Deep Sylvester Posterior Inference for Adaptive Compressed Sensing in Ultrasound Imaging

Published: Jan. 13, 2025

Ultrasound images are commonly formed by sequential acquisition of beam-steered scan-lines. Minimizing the number required scan-lines can significantly enhance frame rate, field view, energy efficiency, and data transfer speeds. Existing approaches typically use static subsampling schemes in combination with sparsity-based or, more recently, deep-learning-based recovery. In this work, we introduce an _adaptive_ method that maximizes intrinsic information gain _in-situ_, employing a Sylvester Normalizing Flow encoder to infer approximate Bayesian posterior under partial observation real-time. Using deep generative model for future observations, determine scheme mutual between subsampled next video. We evaluate our approach using EchoNet cardiac ultrasound video dataset demonstrate active sampling outperforms competitive baselines, including uniform variable-density random sampling, as well equidistantly spaced scan-lines, improving mean absolute reconstruction error 15%. Moreover, inference generation performed just 0.015 seconds (66Hz), making it fast enough real-time 2D imaging applications.

Language: Английский

Ultrasound Image Enhancement with the Variance of Diffusion Models

Published: Sept. 22, 2024

Language: Английский