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KMID : 1137820230440040255
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의공학회지
2023 Volume.44 No. 4 p.255 ~ p.263
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3D Ultrasound Panoramic Image Reconstruction using Deep Learning
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Lee Si-Yeoul
Kim Seon-Ho
Lee Dong-Eon
Park Chun-Su
Kim Min-Woo
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Abstract
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Clinical ultrasound (US) is a widely used imaging modality with various clinical applications. However, cap- turing a large field of view often requires specialized transducers which have limitations for specific clinical scenarios.
Panoramic imaging offers an alternative approach by sequentially aligning image sections acquired from freehand sweeps using a standard transducer. To reconstruct a 3D volume from these 2D sections, an external device can be employed to track the transducer’s motion accurately. However, the presence of optical or electrical interferences in a clinical setting often leads to incorrect measurements from such sensors. In this paper, we propose a deep learn- ing (DL) framework that enables the prediction of scan trajectories using only US data, eliminating the need for an external tracking device. Our approach incorporates diverse data types, including correlation volume, optical flow, B-mode images, and rawer data (IQ data). We develop a DL network capable of effectively handling these data types and introduce an attention technique to emphasize crucial local areas for precise trajectory prediction. Through exten- sive experimentation, we demonstrate the superiority of our proposed method over other DL-based approaches in terms of long trajectory prediction performance. Our findings highlight the potential of employing DL techniques for trajectory estimation in clinical ultrasound, offering a promising alternative for panoramic imaging.
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KEYWORD
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Ultrasound, Panoramic, 3D imaging, Deep learning
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