From Narrow to Wide: Autoencoding Transformers for Ultrasound Bandwidth Recovery

📝 Original Info

• Title: From Narrow to Wide: Autoencoding Transformers for Ultrasound Bandwidth Recovery
• ArXiv ID: 2511.02938
• Date: 2025-11-04
• Authors: ** 논문에 명시된 저자 정보가 제공되지 않았습니다. (정보 없음) **

📝 Abstract

Conventional pulse-echo ultrasound suffers when low-cost probes deliver only narrow fractional bandwidths, elongating pulses and erasing high-frequency detail. We address this limitation by learning a data-driven mapping from band-limited to broadband spectrogram of radio-frequency (RF) lines. To this end, a variation of Tiny Vision Transform (ViT) auto-encoder is trained on simulation data using a curriculum-weighted loss. On heterogeneous speckle-cyst phantoms, the network reduces image-domain MSE by 90 percent, boosts PSNR by 6.7 dB, and raises SSIM to 0.965 compared with the narrow-band input. It also sharpens point-target rows in a completely unseen resolution phantom, demonstrating strong out-of-distribution generalisation without sacrificing frame rate or phase information. These results indicate that a purely software upgrade can endow installed narrow-band probes with broadband-like performance, potentially widening access to high-resolution ultrasound in resource-constrained settings.

💡 Deep Analysis

📄 Full Content

Reference