Efficient Aberration Correction via Optimal Bulk Speed of Sound Compensation

Diagnostic ultrasound is a versatile and practical tool in the abdomen, and is particularly vital toward the detection and mitigation of early-stage non-alcoholic fatty liver disease (NAFLD). However, its performance in those with obesity -- who are at increased risk for NAFLD -- is degraded due to distortions of the ultrasound as it traverses thicker, acoustically heterogeneous body walls (aberration). Here, we assess the capability of a bulk speed of sound correction in receive beamforming to correct aberration, and improve the resulting images. We find that a bulk correction may approximate the aberration profile for layers or relevant thicknesses (1 to 3 cm) and speeds of sound (1400 to 1500 m/s). Additionally, through in vitro experiments, we show significant improvement in resolution (average point target width reduced by 60 %) with bulk speed of sound correction determined automatically from the beamformed images. Together, these results demonstrate the utility of simple, efficient bulk speed of sound correction to improve the quality of diagnostic liver images.

Weakly Supervised Context Encoder using DICOM metadata in Ultrasound Imaging

Modern deep learning algorithms geared towards clinical adaption rely on a significant amount of high fidelity labeled data. Low-resource settings pose challenges like acquiring high fidelity data and becomes the bottleneck for developing artificial intelligence applications. Ultrasound images, stored in Digital Imaging and Communication in Medicine (DICOM) format, have additional metadata data corresponding to ultrasound image parameters and medical exams. In this work, we leverage DICOM metadata from ultrasound images to help learn representations of the ultrasound image. We demonstrate that the proposed method outperforms the non-metadata based approaches across different downstream tasks.