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15 March 2019 Deep 3D convolutional neural networks for fast super-resolution ultrasound imaging
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Abstract
Super-resolution ultrasound imaging (SR-US) is a new technique which breaks the diffraction limit and can help visualize microvascularity at a resolution of tens of microns. However, image processing methods for spatiotemporal filtering needed in SR-US for microvascular delineation, such as singular value decomposition (SVD), are computationally burdensome and must be performed off-line. The goal of this study was to evaluate a novel and fast method for spatiotemporal filtering to segment the microbubble (MB) contrast agent from the tissue signal with a trained 3D convolutional neural network (3DCNN). In vitro data was collected using a programmable ultrasound (US) imaging system (Vantage 256, Verasonics Inc, Kirkland, WA) equipped with an L11-4v linear array transducer and obtained from a tissue-mimicking vascular flow phantom at flow rates representative of microvascular conditions. SVD was used to detect MBs and label the data for training. Network performance was validated with a leave-one-out approach. The 3DCNN demonstrated a 22% higher sensitivity in MB detection than SVD on in vitro data. Further, in vivo 3DCNN results from a cancer-bearing murine model revealed a high level of detail in the SR-US image demonstrating the potential for transfer learning from a neural network trained with in vitro data. The preliminary performance of segmentation with the 3DCNN was encouraging for real-time SR-US imaging with computation time as low as 5 ms per frame.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Katherine Brown, James Dormer, Baowei Fei, and Kenneth Hoyt "Deep 3D convolutional neural networks for fast super-resolution ultrasound imaging", Proc. SPIE 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography, 1095502 (15 March 2019); https://doi.org/10.1117/12.2511897
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