Sparse Analyzer Tool for Biomedical Signals.

The virtual (software) instrument with a statistical analyzer for testing algorithms for biomedical signals' recovery in compressive sensing (CS) scenario is presented. Various CS reconstruction algorithms are implemented with the aim to be applicable for different types of biomedical signals and different applications with under-sampled data. Incomplete sampling/sensing can be considered as a sort of signal damage, where missing data can occur as a result of noise or the incomplete signal acquisition procedure. Many approaches for recovering the missing signal parts have been developed, depending on the signal nature. Here, several approaches and their applications are presented for medical signals and images. The possibility to analyze results using different statistical parameters is provided, with the aim to choose the most suitable approach for a specific application. The instrument provides manifold possibilities such as fitting different parameters for the considered signal and testing the efficiency under different percentages of missing data. The reconstruction accuracy is measured by the mean square error (MSE) between original and reconstructed signal. Computational time is important from the aspect of power requirements, thus enabling the selection of a suitable algorithm. The instrument contains its own signal database, but there is also the possibility to load any external data for analysis.

Building a Successful Superficial Venous Program.

OPINION STATEMENT: As the treatment of superficial venous insufficiency transitioned from the hospital to the office setting, a remarkable increase in provider interest developed. However, the novelty of the disease process and procedural opportunities are tempered by the challenges associated with knowledge acquisition, skill development, strategic planning, and program development. Only a unique recipe of clinical growth, technical acumen, management skill, operational efficiency, and financial sense lead to program success. Despite the variety of skills required, treating superficial venous insufficiency is obtainable for both the formally and non-formally trained physicians with sufficient commitment, education, and resources.

Usefulness of 64-detector computed tomographic angiography for diagnosing in-stent restenosis in native coronary arteries.

The purpose of this study was to evaluate the accuracy of detector computed tomographic angiographic qualitative and quantitative analyses for the detection of in-stent restenosis (ISR) Previous studies have used qualitative analyses exclusively and have excluded "unevaluable" stents. Multidetector computed tomographic angiography (MDCT) was performed before quantitative coronary angiography in 67 patients with 132 stents that were evaluated by 2 techniques: (1) qualitative, on the basis of degree of visual hypodensity, and (2) quantitative, comparing in-stent with prestent Hounsfield units. All stents were evaluated, irrespective of image quality. The incidence of ISR was 12.5%. The sensitivity (94%), specificity (74%), and positive predictive value (39%) of the qualitative evaluation were superior to the quantitative technique (82%, 54%, and 21%, respectively); negative predictive values were similar (99% vs 95%). Accuracies were equal in stents located in proximal and distal vessels. In conclusion, ISR can be evaluated qualitatively by 64-slice MDCT with excellent sensitivity and negative predictive accuracy without exclusion of unevaluable stents and with reasonable specificity but low positive predictive value. Quantitative analysis was less accurate.