← Back to all news
arXiv Computer Vision By D. Hudson Smith, Ahmer Raza

Compression Hurts, Pooling Helps: Information Loss in Rayleigh-Scale Estimation from B-Mode Ultrasound

Read the original on arXiv Computer Vision →

The paper investigates how unknown log‑compression applied by clinical ultrasound devices degrades the estimation of the Rayleigh scale parameter σ, which governs diffuse speckle in B‑mode images. Using Fisher information analysis, it shows that finite‑offset log compression inflates the minimum achievable variance for unbiased σ estimation by a compression‑independent factor of about φ–MinInflation, and that even with multiple image windows the variance reduction is limited, requiring many windows to approach the ideal variance. The authors validate their theoretical predictions with synthetic experiments and demonstrate RF‑scale recovery on real RF‑envelope data from the OASBUD dataset, thereby highlighting the constraints of using routine B‑mode images for quantitative ultrasound analysis.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv Machine Learning
Sep 25

Physics-Guided Multi-Objective Deep Learning for Ultrasound RF Data Interpolation in Resource-Constrained Imaging

The paper introduces a physics‑guided, data‑driven framework for reconstructing dense ultrasound RF data from sparse acquisitions. It trains an end‑to‑end interpolation network with a hybrid RF‑ and beamforming‑domain loss, stabilized by exponential moving average, and employs random‑skip masking to generalize across varying sparsity patterns and channel configurations. On a held‑out test set, the method achieves a mean SSIM of about 0.95 across decimation factors from ×2 to ×13, consistently improving RF reconstruction and post‑beamforming image quality.

By Luoyuan Zhang, Yiyang You, Ananya Tandri, Yinan Feng, Hyunwoo Song, Jeeun Kang, Youzuo Lin
efficiency
More like this →
arXiv Computer Vision
Sep 25

Shadow Reduction in Ultrasound Imaging Using Differentiable Simulation and Radiance Field Decomposition

RFlash is a physics‑informed post‑processing technique that removes acoustic shadows in ultrasound by decomposing beamformed images into attenuation and scatter‑intensity maps using a differentiable radiance‑field model. It re‑renders images to eliminate depth‑dependent signal loss, effectively simulating a virtual transducer advance. Across thousands of fetal brain, abdominal, and liver scans, RFlash outperforms traditional attenuation correction, reduces prediction error by 40% in fetal brain imaging, and provides shadow‑confidence maps that enhance bone‑shadow segmentation.

By Valentin Bacher (Oxford Machine Learning in NeuroImaging Lab, University of Oxford, United Kingdom), Pak Hei Yeung (Oxford Machine Learning in NeuroImaging Lab, University of Oxford, United Kingdom, Quantitative Healthcare Analysis), Bernhard Kainz (Friedrich-Alexander-Universit\"at Erlangen-N\"urnberg, Germany, Imperial College London, United Kingdom), Madeleine K. Wyburd (Oxford Machine Learning in NeuroImaging Lab, University of Oxford, United Kingdom, Department of Computer Science, University of Copenhagen, Denmark), Nicola K. Dinsdale (Oxford Machine Learning in NeuroImaging Lab, University of Oxford, United Kingdom), Michael Gray (Institute of Biomedical Engineering, University of Oxford, United Kingdom), Ana I. L. Namburete (Oxford Machine Learning in NeuroImaging Lab, University of Oxford, United Kingdom)
computer-vision
More like this →