arXiv subjects

Yangmo Yoo

Publications and source records attributed to Yangmo Yoo.

5 recordsLinked to original sources

arXiv2026

Smart Spectral Doppler Ultrasound Imaging using Motion Tracking based on Wide-beam transmission and Neural Networks: Preliminary Results

Ultrasound spectral Doppler imaging provides quantitative blood flow information for vascular diagnosis, but its accuracy is limited by the one-dimensional (1D) sample volume (SV). Motion from respiration, patient movement, and probe manipulation can move the target vessel out of the SV, causing spectral broadening, signal reduction, or loss of the spectrum. A two-dimensional (2D) SV can address this, but sequential focused-beam transmission reduces the pulse-repetition frequency (PRF), and plane-wave transmission lowers the signal-to-noise ratio (SNR). In this study, we propose a wide-beam transmission method that acquires a 2D SV from a single transmit event while preserving the PRF. A Hamming network, which requires no prior training, tracks axial and lateral motion within the 2D SV and updates the position of an adaptive 1D SV for each frame. Field II simulations showed that the wide-beam transmission had a spectral SNR comparable to that of focused-beam transmission and approximately 10 dB higher than that of plane-wave transmission. The motion tracking correctly detected the target position in over 93% of frames for vessel radii greater than 1 mm, and in 100% of frames for radii greater than 2 mm. In vivo studies of a carotid artery (linear array) and a hepatic vein (convex array) showed that the proposed method recovered obscured spectra and increased the spectral SNR by 9.3 dB and 4.5 dB, respectively. These results demonstrate that the proposed wide-beam transmission with Hamming network-based tracking can improve the accuracy of spectral Doppler imaging under tissue motion. Moreover, we anticipate that the proposed approach can be implemented in real-time due to its single transmission and simple two-layer architecture with low computational complexity.

physics.med-ph↗

arXiv2026

pCoRev: Physics-Informed Contrastive Learning for Unsupervised Reverberation Artifact Suppression in Ultrafast Plane-Wave Imaging

Reverberation artifact suppression in ultrafast plane-wave (PW) imaging remains challenging because artifact-free reference data are generally unavailable and multipath echoes often resemble tissue signals. Coherent plane-wave compounding (CPWC) attenuates reverberation only partially and sacrifices the frame rate that motivates PW imaging. We propose pCoRev, an unsupervised physics-informed contrastive learning framework that leverages the distinct angular coherence characteristics of tissue echoes and reverberation artifacts. A single-angle I/Q frame is the network input, and the remaining angles of the same CPWC acquisition provide angle-consistent positive pairs, residual-derived negative pairs, and coherence-guided self-supervision, so no paired artifact-free data are required. On a fabricated reverberation phantom with 31 validation acquisitions, pCoRev achieved the best contrast ratio (CR), contrast-to-noise ratio (CNR), and generalized CNR (gCNR) of 38.76 +/- 5.40 dB, 11.75 +/- 1.81 dB, and 0.932 +/- 0.051 against delay-and-sum (DAS), delay-multiply-and-sum, a supervised 3D convolutional neural network, and unsupervised deep coherence learning. Because the network is trained on single-angle inputs, it also operates on a single PW transmission: with one angle, pCoRev reached a CR of 32.10 +/- 7.79 dB and a CNR of 10.39 +/- 3.02 dB, exceeding 75-angle compounded DAS (20.45 +/- 3.84 dB and 8.38 +/- 2.00 dB) with 75-fold fewer transmissions. On four clinical acquisitions from other scanners, pCoRev improved CR over DAS by 9 to 21 dB without fine-tuning. These results indicate that physics-informed unsupervised contrastive learning suppresses reverberation effectively while preserving the frame-rate advantage of ultrafast PW imaging.

eess.SP↗

arXiv2026

MC-GenRef: Annotation-free mammography microcalcification segmentation with generative posterior refinement

