July 10, 2020 — An open-access American Journal of Roentgenology (AJR) article investigating the differences in...
Clinical trials and studies about imaging technology can be found on this channel.
Artificial intelligence (AI)-assisted software was used to identify inflammatory tissues in lung and automatically segment inflammatory lesions. Three-dimensional image shows regions of COVID-19 pneumonia in lung through AI postprocessing. Image courtesy of the American Journal of Roentgenology (AJR)
R2* maps of healthy control participants and participants with Alzheimer disease. R2* maps are windowed between 10 and 50 sec21. Differences in iron concentration in basal ganglia are too small to allow visual separation between patients with Alzheimer disease and control participants, and iron levels strongly depend on anatomic structure and subject age. Image courtesy of Radiological Society of North America
Thoracic findings in a 15-year-old girl with Multisystem Inflammatory Syndrome in Children (MIS-C). (a) Chest radiograph on admission shows mild perihilar bronchial wall cuffing. (b) Chest radiograph on the third day of admission demonstrates extensive airspace opacification with a mid and lower zone predominance. (c, d) Contrast-enhanced axial CT chest of the thorax at day 3 shows areas of ground-glass opacification (GGO) and dense airspace consolidation with air bronchograms. (c) This conformed to a mosaic pattern with a bronchocentric distribution to the GGO (white arrow, d) involving both central and peripheral lung parenchyma with pleural effusions (black small arrow, d). image courtesy of Radiological Society of North America
This image of DCE-MRI reveals persistent blood brain barrier disorder in American football players. Using brain imaging techniques and analytical methods, researchers can determine whether football players have CTE by measuring leakage of the blood-brain barrier. Image courtesy of Ben-Gurion University
a Schematic of the system. The entire solid tumour is illuminated from four sides by a four-arm fibre bundle. A cylindrically focused linear array is designed to detect optoacoustic signals from the tumour. In vivo imaging is performed in conical scanning geometry by controlling the rotation and translation stages. The sensing part of the transducer array and the tumour are submerged in water to provide acoustic coupling. b Maximum intensity projections of the optoacoustic reconstruction of a phantom of polyethylene microspheres (diameter, 20 μm) dispersed in agar. The inset shows a zoomed-in view of the region boxed with a yellow dashed line. In addition, the yellow boxes are signal profiles along the x, y and z axes across the microsphere centre, as well as the corresponding full width at half-maximum values. c Normalized absorption spectra of Hb, HbO2 and gold nanoparticles (AuNPs). The spectrum for the AuNPs was obtained using a USB4000 spectrometer (Ocean Optics, Dunedin, FL, USA), while the spectra for Hb and HbO2 were taken from http://omlc.org/spectra/haemoglobin/index.html. The vertical dashed lines indicate the five wavelengths used to stimulate the three absorbers: 710, 750, 780, 810 and 850 nm. Optoacoustic signals were filtered into a low-frequency band (red) and high-frequency band (green), which were used to reconstruct separate images.
A new technique developed by researchers at UC Davis offers a significant advance in using magnetic resonance imaging to pick out even very small tumors from normal tissue. The team created a probe that generates two magnetic resonance signals that suppress each other until they reach the target, at which point they both increase contrast between the tumor and surrounding tissue. Image courtesy of Xiandoing Xue, UC Davis
Figure 1: Examples of chest CT images of COVID-19 (+) patients and visualization of features correlated to COVID-19 positivity. For each pair of images, the left image is a CT image showing the segmented lung used as input for the CNN (convolutional neural network algorithm) model trained on CT images only, and the right image shows the heatmap of pixels that the CNN model classified as having SARS-CoV-2 infection (red indicates higher probability). (a) A 51-year-old female with fever and history of exposure to SARS-CoV-2. The CNN model identified abnormal features in the right lower lobe (white color), whereas the two radiologists labeled this CT as negative. (b) A 52-year-old female who had a history of exposure to SARS-CoV-2 and presented with fever and productive cough. Bilateral peripheral ground-glass opacities (arrows) were labeled by the radiologists, and the CNN model predicted positivity based on features in matching areas. (c) A 72-year-old female with exposure history to the animal market in Wuhan presented with fever and productive cough. The segmented CT image shows ground-glass opacity in the anterior aspect of the right lung (arrow), whereas the CNN model labeled this CT as negative. (d) A 59-year-old female with cough and exposure history. The segmented CT image shows no evidence of pneumonia, and the CNN model also labeled this CT as negative.