The quantitative imaging of attenuation coefficients slope (ACS) has the potential to improve medical diagnostics. However, attempts to characterize ACS using pulse-echo data have been limited by the large statistical variations in the estimates. Previous studies demonstrated that it is possible to extend the trade-off between variance and spatial resolution of quantitative ultrasound, spectral-based parameters by the use of full angular (i.e., 360•) spatial compounding (FASC). In the present work, the use of FASC has been extended to the estimation of ACS and its performance has been experimentally evaluated using two physical phantoms. The ACSs of the background and inclusion regions were estimated using insertion loss measurements to be 0.41 and 0.75 dB/cm/MHz for Phantom #1, and 0.54 and 1.04 dB/cm/MHz for Phantom #2, respectively. Pulseecho data were collected using a 7.5 MHz, f/4 transducer at 30 angles of view uniformly distributed between 0 and 360º. Single view ACS maps were generated using a spectral log difference method with 0.6 by 0.6 mm data blocks. The FASC images were constructed by assigning to a pixel the median of its corresponding estimates from all 30 angles of view. The reduction in the variance of the FASC estimates compared to the variance of estimates from a single view (i.e., variance averaged from the 30 single views) in the inclusion and background regions were 89.18% and 88.71% for Phantom #1 and 92.33% and 86.98% for Phantom #2. Moreover, in all the cases the estimation bias in the inclusion and background regions using FASC was lower than 9.0%. These results suggest that the variance of attenuation coefficient slope estimation can be significantly reduced without sacrificing spatial resolution by the use of full angular spatial compounding.

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Pediatric Pneumonia is one of the principal causes of death by year on children under the age of five worldwide. The diagnosis is commonly made by clinical criteria with support from imaging tools like radiography. Lung ultrasound has been considered a low-cost and portable alternative for pneumonia imaging; however, interpretation is subjective and requires adequate training. In the present work, a pneumonia detection algorithm based on the measurement of the fundamental bandwidth downshift over depth of ultrasound radiofrequency (RF) signals is presented. RF-data was obtained from lung ultrasound samples of children aged between six months and five years. Sampling was performed using a 6.6 MHz linear transducer. The sample consisted of 10 positive-and 10 negative-diagnosed RF cine-loops selected by a medical expert and captured in a local pediatric health institute. For each frame, several regions of interest were outlined starting from the pleural line. Corresponding functions for each RF-line of the maximum frequency decrement rate over depth from the fundamental spectra at a fixed bandwidth were estimated and linearly fitted. Finally, a descriptor function was build concatenating all fitted values from the RF-lines for each frame respectively. Each descriptor function was later thresholded to differentiate between healthy and pneumonic regions frame-wise. The optimal threshold was found to be 0.46 MHz/cm and was selected based on a receiver operator characteristic (ROC) curve analysis. The results revealed an accuracy rate higher than 90% on the sample.