Background

Segmented cine cardiac MRI combines data from multiple heartbeats to achieve high spatiotemporal resolution cardiac images, yet predefined k-space segmentation trajectories can lead to suboptimal k-space sampling. In this work, we developed and evaluated an autonomous and closed-loop control system for radial k-space sampling (ARKS) to increase sampling uniformity.

Methods

The closed-loop system autonomously selects radial k-space sampling trajectory during live segmented cine MRI and attempts to optimize angular sampling uniformity by selecting views in regions of k-space that were not previously well-sampled. Sampling uniformity and the ability to detect cardiac phase in vivo was assessed using ECG data acquired from 10 normal subjects in an MRI scanner. The approach was then implemented with a fast gradient echo sequence on a whole-body clinical MRI scanner and imaging was performed in 4 healthy volunteers. The closed-loop k-space trajectory was compared to random, uniformly distributed and golden angle view trajectories via measurement of k-space uniformity and the point spread function. Lastly, an arrhythmic dataset was used to evaluate a potential application of the approach.

Results

The autonomous trajectory increased k-space sampling uniformity by 15±7%, main lobe point spread function (PSF) signal intensity by 6±4%, and reduced ringing relative to golden angle sampling. When implemented, the autonomous pulse sequence prescribed radial view angles faster than the scan TR (0.98 ± 0.01 ms, maximum = 1.38 ms) and increased k-space sampling mean uniformity by 10±11%, decreased uniformity variability by 44±12%, and increased PSF signal ratio by 6±6% relative to golden angle sampling.

Conclusion

The closed-loop approach enables near-uniform radial sampling in a segmented acquisition approach which was higher than predetermined golden-angle radial sampling. This can be utilized to increase the sampling or decrease the temporal footprint of an acquisition and the closed-loop framework has the potential to be applied to patients with complex heart rhythms.

Background

Identification of bacterial coinfection in patients with coronavirus disease 2019 (COVID-19) facilitates appropriate initiation or withholding of antibiotics. The Inflammatix Bacterial Viral Noninfected (IMX-BVN) classifier determines the likelihood of bacterial and viral infections. In a multicenter study, we investigated whether IMX-BVN version 3 (IMX-BVN-3) identifies patients with COVID-19 and bacterial coinfections or superinfections.

Methods

Patients with polymerase chain reaction-confirmed COVID-19 were enrolled in Berlin, Germany; Basel, Switzerland; and Cleveland, Ohio upon emergency department or hospital admission. PAXgene Blood RNA was extracted and 29 host mRNAs were quantified. IMX-BVN-3 categorized patients into very unlikely, unlikely, possible, and very likely bacterial and viral interpretation bands. IMX-BVN-3 results were compared with clinically adjudicated infection status.

Results

IMX-BVN-3 categorized 102 of 111 (91.9%) COVID-19 patients into very likely or possible, 7 (6.3%) into unlikely, and 2 (1.8%) into very unlikely viral bands. Approximately 94% of patients had IMX-BVN-3 unlikely or very unlikely bacterial results. Among 7 (6.3%) patients with possible (n = 4) or very likely (n = 3) bacterial results, 6 (85.7%) had clinically adjudicated bacterial coinfection or superinfection. Overall, 19 of 111 subjects for whom adjudication was performed had a bacterial infection; 7 of these showed a very likely or likely bacterial result in IMX-BVN-3.

Conclusions

IMX-BVN-3 identified COVID-19 patients as virally infected and identified bacterial coinfections and superinfections. Future studies will determine whether a point-of-care version of the classifier may improve the management of COVID-19 patients, including appropriate antibiotic use.

Myocardial infarction leads to changes in the geometry (remodeling) of the left ventricle (LV) of the heart. The degree and type of remodeling provides important diagnostic information for the therapeutic management of ischemic heart disease. In this paper, we present a novel analysis framework for characterizing remodeling after myocardial infarction, using LV shape descriptors derived from atlas-based shape models. Cardiac magnetic resonance images from 300 patients with myocardial infarction and 1991 asymptomatic volunteers were obtained from the Cardiac Atlas Project. Finite element models were customized to the spatio-temporal shape and function of each case using guide-point modeling. Principal component analysis was applied to the shape models to derive modes of shape variation across all cases. A logistic regression analysis was performed to determine the modes of shape variation most associated with myocardial infarction. Goodness of fit results obtained from end-diastolic and end-systolic shapes were compared against the traditional clinical indices of remodeling: end-diastolic volume, end-systolic volume and LV mass. The combination of end-diastolic and end-systolic shape parameter analysis achieved the lowest deviance, Akaike information criterion and Bayesian information criterion, and the highest area under the receiver operating characteristic curve. Therefore, our framework quantitatively characterized remodeling features associated with myocardial infarction, better than current measures. These features enable quantification of the amount of remodeling, the progression of disease over time, and the effect of treatments designed to reverse remodeling effects.

These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.