arXiv AI
Coherence-Guided Pyramid Self-Contrastive Disentanglement for Ultrasound Imaging via Single-Shot Test-Time Training
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Pyramid Self-Contrastive Learning for Single-shot Test-time Ultrasound Image Denoising
arXiv:2605. 12567v2 Announce Type: replace-cross Abstract: The inherent electronic and speckle noise complicates clinical interpretation of ultrasound images.
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Jul 27
IQ-JEPA: A Joint-Embedding Predictive Architecture with a Hermitian Vision Transformer for Sound Speed and Attenuation Estimation from Ultrasound IQ Data
arXiv:2607. 22351v1 Announce Type: new Abstract: The speed of sound in tissue is a prerequisite for well-focused imaging and has diagnostic value, but recovering it from raw pulse-echo channel data is fundamentally a nonlinear inverse problem.
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Null-Space Diffusion Restoration with Adaptive Uncertainty-Guided Fusion for Ultrasound Speckle Reduction
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arXiv Machine Learning
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Few-shot Deep Learning for Phase-Amplitude Aberration Correction in Transcranial Focused Ultrasound
arXiv:2607. 29182v1 Announce Type: cross Abstract: Transcranial focused ultrasound (tFUS) is a non-invasive technique that delivers focused acoustic energy through the skull for neuromodulation and therapeutic applications.
arXiv Computer Vision
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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.
arXiv AI
Jul 23
PRIME-SVR: Physics-infoRmed Implicit Multi-Echo Slice-to-Volume Reconstruction for Fetal T2 mapping
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