Extracorporeal membrane oxygenation (ECMO) is a life-saving treatment for pediatric patients with respiratory and/or cardiac failure. The ECMO circuit oxygenates and sometimes pumps the blood, effectively replacing lung and/or heart function temporarily. ECMO patients are clinically very complex not only because of their underlying, life-threatening pathology, but also because of the many physiological parameters that must be monitored and adjusted to maintain adequate tissue perfusion and oxygenation. Drainage and reinfusion cannulae connecting the patient to the ECMO circuit are visible on radiograph. These cannulae have different functions, different configurations, different radiographic appearances, and different positions that should be familiar to the interpreting pediatric radiologist. The primary complications of ECMO include hemorrhage, thrombosis and ischemia, as well as equipment failure and cannula malpositioning, all of which may be detected on imaging. In this pictorial essay, we discuss the basics of ECMO function and clinical management, ECMO cannula features and configurations, and the many complications of ECMO from an imaging perspective. Our goal is to educate pediatric radiologists about ECMO imaging, equipping them to properly interpret these studies and to become a useful consultant in ECMO patient care.
This paper presents a multi-dimensional computational method to predict the spatial variation data inside and across multiple dies of a wafer. This technique is based on tensor computation. A tensor is a high-dimensional generalization of a matrix or a vector. By exploiting the hidden low-rank property of a high-dimensional data array, the large amount of unknown variation testing data may be predicted from a few random measurement samples. The tensor rank, which decides the complexity of a tensor representation, is decided by an available variational Bayesian approach. Our approach is validated by a practical chip testing data set, and it can be easily generalized to characterize the process variations of multiple wafers. Our approach is more efficient than the previous virtual probe techniques in terms of memory and computational cost when handling high-dimensional chip testing data.
The use of three-dimensional (3D) technologies in medical practice is increasing; however, its use is largely untested. One 3D technology, stereoscopic volume-rendered 3D display, can improve depth perception. Pulmonary vein stenosis (PVS) is a rare cardiovascular pathology, often diagnosed by computed tomography (CT), where volume rendering may be useful. Depth cues may be lost when volume rendered CT is displayed on regular screens instead of 3D displays. The objective of this study was to determine whether the 3D stereoscopic display of volume-rendered CT improved perception compared to standard monoscopic display, as measured by PVS diagnosis. CT angiograms (CTAs) from 18 pediatric patients aged 3 weeks to 2 years were volume rendered and displayed with and without stereoscopic display. Patients had 0 to 4 pulmonary vein stenoses. Participants viewed the CTAs in 2 groups with half on monoscopic and half on stereoscopic display and the converse a minimum of 2 weeks later, and their diagnoses were recorded. A total of 24 study participants, comprised of experienced staff cardiologists, cardiovascular surgeons and radiologists, and their trainees viewed the CTAs and assessed the presence and location of PVS. Cases were classified as simple (2 or fewer lesions) or complex (3 or more lesions). Overall, there were fewer type 2 errors in diagnosis for stereoscopic display than standard display, an insignificant difference (p = 0.095). There was a significant decrease in type 2 errors for complex multiple lesion cases (≥3) vs simpler cases (p = 0.027) and improvement in localization of pulmonary veins (p = 0.011). Subjectively, 70% of participants stated that stereoscopy was helpful in the identification of PVS. The stereoscopic display did not result in significantly decreased errors in PVS diagnosis but was helpful for more complex cases.
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