The Nuclear Imaging Uncertainty Principle. Do Nuclear Cameras Really Work?

The introduction of the Heisenberg Uncertainty principle and Nuclear Cardiology occurred simultaneously in 1925-1927. Thirty years later the Anger gamma camera would allow for a more sophisticated radioactive isotope counting to determine the presence or absence of disease. When employed with technetium-99m isotopes, ischemic heart disease can be inferred by differences in visual appearance of cardiac images. These gestalts of imaging results have been separated from the quantitative information recorded by the cameras computer. We investigated whether current camera and computer systems are sophisticated enough to quantify differences between images to be clinically relevant. Our study demonstrated that efforts to "sharpen" image appearance does so at a reduction in "accuracy". Like Heisenberg, this work shows that one cannot know the exact location AND the amount of activity simultaneously and that a decision must be made for accuracy over image sharpness if one is to truly quantify differences in isotope concentration between images

Adding attenuation corrected images in myocardial perfusion imaging reduces the need for a rest study.

The American Society of Nuclear Cardiology and the Society of Nuclear Medicine conclude that incorporation of attenuation corrected (AC) images in myocardial perfusion scintigraphy (MPS) will improve diagnostic accuracy. The aim was to investigate the value of adding AC stress-only images for the decision whether a rest study is necessary or not

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

World wide web and scientific publishing

The relationship of scientific publishing and the world wide web steadily evolves as the internet technology advances in its sophistication and ability to reach an increasing number of people. Many well established medical publication currently publish their journal electronically as well as in print format. Forced by high volume of manuscript submissions as well as financial restraints, some journals publish a number of their articles on the internet alone, while others-such as this journal-are starting an entirely new publication in an electronic version only. The presentation of articles in the field of pediatric cardiology electronically has numerous advantages, particularly the ability to include movie clips, sound and animations in the published articles. This format of publication continues to have limitation. It is still not as widely available as a print journal could be and it is awkward to curl up in bed with a lap top, no matter how small or light it ispeer-reviewe

Novel X-ray imaging technology enables significant patient dose reduction in interventional cardiology while maintaining diagnostic image quality

Objectives: The purpose of this study was to quantify the reduction in patient radiation dose during coronary angiography (CA) by a new X-ray technology, and to assess its impact on diagnostic image quality. Background: Recently, a novel X-ray imaging technology has become available for interventional cardiology, using advanced image processing and an optimized acquisition chain for radiation dose reduction. Methods: 70 adult patients were randomly assigned to a reference X-ray system or the novel X-ray system. Patient demographics were registered and exposure parameters were recorded for each radiation event. Clinical image quality was assessed for both patient groups. Results: With the same angiographic technique and a comparable patient population, the new imaging technology was associated with a 75% reduction in total kerma-area product (KAP) value (decrease from 47 Gycm(2) to 12 Gycm(2), P<0.001). Clinical image quality showed an equivalent detail and contrast for both imaging systems. On the other hand, the subjective appreciation of noise was more apparent in images of the new image processing system, acquired at lower doses, compared to the reference system. However, the higher noise content did not affect the overall image quality score, which was adequate for diagnosis in both systems. Conclusions: For the first time, we present a new X-ray imaging technology, combining advanced noise reduction algorithms and an optimized acquisition chain, which reduces patient radiation dose in CA drastically (75%), while maintaining diagnostic image quality. Use of this technology may further improve the radiation safety of cardiac angiography and interventions

The Relationship of Left Ventricular Trabeculation to Ventricular Function and Structure Over a 9.5-Year Follow-Up The MESA Study

Left ventricular (LV) trabeculation is highly variable among individuals and is increased in some diseases (e.g., congenital heart disease or cardiomyopathies), but its significance in population-representative individuals is unknown

Fast and accurate classification of echocardiograms using deep learning

Echocardiography is essential to modern cardiology. However, human interpretation limits high throughput analysis, limiting echocardiography from reaching its full clinical and research potential for precision medicine. Deep learning is a cutting-edge machine-learning technique that has been useful in analyzing medical images but has not yet been widely applied to echocardiography, partly due to the complexity of echocardiograms' multi view, multi modality format. The essential first step toward comprehensive computer assisted echocardiographic interpretation is determining whether computers can learn to recognize standard views. To this end, we anonymized 834,267 transthoracic echocardiogram (TTE) images from 267 patients (20 to 96 years, 51 percent female, 26 percent obese) seen between 2000 and 2017 and labeled them according to standard views. Images covered a range of real world clinical variation. We built a multilayer convolutional neural network and used supervised learning to simultaneously classify 15 standard views. Eighty percent of data used was randomly chosen for training and 20 percent reserved for validation and testing on never seen echocardiograms. Using multiple images from each clip, the model classified among 12 video views with 97.8 percent overall test accuracy without overfitting. Even on single low resolution images, test accuracy among 15 views was 91.7 percent versus 70.2 to 83.5 percent for board-certified echocardiographers. Confusional matrices, occlusion experiments, and saliency mapping showed that the model finds recognizable similarities among related views and classifies using clinically relevant image features. In conclusion, deep neural networks can classify essential echocardiographic views simultaneously and with high accuracy. Our results provide a foundation for more complex deep learning assisted echocardiographic interpretation.Comment: 31 pages, 8 figure

Concepts in cardiology : a historical perspective

Our current knowledge of the anatomy and physiology of the circulatory system has been reached by deduction and reasoning over several centuries. In this article, the history of these theories is briefly outlined.peer-reviewe

Organized intra-atrial thrombus in growing premature infant

This article reports interesting echocardiographic images of the atrial septum in two growing premature infants related to the previous use of umbilical venous lines in the neonatal period. Complications related to central venous lines are well documented but when they are identified in follow up after several months, their relevance is difficult to establish. Atrial septal findings of organized clot in these asymptomatic infants needs to be correlated with details of neonatal care.peer-reviewe