The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs.

We describe the curation, annotation methodology, and characteristics of the dataset used in an artificial intelligence challenge for detection and localization of COVID-19 on chest radiographs. The chest radiographs were annotated by an international group of radiologists into four mutually exclusive categories, including "typical," "indeterminate," and "atypical appearance" for COVID-19, or "negative for pneumonia," adapted from previously published guidelines, and bounding boxes were placed on airspace opacities. This dataset and respective annotations are available to researchers for academic and noncommercial use.

An Adaptive Seismocardiography (SCG)-ECG Multimodal Framework for Cardiac Gating Using Artificial Neural Networks.

To more accurately trigger data acquisition and reduce radiation exposure of coronary computed tomography angiography (CCTA), a multimodal framework utilizing both electrocardiography (ECG) and seismocardiography (SCG) for CCTA prospective gating is presented. Relying upon a three-layer artificial neural network that adaptively fuses individual ECG- and SCG-based quiescence predictions on a beat-by-beat basis, this framework yields a personalized quiescence prediction for each cardiac cycle. This framework was tested on seven healthy subjects (age: 22-48; m/f: 4/3) and eleven cardiac patients (age: 31-78; m/f: 6/5). Seventeen out of 18 benefited from the fusion-based prediction as compared to the ECG-only-based prediction, the traditional prospective gating method. Only one patient whose SCG was compromised by noise was more suitable for ECG-only-based prediction. On average, our fused ECG-SCG-based method improves cardiac quiescence prediction by 47% over ECG-only-based method; with both compared against the gold standard, B-mode echocardiography. Fusion-based prediction is also more resistant to heart rate variability than ECG-only- or SCG-only-based prediction. To assess the clinical value, the diagnostic quality of the CCTA reconstructed volumes from the quiescence derived from ECG-, SCG- and fusion-based predictions were graded by a board-certified radiologist using a Likert response format. Grading results indicated the fusion-based prediction improved diagnostic quality. ECG may be a sub-optimal modality for quiescence prediction and can be enhanced by the multimodal framework. The combination of ECG and SCG signals for quiescence prediction bears promise for a more personalized and reliable approach than ECG-only-based method to predict cardiac quiescence for prospective CCTA gating.

Echocardiography as an indication of continuous-time cardiac quiescence.

Cardiac computed tomography (CT) angiography using prospective gating requires that data be acquired during intervals of minimal cardiac motion to obtain diagnostic images of the coronary vessels free of motion artifacts. This work is intended to assess B-mode echocardiography as a continuous-time indication of these quiescent periods to determine if echocardiography can be used as a cost-efficient, non-ionizing modality to develop new prospective gating techniques for cardiac CT. These new prospective gating approaches will not be based on echocardiography itself but on CT-compatible modalities derived from the mechanics of the heart (e.g. seismocardiography and impedance cardiography), unlike the current standard electrocardiogram. To this end, echocardiography and retrospectively-gated CT data were obtained from ten patients with varied cardiac conditions. CT reconstructions were made throughout the cardiac cycle. Motion of the interventricular septum (IVS) was calculated from both echocardiography and CT reconstructions using correlation-based, deviation techniques. The IVS was chosen because it (1) is visible in echocardiography images, whereas the coronary vessels generally are not, and (2) has been shown to be a suitable indicator of cardiac quiescence. Quiescent phases were calculated as the minima of IVS motion and CT volumes were reconstructed for these phases. The diagnostic quality of the CT reconstructions from phases calculated from echocardiography and CT data was graded on a four-point Likert scale by a board-certified radiologist fellowship-trained in cardiothoracic radiology. Using a Wilcoxon signed-rank test, no significant difference in the diagnostic quality of the coronary vessels was found between CT volumes reconstructed from echocardiography- and CT-selected phases. Additionally, there was a correlation of 0.956 between the echocardiography- and CT-selected phases. This initial work suggests that B-mode echocardiography can be used as a tool to develop CT-compatible gating techniques based on modalities derived from cardiac mechanics rather than relying on the ECG alone.

Activation detection in event-related fMRI data based on spatio-temporal properties.

Template-based activation detection methods, such as cross-correlation, could be difficult to apply in event-related functional MRI data because accurate a priori knowledge about the activation signal patterns is often not available. As a result, several categories of template-free data analysis techniques have been introduced in the fMRI literature. One previously described template-free activation detection technique is based on the feature that activated voxels yield reproducible time course patterns as the subject undergoes the same simulation in repeated epochs. In this paper, spatial information is incorporated as a second feature and a combined univariate measure is formed. The resulting method is shown to offer measurable improvement in detecting activation regions in simulated data in a highly computationally efficient manner. Its practical utility is demonstrated with an experimental data set obtained with a visually guided motor paradigm.

Nonadditive two-way ANOVA for event-related fMRI data analysis.

A significant recent development in functional magnetic resonance imaging (fMRI) is the introduction of event-related fMRI, also known as time-resolved fMRI. Because the exact shape of the MR response in an event-related fMRI experiment is often not known, traditional methods developed for block design experiments, such as t test and correlation analysis, are not well-suited for extracting activated pixels from the event-related data. In this work, a statistical technique based on nonadditive two-way analysis of variance is developed for use in event-related studies. Theoretical and experimental work were carried out for establishing a statistical threshold to determine pixel activation. Experimental studies were performed to demonstrate the utility of this approach.

Aerosol particle microphotography and glare-spot absorption spectroscopy.

The relative intensities of glare spots in the image of an electrodynamically trapped aerosol droplet are measured experimentally with an aerosol particle microscope and calculated theoretically. The theoretical calculations are in good agreement with these experiments and indicate that the intensities of these spots are extremely sensitive to the imaginary part of the refractive index. Experimentally, we obtain the molecular absorption spectrum of an impurity within a droplet by recording the spectrum of an individual glare spot produced by broadband illumination.