Imaging Technology News - ITN
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.
Top image: Chest radiograph of a 23-year-old male with no past medical history who tested positive for COVID-19 via RT-PCR and was subsequently discharged from the emergency department with home care and isolation precautions. Portable CXR shows right and left peripheral lower lung zone hazy opacities; total score=2.
Bottom image: Chest radiograph in a 32-year-old overweight (BMI=30) COVID-19 positive male with a history of childhood asthma who was subsequently admitted and intubated in the ICU for 3 days. Portable CXR shows opacities in all three right lung zones and in the left middle and lower lung zones; total score=5. Image courtesy of Mount Sinai Health System
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.
Maximum-intensity projections, transaxial fusion, and PET images of 18F-PSMA1007 (A-C) and 68Ga-PSMA-11 (D-F) PET/CT scans of 67-y-old patient with GS 8 and PSA 4.9 ng/mL. Marked uptake is seen in urinary bladder and left ureter (arrow) on maximum-intensity projection image of 68Ga-PSMA-11 (D), as opposed to nearly negligible 18F-PSMA-1007 urinary excretion (A). Dominant lesion in left prostatic lobe is evident on both scans (arrowheads). However, second lesion is seen in right lobe only on 18F-PSMA-1007 scan (arrow in C), later verified on pathology as true malignant lesion. Images created by J. Kuten et al., Tel-Aviv Sourasky Medical Center, Tel-Aviv, Israel.