SUMMARY: Different MR imaging patterns of cerebral fat embolism have been reported in the literature without a systematic review. Our goal was to describe the patterns, explore the relationship between disease course and the imaging patterns, and discuss the underlying mechanism. We reveal 5 distinctive MR imaging patterns: 1) scattered embolic ischemia occurring dominantly at the acute stage; 2) confluent symmetric cytotoxic edema located at the cerebral white matter, which mainly occurs at the subacute stage; 3) vasogenic edematous lesions also occurring at the subacute stage; 4) petechial hemorrhage, which persists from the acute to the chronic stage; and 5) chronic sequelae, occurring at late stage, including cerebral atrophy, demyelinating change, and sequelae of infarction or necrosis. Underlying mechanisms of these imaging patterns are further discussed. Recognition of the 5 evolving MR imaging patterns of cerebral fat embolism may result in adjustment of the appropriate management and improve the outcome.
ABBREVIATION: CFE ϭ cerebral fat embolism
Early detection and diagnosis of coronary artery disease could reduce the risk of developing a heart attack. The coronary arteries are optimally visualised using computed tomography coronary angiography (CTCA) imaging. These images are reviewed by specialist radiologists who evaluate the coronary arteries for potential narrowing. A lack of radiologists in the UK is a constraint to timely diagnosis of coronary artery disease, particularly in the acute accident and emergency department setting. The development of automated methods by which coronary artery narrowing can be identified rapidly and accurately are therefore timely. Such complex computer-based tools also need to be sufficiently computationally efficient that they can run on servers typically found in hospital settings, where graphical processing units for example are unavailable. We propose a fully automatic two-dimensional Unet model to segment the aorta and coronary arteries on CTCA images. Two models are trained to segment two regions of interest, (1) the aorta and the coronary arteries or (2) the coronary arteries alone. Our method achieves 91.20% and 88.80% dice similarity coefficient accuracy on regions of interest 1 and 2 respectively.Compared with a semi-automatic segmentation method, our model performs better when segmenting the coronary arteries alone. The performance of the proposed method is comparable to existing published twodimensional or three-dimensional deep learning models. Importantly, the algorithmic and graphical processing unit memory efficiencies are maintained such that the model can be deployed without requiring graphical processing units, and therefore can be used in a hospital setting.
An automated attenuation correction and normalisation algorithm was developed to improve the quantification of contrast enhancement in ultrasound images of carotid arteries. The algorithm first corrects attenuation artefact and normalises intensity within the contrast agentfilled lumen and then extends the correction and normalisation to regions beyond the lumen. The algorithm was first validated on phantoms consisting of contrast agent-filled vessels embedded in tissue-mimicking materials of known attenuation. It was subsequently applied to in vivo contrast-enhanced ultrasound (CEUS) images of human carotid arteries. Both in vitro and in vivo results indicated significant reduction in the shadowing artefact and improved homogeneity within the carotid lumens after the correction. The error in quantification of microbubble contrast enhancement caused by attenuation on phantoms was reduced from 55% to 5% on average. In conclusion, the proposed method exhibited great potential in reducing attenuation artefact and improving quantification in contrast-enhanced ultrasound of carotid arteries.
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