Clinical trials and studies about imaging technology can be found on this channel.
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.
Experimental Protocol and Representative MRI of Brains at Various Key Points in That Protocol. (A) Experimental timeline. (B) Representative T2WI (using an 11.7T MRI) of the brain of a postnatal day (PND) 11 pup, 1 day after inducing left HII and prior to hNSC transplantation. Note the beginning of an increasingly intense “water signal” (white) on the left (“HII lesion”). (C) Representative T2WI (using an 11.7T MRI) 3 days post-HII, shortly after implantation of SPIO pre-labeled hNSCs into the contralateral cerebral ventricle (“Lateral Vent”). Note the “HII lesion” on the left becoming hyperintense (white) and the black signal void of the SPIO-labeled hNSCs in the lateral ventricle (black arrow). Red arrows denote the needle track. In contrast to what occurs in the intact brain (Figure S4), in a brain subjected to left HII, the implanted SPIO-labeled hNSCs (black signal void) (black arrow) migrate from the right (“R”) to the left (“L”) hemisphere to enter the lesion. (D and E) Shown here (using a 4.7T MRI) are SPIO-labeled hNSCs (black signal void) (black arrow) at 1 month post-implantation into the contralateral ventricle (D) and, in the same representative animal, at 3 months post-implantation (E)–stably integrated and surrounding a much-reduced residual lesion, with no interval enlargement of the graft or ventricles.
According to the 10 authors from multiple institutions across the US who reviewed the most frequently cited studies on the subject: 'Test performance and management issues arise when inappropriate and potentially overreaching conclusions regarding the diagnostic performance of CT for COVID-19 pneumonia are based on low-quality studies with biased cohorts, confounding variables, and faulty design characteristics. Image courtesy of American Journal of Roentgenology (AJR)
Fig 1. A sample scoring on CT images of a 63-year-old woman from mortality group demonstrated a total score of 63. It was calculated as: for upper zone (A), 3 (consolidation) × 3 (50–75% distribution) × 2 (both right and left lungs) + 2 (ground glass opacity) ×1 (< 25% distribution) × 2 (both right and left lungs); for middle zone (B), 3 (consolidation) × 2 (25–50% distribution) × 2 (both right and left lungs) + 2 (ground glass opacity) × 2 (25–50% distribution) × 2 (both right and left lungs); for lower zone (C), 3 (consolidation) × (2 (25–50% distribution of the right lung) + 3 (50–75% distribution of the left lung)) + 2 (ground glass opacity) × (2 (25–50% distribution of the right lung) + 1 (< 25% distribution of the left lung)) Yuan et al, 2020 (CC BY 4.0)
Example: SoftVue image stacks of sound speed, as shown for cases ranging across the four Breast Imaging Reporting and Data System (BI-RADS) breast density categories ((a), fatty; (b), scattered; (c), heterogeneously dense; (d), extremely dense). Note the quantitative scale indicating that absolute measurements are obtained. Image courtesy of MDPI
Figure 1: Depiction of the fully automated CT biomarkers tools used in this study. (A) Schematic depiction of the automated process for assessing fat, muscle, liver, aortic calcification, and bone from original abdominal CT scan data. (B) Case example in an asymptomatic 52-year-old man undergoing CT for colorectal cancer screening. At the time of CT screening, he had a body-mass index of 27·3 and Framingham risk score of 5% (low risk). However, several CT-based metabolic markers were indicative of underlying disease. Multivariate Cox model prediction based on these three CT-based results put the risk of cardiovascular event at 19% within 2 years, at 40% within 5 years, and at 67% within 10 years, and the risk of death at 4% within 2 years, 11% within 5 years, and 27% within 10 years. At longitudinal clinical follow-up, the patient suffered an acute myocardial infarction 3 years after this initial CT and died 12 years after CT at the age of 64 years. (C) Contrast-enhanced CT performed 7 months before death for minor trauma was interpreted as negative but does show significant progression of vascular calcification, visceral fat, and hepatic steatosis. HU=Hounsfield units.