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Tenecteplase for Stroke at 4.5 to 24 Hours with Perfusion-Imaging Selection
Tenecteplase for thrombolysis in a 4.5-to-24-hour window did not improve disability outcomes at 90 days in patients with ischemic stroke who had been chosen on the basis of imaging. Most patients had endovascular thrombectomy.
Journal Article
Amygdalar nuclei and hippocampal subfields on MRI: Test-retest reliability of automated volumetry across different MRI sites and vendors
The amygdala and the hippocampus are two limbic structures that play a critical role in cognition and behavior, however their manual segmentation and that of their smaller nuclei/subfields in multicenter datasets is time consuming and difficult due to the low contrast of standard MRI. Here, we assessed the reliability of the automated segmentation of amygdalar nuclei and hippocampal subfields across sites and vendors using FreeSurfer in two independent cohorts of older and younger healthy adults.
Sixty-five healthy older (cohort 1) and 68 younger subjects (cohort 2), from the PharmaCog and CoRR consortia, underwent repeated 3D-T1 MRI (interval 1–90 days). Segmentation was performed using FreeSurfer v6.0. Reliability was assessed using volume reproducibility error (ε) and spatial overlapping coefficient (DICE) between test and retest session.
Significant MRI site and vendor effects (p < .05) were found in a few subfields/nuclei for the ε, while extensive effects were found for the DICE score of most subfields/nuclei. Reliability was strongly influenced by volume, as ε correlated negatively and DICE correlated positively with volume size of structures (absolute value of Spearman’s r correlations >0.43, p < 1.39E-36). In particular, volumes larger than 200 mm3 (for amygdalar nuclei) and 300 mm3 (for hippocampal subfields, except for molecular layer) had the best test-retest reproducibility (ε < 5% and DICE > 0.80).
Our results support the use of volumetric measures of larger amygdalar nuclei and hippocampal subfields in multisite MRI studies. These measures could be useful for disease tracking and assessment of efficacy in drug trials.
•Differences in MRI site/vendor had a limited effect on volume reproducibility.•Differences in MRI site/vendor had an extensive effect on spatial accuracy.•Reliability is good for larger amygdalar and hippocampal structures.•Automated volumetry is reliable in multicenter MRI studies.
Journal Article
The art of insight : how great visualization designers think
\"The Art of Insight: How Great Visualization Designers Think is a book about making design decisions in difficult situations. Decision-making is an essential skill for designers because anyone can create a data visualization with just a few clicks. Data is easily available online, and multiple free and easy-to-use software tools have appeared in the past few years. These developments have led to an explosion in the amount and variety of graphs, charts, and maps. We see them everywhere, from news publications to social media. Cairo explains this is a positive phenomenon, but only if the creators of those visualizations are able to think clearly and ethically about what they are doing. As the famous line from the 2002 Spider-Man movie says, with great power comes great responsibility. Visualization books often focus on rules for creating charts and maps, but rarely explain the origin of those rules. Readers are told to start all graphs at a zero baseline, never use pie charts, maximize the data-ink ratio, and so on. Cairo argues that this approach is misguided: it shoehorns designers into a single rigid mode of thinking, based only on the perspective of the book's author or authors\"-- Provided by publisher.
Book
Automatic classification of focal liver lesions based on MRI and risk factors
Accurate classification of focal liver lesions is an important part of liver disease diagnostics. In clinical practice, the lesion type is often determined from the abdominal MR examination, which includes T2-weighted and dynamic contrast enhanced (DCE) MR images. To date, only T2-weighted images are exploited for automatic classification of focal liver lesions. In this study additional MR sequences and risk factors are used for automatic classification to improve the results and to make a step forward to a clinically useful aid for radiologists.
Clinical MRI data sets of 95 patients with in total 125 benign lesions (40 adenomas, 29 cysts and 56 hemangiomas) and 88 malignant lesions (30 hepatocellular carcinomas (HCC) and 58 metastases) were included in this study. Contrast curve, gray level histogram, and gray level co-occurrence matrix texture features were extracted from the DCE-MR and T2-weighted images. In addition, risk factors including the presence of steatosis, cirrhosis, and a known primary tumor were used as features. Fifty features with the highest ANOVA F-score were selected and fed to an extremely randomized trees classifier. The classifier evaluation was performed using the leave-one-out principle and receiver operating characteristic (ROC) curve analysis.
The overall accuracy for the classification of the five major focal liver lesion types is 0.77. The sensitivity/specificity is 0.80/0.78, 0.93/0.93, 0.84/0.82, 0.73/0.56, and 0.62/0.77 for adenoma, cyst, hemangioma, HCC, and metastasis, respectively.
The proposed classification system using features derived from clinical DCE-MR and T2-weighted images, with additional risk factors is able to differentiate five common types of lesions and is a step forward to a clinically useful aid for focal liver lesion diagnosis.
Journal Article
Point-of-Care Ultrasonography
Point-of-care ultrasonography (POCUS) is performed by a physician at the bedside and is standard practice in obstetric, emergency, and musculoskeletal medicine. When compared with formal sonography, POCUS is equivalent in screening for abdominal aortic aneurysm and as accurate in diagnosing deep venous thrombosis. POCUS has high accuracy for diagnosing pneumonia and detecting acute decompensated heart failure but is less accurate than computed tomography for identifying pulmonary embolism. POCUS confirmation of intrauterine pregnancy rules out an ectopic pregnancy. In the third trimester of high-risk pregnancies, umbilical artery Doppler ultrasonography can improve perinatal outcomes. Musculoskeletal POCUS is used to diagnose and guide treatment of many joint and soft tissue conditions. It is as accurate as magnetic resonance imaging in the diagnosis of complete rotator cuff tears. Ultrasound guidance improves outcomes in the placement of central venous catheters and fluid drainage from body cavities and lumbar punctures. Ultrasonography can reduce the use of CT for diagnosis of appendicitis; however, negative scan results do not rule out disease. POCUS can accurately diagnose and rule out gallbladder pathology, and is effective for diagnosing urolithiasis. Focused cardiac ultrasonography can detect pericardial effusion and decreased systolic function, but is less accurate than lung ultrasonography at diagnosing acute heart failure. Limited evidence demonstrates a benefit of diagnosing testicular and gynecologic conditions. The American College of Emergency Physicians, the American Institute of Ultrasound in Medicine, the Society for Academic Emergency Medicine, the American College of Radiology, and others offer POCUS training. Training standards for POCUS have been defined for residency programs but are less established for credentialing. Illustration by Jonathan Dimes
Journal Article
Geometric and Topological Mesh Feature Extraction for 3D Shape Analysis
Three-dimensional surface meshes are the most common discrete representation of the exterior of a virtual shape. Extracting relevant geometric or topological features from them can simplify the way objects are looked at, help with their recognition, and facilitate description and categorization according to specific criteria. This book adopts the point of view of discrete mathematics, the aim of which is to propose discrete counterparts to concepts mathematically defined in continuous terms. It explains how standard geometric and topological notions of surfaces can be calculated and computed on a 3D surface mesh, as well as their use for shape analysis. Several applications are also detailed, demonstrating that each of them requires specific adjustments to fit with generic approaches. The book is intended not only for students, researchers and engineers in computer science and shape analysis, but also numerical geologists, anthropologists, biologists and other scientists looking for practical solutions to their shape analysis, understanding or recognition problems.
eBook