Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

preliminary clinical evaluation of the ASTRA4D algorithm

Objectives. To propose and evaluate a four-dimensional (4D) algorithm for joint motion elimination and spatiotemporal noise reduction in low-dose dynamic myocardial computed tomography perfusion (CTP). Methods. Thirty patients with suspected or confirmed coronary artery disease were prospectively included und underwent dynamic contrast-enhanced 320-row CTP. The presented deformable image registration method ASTRA4D identifies a low-dimensional linear model of contrast propagation (by principal component analysis, PCA) of the ex-ante temporally smoothed time-intensity curves (by local polynomial regression). Quantitative (standard deviation, signal-to-noise ratio (SNR), temporal variation, volumetric deformation) and qualitative (motion, contrast, contour sharpness; 1, poor; 5, excellent) measures of CTP quality were assessed for the original and motion-compensated volumes (without and with temporal filtering, PCA/ASTRA4D). Following visual myocardial perfusion deficit detection by two readers, diagnostic accuracy was evaluated using 1.5T magnetic resonance (MR) myocardial perfusion imaging as the reference standard in 15 patients. Results. Registration using ASTRA4D was successful in all 30 patients and resulted in comparison with the benchmark PCA in significantly (p<0.001) reduced noise over time (-83%, 178.5 vs 29.9) and spatially (-34%, 21.4 vs 14.1) as well as improved SNR (+47%, 3.6 vs 5.3) and subjective image quality (motion, contrast, contour sharpness: +1.0, +1.0, +0.5). ASTRA4D resulted in significantly improved per-segment sensitivity of 91% (58/64) and similar specificity of 96% (429/446) compared with PCA (52%, 33/64; 98%, 435/446; p=0.011) and the original sequence (45%, 29/64; 98%, 438/446; p=0.003) in the visual detection of perfusion deficits. Conclusions. The proposed functional approach to temporal denoising and morphologic alignment was shown to improve quality metrics and sensitivity of 4D CTP in the detection of myocardial ischemia.Zielsetzung. Die Entwicklung und Bewertung einer Methode zur simultanen Rauschreduktion und Bewegungskorrektur für niedrig dosierte dynamische CT Myokardperfusion. Methoden. Dreißig prospektiv eingeschlossene Patienten mit vermuteter oder bestätigter koronarer Herzkrankheit wurden einer dynamischen CT Myokardperfusionsuntersuchung unterzogen. Die präsentierte Registrierungsmethode ASTRA4D ermittelt ein niedrigdimensionales Modell des Kontrastmittelflusses (mittels einer Hauptkomponentenanalyse, PCA) der vorab zeitlich geglätteten Intensitätskurven (mittels lokaler polynomialer Regression). Quantitative (Standardabweichung, Signal-Rausch-Verhältnis (SNR), zeitliche Schwankung, räumliche Verformung) und qualitative (Bewegung, Kontrast, Kantenschärfe; 1, schlecht; 5, ausgezeichnet) Kennzahlen der unbearbeiteten und bewegungskorrigierten Perfusionsdatensätze (ohne und mit zeitlicher Glättung PCA/ASTRA4D) wurden ermittelt. Nach visueller Beurteilung von myokardialen Perfusionsdefiziten durch zwei Radiologen wurde die diagnostische Genauigkeit im Verhältnis zu 1.5T Magnetresonanztomographie in 15 Patienten ermittelt. Resultate. Bewegungskorrektur mit ASTRA4D war in allen 30 Patienten erfolgreich und resultierte im Vergleich mit der PCA Methode in signifikant (p<0.001) verringerter zeitlicher Schwankung (-83%, 178.5 gegenüber 29.9) und räumlichem Rauschen (-34%, 21.4 gegenüber 14.1) sowie verbesserter SNR (+47%, 3.6 gegenüber 5.3) und subjektiven Qualitätskriterien (Bewegung, Kontrast, Kantenschärfe: +1.0, +1.0, +0.5). ASTRA4D resultierte in signifikant verbesserter segmentweiser Sensitivität 91% (58/64) und ähnlicher Spezifizität 96% (429/446) verglichen mit der PCA Methode (52%, 33/64; 98%, 435/446; p=0.011) und dem unbearbeiteten Perfusionsdatensatz (45%, 29/64; 98%, 438/446; p=0.003) in der visuellen Beurteilung von myokardialen Perfusionsdefiziten. Schlussfolgerungen. Der vorgeschlagene funktionale Ansatz zur simultanen Rauschreduktion und Bewegungskorrektur verbesserte Qualitätskriterien und Sensitivität von dynamischer CT Perfusion in der visuellen Erkennung von Myokardischämie

Aerospace Medicine and Biology: A continuing bibliography, supplement 191

A bibliographical list of 182 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1979 is presented

Focal Spot, Winter 2006/2007

https://digitalcommons.wustl.edu/focal_spot_archives/1104/thumbnail.jp

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 190, February 1979

This bibliography lists 235 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1979

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 182, July 1978

This bibliography lists 165 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1978

Evaluation of pericoronary adipose tissue attenuation on CT

Pericoronary adipose tissue (PCAT) is the fat deposit surrounding coronary arteries. Although PCAT is part of the larger epicardial adipose tissue (EAT) depot, it has different pathophysiological features and roles in the atheroscle-rosis process. While EAT evaluation has been studied for years, PCAT evaluation is a relatively new concept. PCAT, especially the mean attenuation derived from CT images may be used to evaluate the inflammatory status of coronary arteries non-invasively. The most commonly used measure, PCATMA, is the mean attenuation of adipose tissue of 3 mm thickness around the proximal right coronary artery with a length of 40 mm. PCATMA can be analyzed on a per-lesion, per-vessel or per-patient basis. Apart from PCATMA, other measures for PCAT have been studied, such as thickness, and volume. Studies have shown associations between PCATMA and anatomical and functional severity of coronary artery disease. PCATMA is associated with plaque components and high -risk plaque features, and can discriminate patients with flow obstructing stenosis and myocardial infarction. Whether PCATMA has value on an individual patient basis remains to be determined. Furthermore, CT imaging settings, such as kV levels and clinical factors such as age and sex affect PCATMA measurements, which complicate implementation in clinical practice. For PCATMA to be widely implemented, a standardized methodology is needed. This review gives an overview of reported PCAT methodologies used in current literature and the potential use cases in clinical practice