Global Ultrasound Elastography Using Convolutional Neural Network

Displacement estimation is very important in ultrasound elastography and failing to estimate displacement correctly results in failure in generating strain images. As conventional ultrasound elastography techniques suffer from decorrelation noise, they are prone to fail in estimating displacement between echo signals obtained during tissue distortions. This study proposes a novel elastography technique which addresses the decorrelation in estimating displacement field. We call our method GLUENet (GLobal Ultrasound Elastography Network) which uses deep Convolutional Neural Network (CNN) to get a coarse time-delay estimation between two ultrasound images. This displacement is later used for formulating a nonlinear cost function which incorporates similarity of RF data intensity and prior information of estimated displacement. By optimizing this cost function, we calculate the finer displacement by exploiting all the information of all the samples of RF data simultaneously. The Contrast to Noise Ratio (CNR) and Signal to Noise Ratio (SNR) of the strain images from our technique is very much close to that of strain images from GLUE. While most elastography algorithms are sensitive to parameter tuning, our robust algorithm is substantially less sensitive to parameter tuning.Comment: 4 pages, 4 figures; added acknowledgment section, submission type late

Microstructural evaluation with type i hot corrosion degradation of gas turbine alloys during burner-rig testing

The hot corrosion resistance of selected gas turbine alloys was evaluated, as a baseline for assessing candidate new hot-section materials. The alloys were tested under burner rig exposures, using ASTM standard seawater for the salt contaminant and combustion conditions that provide representative materials evolution and degradation behavior relative to what is observed with marine gas turbines under service environments. Modern characterization techniques were utilized to evaluate the hot corrosion behavior and resistance of the evaluated material systems, to observe the degradation of the alloys and to study the underlying degradation mechanisms active during hot corrosion attack. Please click Additional Files below to see the full abstract