Microcalcification (MC) analysis is clinically important in screening mammography because clustered puncta can be an early sign of malignancy, yet dense MC segmentation remains challenging: targets are extremely small and sparse, dense pixel-level labels are expensive and ambiguous, and cross-site shift often induces texture-driven false positives and missed puncta in dense tissue. We propose MC-GenRef, a real dense-label-free framework that combines high-fidelity synthetic supervision with test-time generative posterior refinement (TT-GPR). During training, real negative mammogram patches are used as backgrounds, and physically plausible MC patterns are injected through a lightweight image formation model with local contrast modulation and blur, yielding exact image-mask pairs without real dense annotation. Using only these synthetic labeled pairs, MC-GenRef trains a base segmentor and a seed-conditioned rectified-flow (RF) generator that serves as a controllable generative prior. During inference, TT-GPR treats segmentation as approximate posterior inference: it derives a sparse seed from the current prediction, forms seed-consistent RF projections, converts them into case-specific surrogate targets through the frozen segmentor, and iteratively refines the logits with overlap-consistent and edge-aware regularization. On INbreast, the synthetic-only initializer achieved the best Dice without real dense annotations, while TT-GPR improved miss-sensitive performance to Recall and FNR, with strong class-balanced behavior (Bal.Acc., G-Mean). On an external private Yonsei cohort ( n=50 ), TT-GPR consistently improved the synthetic-only initializer under cross-site shift, increasing Dice and Recall while reducing FNR. These results suggest that test-time generative posterior refinement is a practical route to reduce MC misses and improve robustness without additional real dense labeling.

eess.IV↗

arXiv2025

EdgeSRIE: A hybrid deep learning framework for real-time speckle reduction and image enhancement on portable ultrasound systems

Speckle patterns in ultrasound images often obscure anatomical details, leading to diagnostic uncertainty. Recently, various deep learning (DL)-based techniques have been introduced to effectively suppress speckle; however, their high computational costs pose challenges for low-resource devices, such as portable ultrasound systems. To address this issue, EdgeSRIE, which is a lightweight hybrid DL framework for real-time speckle reduction and image enhancement in portable ultrasound imaging, is introduced. The proposed framework consists of two main branches: an unsupervised despeckling branch, which is trained by minimizing a loss function between speckled images, and a deblurring branch, which restores blurred images to sharp images. For hardware implementation, the trained network is quantized to 8-bit integer precision and deployed on a low-resource system-on-chip (SoC) with limited power consumption. In the performance evaluation with phantom and in vivo analyses, EdgeSRIE achieved the highest contrast-to-noise ratio (CNR) and average gradient magnitude (AGM) compared with the other baselines (different 2-rule-based methods and other 4-DL-based methods). Furthermore, EdgeSRIE enabled real-time inference at over 60 frames per second while satisfying computational requirements (< 20K parameters) on actual portable ultrasound hardware. These results demonstrated the feasibility of EdgeSRIE for real-time, high-quality ultrasound imaging in resource-limited environments.

eess.IV↗

arXiv2023

Deep Coherence Learning: An Unsupervised Deep Beamformer for High Quality Single Plane Wave Imaging in Medical Ultrasound

Plane wave imaging (PWI) in medical ultrasound is becoming an important reconstruction method with high frame rates and new clinical applications. Recently, single PWI based on deep learning (DL) has been studied to overcome lowered frame rates of traditional PWI with multiple PW transmissions. However, due to the lack of appropriate ground truth images, DL-based PWI still remains challenging for performance improvements. To address this issue, in this paper, we propose a new unsupervised learning approach, i.e., deep coherence learning (DCL)-based DL beamformer (DL-DCL), for high-quality single PWI. In DL-DCL, the DL network is trained to predict highly correlated signals with a unique loss function from a set of PW data, and the trained DL model encourages high-quality PWI from low-quality single PW data. In addition, the DL-DCL framework based on complex baseband signals enables a universal beamformer. To assess the performance of DL-DCL, simulation, phantom and in vivo studies were conducted with public datasets, and it was compared with traditional beamformers (i.e., DAS with 75-PWs and DMAS with 1-PW) and other DL-based methods (i.e., supervised learning approach with 1-PW and generative adversarial network (GAN) with 1-PW). From the experiments, the proposed DL-DCL showed comparable results with DMAS with 1-PW and DAS with 75-PWs in spatial resolution, and it outperformed all comparison methods in contrast resolution. These results demonstrated that the proposed unsupervised learning approach can address the inherent limitations of traditional PWIs based on DL, and it also showed great potential in clinical settings with minimal artifacts.

eess.IV↗