Learning Continuous Receive Apodization Weights via Implicit Neural Representation for Ultrafast ICE Ultrasound Imaging

Ultrafast intracardiac echocardiography (ICE) uses unfocused transmissions to capture cardiac motion at frame rates exceeding 1 kHz. While this enables real-time visualization of rapid dynamics, image quality is often degraded by diffraction artifacts, requiring many transmits to achieve satisfying resolution and contrast. To address this limitation, we propose an implicit neural representation (INR) framework to encode complex-valued receive apodization weights in a continuous manner, enabling high-quality ICE reconstructions from only three diverging wave (DW) transmits. Our method employs a multi-layer perceptron that maps pixel coordinates and transmit steering angles to complex-valued apodization weights for each receive channel. Experiments on a large in vivo porcine ICE imaging dataset show that the learned apodization suppresses clutter and enhances contrast, yielding reconstructions closely matching 26-angle compounded DW ground truths. Our study suggests that INRs could offer a powerful framework for ultrasound image enhancement.