaging; however, the last 20 years of research have changed our thinking about the disease and its treatment. We know today that OA takes up to 10–15 years to develop, has a range of risk factors, and that there is a considerable discordance between symptoms and structural signs, such that new classifications and definitions are moving away from structural criteria to combined structure and pain definitions. This chapter reviews the definition and classification of OA and its prevalence, incidence, and natural history.

Application of High-Resolution Magnetic Resonance Imaging in the Classification of Trial of ORG 10172 in Acute Stroke Treatment in Cerebral Infarction in Posterior Circulation Ischemia

2020 ◽  
Vol 10 (9) ◽  
pp. 2090-2095
Author(s):  
Xiangfu Meng ◽  
Wei Liu
Keyword(s):  
Magnetic Resonance Imaging ◽  
Risk Factors ◽  
Magnetic Resonance ◽  
High Resolution ◽  
Cerebral Infarction ◽  
Posterior Circulation ◽  
Resonance Imaging ◽  
Traditional Risk Factors ◽  
Toast Classification

Objective: To analyze the relationship between TOAST classification of cerebral infarction after cerebral ischemia and traditional risk factors by high-resolution NMR. Methods: A total of 942 patients with cerebral infarction who were hospitalized in our hospital from January 2012 to October 2019 were enrolled. After performing brain magnetic resonance examination, they were classified according to magnetic resonance imaging, and the patient’s age was recorded. Clinical data such as gender and disease history, and routine examination of diabetes, blood lipids, etc., according to the results of the test, TOAST classification, comparison with magnetic resonance imaging classification results, and correlation analysis of risk factors affecting cerebral infarction. Results: The results of the study showed that 942 patients with posterior circulation ischemic cerebral infarction had aortic atherosclerosis (49.04%), small artery occlusion (39.49%), cardiogenic embolism (6.16%), and unexplained type. (5.20%), other reasons (0.11%). There was a significant correlation between DWI imaging characteristics and TOAST classification (χ = 397.785, P = 0.000). Cortical.cortical infarction, unilateral anterior circulation infarction, large perforating infarction, and anterior.posterior circulation infarction were associated with LAA type, and the difference was statistically significant (P < 0.05). Conclusion: The results of the study fully demonstrate that the characteristics of high-resolution NMR imaging are related to the TOAST classification of patients with cerebral infarction caused by posterior circulation ischemia. Traditional risk factors such as age, NIHSS score, coronary heart disease and atrial fibrillation have certain characteristics on DWI imaging. Impact. Therefore, patients with posterior circulation ischemic cerebral infarction need early high-resolution MRI and combined with traditional risk factors to choose treatment options to reduce the disability and mortality of patients.

scholarly journals High-Resolution Vessel Wall Magnetic Resonance Imaging of Small Unruptured Intracranial Aneurysms

2021 ◽  
Vol 10 (2) ◽  
pp. 225
Author(s):  
Łukasz Zwarzany ◽  
Ernest Tyburski ◽  
Wojciech Poncyljusz
Keyword(s):  
Magnetic Resonance Imaging ◽  
Risk Factors ◽  
Magnetic Resonance ◽  
High Resolution ◽  
Vessel Wall ◽  
Intracranial Aneurysms ◽  
Aneurysm Rupture ◽  
Resonance Imaging ◽  
Aneurysm Wall ◽  
Conventional Risk Factors

Background: We decided to investigate whether aneurysm wall enhancement (AWE) on high-resolution vessel wall magnetic resonance imaging (HR VW-MRI) coexists with the conventional risk factors for aneurysm rupture. Methods: We performed HR VW-MRI in 46 patients with 64 unruptured small intracranial aneurysms. Patient demographics and clinical characteristics were recorded. The PHASES score was calculated for each aneurysm. Results: Of the 64 aneurysms, 15 (23.4%) showed wall enhancement on post-contrast HR VW-MRI. Aneurysms with wall enhancement had significantly larger size (p = 0.001), higher dome-to-neck ratio (p = 0.024), and a more irregular shape (p = 0.003) than aneurysms without wall enhancement. The proportion of aneurysms with wall enhancement was significantly higher in older patients (p = 0.011), and those with a history of prior aneurysmal SAH. The mean PHASES score was significantly higher in aneurysms with wall enhancement (p < 0.000). The multivariate logistic regression analysis revealed that aneurysm irregularity and the PHASES score are independently associated with the presence of AWE. Conclusions: Aneurysm wall enhancement on HR VW-MRI coexists with the conventional risk factors for aneurysm rupture.

scholarly journals Impact of the COVID-19 pandemic on neuroimaging scan volumes at a teaching hospital

2021 ◽  
pp. 197140092098866
Author(s):  
Daniel Thomas Ginat ◽  
James Kenniff
Keyword(s):  
Magnetic Resonance Imaging ◽  
Computed Tomography ◽  
Magnetic Resonance ◽  
The United States ◽  
Radiology Department ◽  
Magnetic Resonance Imaging Scan ◽  
Resonance Imaging ◽  
Academic Medical Centre ◽  
Academic Medical ◽  
The Impact

Background The COVID-19 pandemic led to a widespread socioeconomic shutdown, including medical facilities in many parts of the world. The purpose of this study was to assess the impact on neuroimaging utilisation at an academic medical centre in the United States caused by this shutdown. Methods Exam volumes from 1 February 2020 to 11 August 2020 were calculated based on patient location, including outpatient, inpatient and emergency, as well as modality type, including computed tomography and magnetic resonance imaging. 13 March 2020 was designated as the beginning of the shutdown period for the radiology department and 1 May 2020 was designated as the reopening date. The scan volumes during the pre-shutdown, shutdown and post-shutdown periods were compared using t-tests. Results Overall, neuroimaging scan volumes declined significantly by 41% during the shutdown period and returned to 98% of the pre-shutdown period levels after the shutdown, with an estimated 3231 missed scans. Outpatient scan volumes were more greatly affected than inpatient scan volumes, while emergency scan volumes declined the least during the shutdown. In addition, the magnetic resonance imaging scan volumes declined to a greater degree than the computed tomography scan volumes during the shutdown. Conclusion The shutdown from the COVID-19 pandemic had a substantial but transient impact on neuroimaging utilisation overall, with variable magnitude depending on patient location and modality type.

