Compact Implicit Neural Representations for Plane Wave Images

TL;DR

This paper introduces a novel use of Implicit Neural Representations to efficiently encode and interpolate plane wave ultrasound images, reducing storage needs while maintaining image quality.

Contribution

It is the first to apply INRs for PW angular interpolation, offering a compact, physics-enhanced neural model for ultrasound imaging.

Findings

Achieves a 15:1 compression ratio compared to raw images.

Maintains high image quality with SSIM and PSNR metrics.

Demonstrates effective angular interpolation of PW images.

Abstract

Ultrafast Plane-Wave (PW) imaging often produces artifacts and shadows that vary with insonification angles. We propose a novel approach using Implicit Neural Representations (INRs) to compactly encode multi-planar sequences while preserving crucial orientation-dependent information. To our knowledge, this is the first application of INRs for PW angular interpolation. Our method employs a Multi-Layer Perceptron (MLP)-based model with a concise physics-enhanced rendering technique. Quantitative evaluations using SSIM, PSNR, and standard ultrasound metrics, along with qualitative visual assessments, confirm the effectiveness of our approach. Additionally, our method demonstrates significant storage efficiency, with model weights requiring 530 KB compared to 8 MB for directly storing the 75 PW images, achieving a notable compression ratio of approximately 15:1.