Deep 3D convolutional neural networks for fast super-resolution ultrasound imaging

Katherine Brown; James Dormer; Baowei Fei; Kenneth Hoyt

doi:10.1117/12.2511897

15 March 2019 Deep 3D convolutional neural networks for fast super-resolution ultrasound imaging

Katherine Brown, James Dormer, Baowei Fei, Kenneth Hoyt

Proceedings Volume 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography; 1095502 (2019) https://doi.org/10.1117/12.2511897
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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

Citation Download Citation

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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