{"ID":2846376,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.02938","arxiv_id":"2511.02938","title":"From Narrow to Wide: Autoencoding Transformers for Ultrasound Bandwidth Recovery","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.","short_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...","url_abs":"https://arxiv.org/abs/2511.02938","url_pdf":"https://arxiv.org/pdf/2511.02938v1","authors":"[\"Sepideh KhakzadGharamaleki\",\"Hassan Rivaz\",\"Brandon Helfield\"]","published":"2025-11-04T19:34:18Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.AI\"]","methods":"[\"Transformer\"]","has_code":false}