Representing 3D Ultrasound with Neural Fields
Ang Nan Gu, Purang Abolmaesumi, Christina Luong, Kwang Moo Yi
3D Ultrasound (3D-US) is a powerful imaging modality, but the high storage requirement and low spatial resolution challenge wider adoption. Recent advancements in Neural Fields suggest a potential for efficient storage and construction of 3D-US data. In this work, we show how to effectively represent 3D-US data with Neural Fields, where we first learn the 2D slices of the 3D ultrasound data and expand to 3D. This two-stage representation learning improves the quality of 3D-US in terms of Peak Signal-to-Noise Ratio (PSNR) to 31.84dB from 28.7dB, a significant improvement directly noticeable to the human eye.
Wednesday 6th July
Poster Session 1.2 - onsite 11:00 - 12:00, virtual 15:20 - 16:20 (UTC+2)