Search arXivSearch

arXiv · 2512.14094

Synthetic Aperture for High Spatial Resolution Acoustoelectric Imaging

Abstract

Acoustoelectric (AE) imaging provides electro-anatomical contrast by mapping the distribution of electric fields in biological tissues, by delivering ultrasound waves which spatially modulate the medium resistivity via the AE effect. The conventional method in AE imaging is to transmit focused ultrasound (FUS) beams; however, the depth-of-field (DOF) of FUS-AE is limited to the size of the focal spot, which does not span across the centimeter-scale of organs. Instead of fixing the focal depth on transmission, we propose to dynamically synthesize the AE modulation regions via a Synthetic Aperture approach (SA-AE). SA-AE involves a straightforward pixel-based delay-and-sum reconstruction of AE images from unfocused AE signals. In saline and ex vivo lobster nerve experiments, FUS-AE was shown to perform well only at the focal depth, with poor spatial resolution for out-of-focus electric sources. Meanwhile, SA-AE generally improved spatial resolution throughout the DOF, but introduced strong background noise. The flexibility of uncoupled, single-element induced AE signals in SA-AE was further leveraged to quantify their spatial coherence across the transmit aperture, obtaining maps of the coherence factor (CF) and pulse-length coherence factor (CFPL). Weighting SA-AE images with their derived CF and CFPL maps resulted in further improvement in image resolution and contrast, and notably, boosted the image SNR beyond that of FUS-AE. CFPL exhibited stronger noise suppression over CF. Using unfocused wave transmissions, the proposed coherence-weighted SA-AE strategy offers a high resolution yet noise-robust solution towards the practical imaging of fast biological currents.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wei Yi Oon, Yuchen Tang, Baiqian Qi, Wei-Ning Lee. 2025-12-16. Synthetic Aperture for High Spatial Resolution Acoustoelectric Imaging. https://arxiv.org/abs/2512.14094

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

LaminoDiff: Generative Computed Laminography via Near-Isotropic Spectral Supervision and Anisotropic Geometry

Computed Laminography (CL) is widely used for nondestructive inspection of extended planar objects, but its restricted angular coverage produces an anisotropic point spread function, missing-cone spectral incompleteness, and severe aliasing with interlayer leakage. This paper presents LaminoDiff, a physics-constrained diffusion framework for CL reconstruction. During training, a CT-derived near-isotropic supervision target is reconstructed from full-angle Computed Tomography (CT) projections physically degraded to match CL detector noise and focal-spot blur while retaining full angular coverage; it is withheld from inference. At inference, the reverse process uses only the CL observation. An Anisotropic Representation (AR) constructs depth-aware channels from three adjacent slices: a neighbor average, a center-neighbor residual, and the retained current slice, followed by directional in-plane feature extraction. Experiments on simulated and real multilayer printed circuit board data, including ball grid array and high-frequency stub samples, show that LaminoDiff improves artifact suppression, edge preservation, and depth stratification over analytic Feldkamp--Davis--Kress and representative learning-based baselines.

eess.IV

IViT: A Novel Interpretable Visual Transformer for Skin Disease Detection

The clinical diagnosis of skin diseases is susceptible to interference from inter-class similarity of skin lesions, and over-reliance on clinicians'experience easily leads to subjective bias. Although existing deep learning aided diagnosis methods achieve competitive accuracy, they suffer from the black-box opacity of Vision Transformer (ViT) and poor adaptability to medical few-shot scenarios. Moreover, mainstream explainable algorithms generally face the bottleneck of significant accuracy degradation when improving interpretability. This paper proposes an interpretable ViT (IViT) constrained by Quadratic Programming (QP). The introduced pre-trained transfer learning adapts to few-shot feature extraction. A discrete QP feature selection framework is constructed to screen generic and discriminative features consistent with clinical diagnostic logic. A multi-objective loss function is designed to reduce feature redundancy and optimize activation distribution while preserving classification performance. Experimental results on six standard skin disease datasets show that IViT achieves an accuracy of 93.80%, only 0.21% lower than the baseline, with feature redundancy reduced by 29.5%. Its core activation regions are consistent with clinically concerned lesion areas. The proposed model balances accuracy and interpretability, providing a reliable solution for the clinical deployment of few-shot intelligent skin disease diagnosis.

eess.IV

Automated Distinction of Intimal and Medial Intracranial Arterial Calcification from CT Head

Intracranial arterial calcifications (IACs) are a common finding on clinical non-contrast enhanced head CT scans and are associated with neurovascular disease. Calcifications can occur in the intimal or medial layer of the arterial wall, subtypes that differ in aetiology and may have distinct clinical relevance. These subtypes can be visually distinguished by radiologists based on the shape of the calcifications. We investigate three automated approaches for subtype classification of IAC from head CT-derived segmentation masks: (1) an automated adaptation of the established radiological visual score, (2) a sphericity-based method, and (3) a method based on shape embeddings extracted by a medical shape foundation model. All approaches use the same lightweight classification pipeline on top of the features they compute and are evaluated using 5-fold cross-validation. The three methods achieved comparable performance, with the embedding-based approach yielding the best overall results with a weighted F1 (mean $\pm$ SD) of up to 71.5 $\pm$ 3.7 for a single artery and 59.8 $\pm$ 1.7 for the joint artery classification. Performance was largely preserved when using automated instead of manual IAC segmentation masks, and we found the difference in weighted F1 not significant. Our results show that fully automated IAC subtype quantification from head CT is feasible and remains robust to the use of manual and automated IAC segmentation masks. Code at https://github.com/bjin96/iac-subtyping.

eess.IV