Reconstruction of ultrasound RF echoes modelled as stable random variables

International audienceThis paper introduces a new technique for reconstruction of biomedical ultrasound images from simulated compressive measurements, based on modeling data with stable distributions. The proposed algorithm exploits two types of prior information: on one hand, our proposed approach is based on the observation that ultrasound RF echoes are best characterized statistically by alpha-stable distributions. On the other hand, through knowledge of the acquisition process, the support of the RF echoes in the Fourier domain can be easily inferred. Together, these two facts form the basis of an ℓp minimization approach that employs the iteratively reweighted least squares (IRLS) algorithm, but in which the parameter p is judiciously chosen, by relating it to the characteristic exponent of the underlying alpha-stable distributed data. We demonstrate, through Monte Carlo simulations, that the optimal value of the parameter p is just below that of the characteristic exponent α, which we estimate from the data. Our reconstruction results show that the proposed algorithm outperforms previously proposed reconstruction techniques, both visually and in terms of two objective evaluation measures

JOINT RECONSTRUCTION OF COMPRESSIVELY SENSED ULTRASOUND RF ECHOES BY EXPLOITING TEMPORAL CORRELATIONS

In this paper, the principles of compressive sensing are exploited for the joint reconstruction of an ensemble of biomedical ultrasound RF echoes, using a highly reduced set of random measurements. Temporal correlations between the distinct RF echoes are taken into account during the reconstruction, which results in a reduction of the required number of measurements, while also increasing the reconstruction quality. The efficiency of recent state-of-the-art methods is evaluated on a set of real ultrasound data, to highlight the importance of accounting for temporal correlations during reconstruction. Our experimental evaluation reveals an improved performance, both visually and in terms of quality metrics, such as the SSIM and PSNR, when such correlations are extracted during the joint reconstruction of RF echoes, compared with previous methods based on the separate recovery of each RF echo. Index Terms — Compressive sensing, joint signal reconstruction, structured sparsity, ultrasound RF echoes. 1

Advanced Sensors for Real-Time Monitoring Applications

It is impossible to imagine the modern world without sensors, or without real-time information about almost everything—from local temperature to material composition and health parameters. We sense, measure, and process data and act accordingly all the time. In fact, real-time monitoring and information is key to a successful business, an assistant in life-saving decisions that healthcare professionals make, and a tool in research that could revolutionize the future. To ensure that sensors address the rapidly developing needs of various areas of our lives and activities, scientists, researchers, manufacturers, and end-users have established an efficient dialogue so that the newest technological achievements in all aspects of real-time sensing can be implemented for the benefit of the wider community. This book documents some of the results of such a dialogue and reports on advances in sensors and sensor systems for existing and emerging real-time monitoring applications

NASA university program management information system, FY 1994

The University Program report, Fiscal Year 1994, provides current information and related statistics for 7841 grants/contracts/cooperative agreements active during the reporting period. NASA field centers and certain Headquarters program offices provide funds for those activities in universities which contribute to the mission needs of that particular NASA element. This annual report is one means of documenting the NASA-university relationship, frequently denoted, collectively, as NASA's University Program