SDHx mutation and the hereditary Head- and Neck paraganglioma: what the radiologist should know

SDHx mutation is a recent discovery in Head- and Neck-paraganglioma (HNPGLs, once known as "glomus tumors") and genetic fields, showing its influence on imaging workup and therapeutic approach. These pieces of knowledge are increasing over the years along with the emerging clinical value of techniques for genetic analyses

Conceivable difference in the anti-inflammatory mechanisms of lipocortins 1 and 5

Human recombinant lipocortins (LCT) 1 and 5 have been expressed in a yeast secretion vector and purified by ion exchange chromatography. The action of the proteins has been investigated in two models of experimental acute inflammation in the rat: carrageenin induced paw oedema and zymosan induced pleurisy. The effects of the proteins on PGE2 release in vitro by rat macrophages stimulated with zymosan and on rat neutrophil chemotaxis induced by FMLP have also been assessed. LCT-1 significantly inhibited both paw swelling in carrageenin oedema and leukocyte migration in zymosan pleurisy. Moreover it showed a dose dependent, inhibitory effect on PGE2 release. Neutrophil chemotaxis was only weakly affected by LCT-1. Conversely LCT-5 did not reduce carrageenin oedema and slightly inhibited PGE2 release, but showed profound, dose dependent inhibitory activity on leukocyte migration in zymosan pleurisy and on neutrophil chemotaxis. These data suggest that LCT-1 acts mainly by interfering with arachidonic acid metabolism via the inhibition of phospholipase A2. The anti-inflammatory activity of LCT-5, at variance with LCT-1, may be due to a direct effect on cell motility in addition to the interference with arachidonic acid metabolism

Unsupervised Deformable Image Registration for Respiratory Motion Compensation in Ultrasound Images

In this paper, we present a novel deep-learning model for deformable registration of ultrasound images and an unsupervised approach to training this model. Our network employs recurrent all-pairs field transforms (RAFT) and a spatial transformer network (STN) to generate displacement fields at online rates (apprx. 30 Hz) and accurately track pixel movement. We call our approach unsupervised recurrent all-pairs field transforms (U-RAFT). In this work, we use U-RAFT to track pixels in a sequence of ultrasound images to cancel out respiratory motion in lung ultrasound images. We demonstrate our method on in-vivo porcine lung videos. We show a reduction of 76% in average pixel movement in the porcine dataset using respiratory motion compensation strategy. We believe U-RAFT is a promising tool for compensating different kinds of motions like respiration and heartbeat in ultrasound images of deformable tissue

Assessing ECG signal quality indices to discriminate ECGs with artefacts from pathologically different arrhythmic ECGs

False and non-actionable alarms in critical care can be reduced by developing algorithms which assess the trueness of an arrhythmia alarm from a bedside monitor. Computational approaches that automatically identify artefacts in ECG signals are an important branch of physiological signal processing which tries to address this issue. Signal quality indices (SQIs) derived considering differences between artefacts which occur in ECG signals and normal QRS morphology have the potential to discriminate pathologically different arrhythmic ECG segments as artefacts. Using ECG signals from the PhysioNet/Computing in Cardiology Challenge 2015 training set, we studied previously reported ECG SQIs in the scientific literature to differentiate ECG segments with artefacts from arrhythmic ECG segments. We found that the ability of SQIs to discriminate between ECG artefacts and arrhythmic ECG varies based on arrhythmia type since the pathology of each arrhythmic ECG waveform is different. Therefore, to reduce the risk of SQIs classifying arrhythmic events as noise it is important to validate and test SQIs with databases that include arrhythmias. Arrhythmia specific SQIs may also minimize the risk of misclassifying arrhythmic events as noise

Unsupervised Deformable Ultrasound Image Registration and Its Application for Vessel Segmentation

This paper presents a deep-learning model for deformable registration of ultrasound images at online rates, which we call U-RAFT. As its name suggests, U-RAFT is based on RAFT, a convolutional neural network for estimating optical flow. U-RAFT, however, can be trained in an unsupervised manner and can generate synthetic images for training vessel segmentation models. We propose and compare the registration quality of different loss functions for training U-RAFT. We also show how our approach, together with a robot performing force-controlled scans, can be used to generate synthetic deformed images to significantly expand the size of a femoral vessel segmentation training dataset without the need for additional manual labeling. We validate our approach on both a silicone human tissue phantom as well as on in-vivo porcine images. We show that U-RAFT generates synthetic ultrasound images with 98% and 81% structural similarity index measure (SSIM) to the real ultrasound images for the phantom and porcine datasets, respectively. We also demonstrate that synthetic deformed images from U-RAFT can be used as a data augmentation technique for vessel segmentation models to improve intersection-over-union (IoU) segmentation performanc