GLUENet: Ultrasound Elastography Using Convolutional Neural Network
Displacement estimation is a critical step in ultrasound elastography and failing to estimate displacement correctly can result in large errors in 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 deformations. 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 but robust 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 exploiting all the information of all the samples of RF data simultaneously. The coarse displacement estimate generated by CNN is substantially more robust than the Dynamic Programming (DP) technique used in GLUE for finding the coarse displacement estimates. Our results validate that GLUENet outperforms GLUE in simulation, phantom and in-vivo experiments.
KeywordsConvolutional neural network Ultrasound elastography Time-delay estimation TDE Deep learning Global elastography
This research has been supported in part by NSERC Discovery Grant (RGPIN-2015-04136). We would like to thank Microsoft Azure Research for a cloud computing grant and NVIDIA for GPU donation. The ultrasound data was collected at Johns Hopkins Hospital. The principal investigators were Drs. E. Boctor, M. Choti, and G. Hager. We thank them for sharing the data with us.
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