Extended aperture image reconstruction for plane-wave imaging.

B-mode images undergo degradation in the boundary region because of the limited number of elements in the ultrasound probe. Herein, a deep learning-based extended aperture image reconstruction method is proposed to reconstruct a B-mode image with an enhanced boundary region. The proposed network can reconstruct an image using pre-beamformed raw data received from the half-aperture of the probe.

Vascular wall motion detection models based on long short-term memory in plane-wave-based ultrasound imaging.

Vascular wall motion can be used to diagnose cardiovascular diseases. In this study, long short-term memory (LSTM) neural networks were used to track vascular wall motion in plane-wave-based ultrasound imaging.The proposed LSTM and convolutional LSTM (ConvLSTM) models were trained using ultrasound data from simulations and tested experimentally using a tissue-mimicking vascular phantom and anstudy using a carotid artery.

Semantic Segmentation of Pancreatic Cancer in Endoscopic Ultrasound Images Using Deep Learning Approach.

Endoscopic ultrasonography (EUS) plays an important role in diagnosing pancreatic cancer. Surgical therapy is critical to pancreatic cancer survival and can be planned properly, with the characteristics of the target cancer determined. The physical characteristics of the pancreatic cancer, such as size, location, and shape, can be determined by semantic segmentation of EUS images.

Reconstruction for plane-wave ultrasound imaging using modified U-Net-based beamformer.

An image reconstruction method that can simultaneously provide high image quality and frame rate is necessary for diagnosis on cardiovascular imaging but is challenging for plane-wave ultrasound imaging. To overcome this challenge, an end-to-end ultrasound image reconstruction method is proposed for reconstructing a high-resolution B-mode image from radio frequency (RF) data. A modified U-Net architecture that adopts EfficientNet-B5 and U-Net as the encoder and decoder parts, respectively, is proposed as a deep learning beamformer.

Deep Learning-Based Differentiation between Mucinous Cystic Neoplasm and Serous Cystic Neoplasm in the Pancreas Using Endoscopic Ultrasonography.

Mucinous cystic neoplasms (MCN) and serous cystic neoplasms (SCN) account for a large portion of solitary pancreatic cystic neoplasms (PCN). In this study we implemented a convolutional neural network (CNN) model using ResNet50 to differentiate between MCN and SCN. The training data were collected retrospectively from 59 MCN and 49 SCN patients from two different hospitals.