Accuracy of Computed Tomography and Magnetic Resonance Imaging for Detection of Pathologic Risk Factors in Patients Diagnosed with Retinoblastoma

2021 ◽  
pp. 1-7
Author(s):  
Damrong Wiwatwongwana ◽  
Pichaya Kulniwatcharoen ◽  
Pongsak Mahanupab ◽  
Pannee Visrutaratna ◽  
Atchareeya Wiwatwongwana
Keyword(s):  
Magnetic Resonance Imaging ◽  
Risk Factors ◽  
Computed Tomography ◽  
Magnetic Resonance ◽  
Resonance Imaging

scholarly journals Neuroimaging at Term Equivalent Age: Is There Value for the Preterm Infant? A Narrative Summary

Children ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 227
Author(s):  
Rudaina Banihani ◽  
Judy Seesahai ◽  
Elizabeth Asztalos ◽  
Paige Terrien Church
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Preterm Infant ◽  
Resonance Imaging ◽  
Long Term Outcomes ◽  
Equivalent Age ◽  
Current State ◽  
Patterns Of Injury ◽  
The Impact

Advances in neuroimaging of the preterm infant have enhanced the ability to detect brain injury. This added information has been a blessing and a curse. Neuroimaging, particularly with magnetic resonance imaging, has provided greater insight into the patterns of injury and specific vulnerabilities. It has also provided a better understanding of the microscopic and functional impacts of subtle and significant injuries. While the ability to detect injury is important and irresistible, the evidence for how these injuries link to specific long-term outcomes is less clear. In addition, the impact on parents can be profound. This narrative summary will review the history and current state of brain imaging, focusing on magnetic resonance imaging in the preterm population and the current state of the evidence for how these patterns relate to long-term outcomes.

scholarly journals Effect of early sleep apnoea treatment with adaptive servo-ventilation in acute stroke patients on cerebral lesion evolution and neurological outcomes: study protocol for a multicentre, randomized controlled, rater-blinded, clinical trial (eSATIS: early Sleep Apnoea Treatment in Stroke)

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Simone B. Duss ◽  
Anne-Kathrin Brill ◽  
Sébastien Baillieul ◽  
Thomas Horvath ◽  
Frédéric Zubler ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Acute Stroke ◽  
Acute Ischaemic Stroke ◽  
Sleep Apnoea ◽  
Resonance Imaging ◽  
Stroke Patients ◽  
Adaptive Servo Ventilation ◽  
Randomized Controlled ◽  
The Impact

Abstract Background Sleep-disordered breathing (SDB) is highly prevalent in acute ischaemic stroke and is associated with worse functional outcome and increased risk of recurrence. Recent meta-analyses suggest the possibility of beneficial effects of nocturnal ventilatory treatments (continuous positive airway pressure (CPAP) or adaptive servo-ventilation (ASV)) in stroke patients with SDB. The evidence for a favourable effect of early SDB treatment in acute stroke patients remains, however, uncertain. Methods eSATIS is an open-label, multicentre (6 centres in 4 countries), interventional, randomized controlled trial in patients with acute ischaemic stroke and significant SDB. Primary outcome of the study is the impact of immediate SDB treatment with non-invasive ASV on infarct progression measured with magnetic resonance imaging in the first 3 months after stroke. Secondary outcomes are the effects of immediate SDB treatment vs non-treatment on clinical outcome (independence in daily functioning, new cardio-/cerebrovascular events including death, cognition) and physiological parameters (blood pressure, endothelial functioning/arterial stiffness). After respiratory polygraphy in the first night after stroke, patients are classified as having significant SDB (apnoea-hypopnoea index (AHI) > 20/h) or no SDB (AHI < 5/h). Patients with significant SDB are randomized to treatment (ASV+ group) or no treatment (ASV− group) from the second night after stroke. In all patients, clinical, physiological and magnetic resonance imaging studies are performed between day 1 (visit 1) and days 4–7 (visit 4) and repeated at day 90 ± 7 (visit 6) after stroke. Discussion The trial will give information on the feasibility and efficacy of ASV treatment in patients with acute stroke and SDB and allows assessing the impact of SDB on stroke outcome. Diagnosing and treating SDB during the acute phase of stroke is not yet current medical practice. Evidence in favour of ASV treatment from a randomized multicentre trial may lead to a change in stroke care and to improved outcomes. Trial registration ClinicalTrials.gov NCT02554487, retrospectively registered on 16 September 2015 (actual study start date, 13 August 2015), and www.kofam.ch (SNCTP000001521).

Survey of denoising, segmentation and classification of magnetic resonance imaging for prostate cancer

2021 ◽  
Author(s):  
Mamta Juneja ◽  
Sumindar Kaur Saini ◽  
Jatin Gupta ◽  
Poojita Garg ◽  
Niharika Thakur ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Resonance Imaging

scholarly journals Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

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