Searcharxiv⌕ Search

arXiv · 2611.09002

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

Abstract

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.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jihye Baek, Allison Lee, Jinbum Kang, Yangmo Yoo. 2026-11-06. Smart Spectral Doppler Ultrasound Imaging using Motion Tracking based on Wide-beam transmission and Neural Networks: Preliminary Results. https://arxiv.org/abs/2611.09002

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

KEEP EXPLORING

Related papers

Dual-energy cardiac DSA imaging using a dual-layer flat-panel detector: in vivo validation study

Background: The temporal misregistration in conventional X-ray digital subtraction angiography (DSA) remains a key challenge for its practical application in cardiac catheterization, leading to incomplete separation of cardiac vessels from background anatomical structures. Purpose: In this study, the X-ray spectral information is unveiled for DSA imaging to generate better visualization of cardiac vessels with negligible anatomical background. Methods: A dedicated dual-energy DSA imaging model is proposed based on the subtraction residuals between pre-contrast and post-contrast spectral projections, enabling accurate material decomposition without requiring precise scatter correction. Specifically, a dual-layer flat-panel detector (DL-FPD) is assumed to acquire the needed temporally registered low-energy and high-energy projections. Moreover, a penalized likelihood material decomposition algorithm is proposed to enhance the material decomposition performance. To validate, in vivo swine experiments were conducted for the first time on a clinical DSA system equipped with a DL-FPD. Results: Monte Carlo simulation results demonstrated that the impact of scatter on the subtraction residuals can be reduced by more than 80%, substantially improving material decomposition accuracy. Compared with analytical material decomposition, the penalized-likelihood material decomposition algorithm significantly improves image contrast-to-noise ratio (CNR), enabling the visualization of small vessels and even the myocardial blush effect. Furthermore, compared with conventional dual-energy subtraction, the penalized-likelihood approach provides more quantitative material maps with fewer overlapping anatomical background artifacts. Conclusion: This preliminary study verifies that dual-energy DSA imaging method is able to generate high-quality cardiovascular imaging results.

physics.med-ph↗

SmartBloch-Agent: An Agentic Framework for Automated MRI Sequence Design and Optimization

Magnetic resonance imaging (MRI) pulse sequences organize radiofrequency excitation, gradient encoding, and signal acquisition, determining magnetization evolution and image formation. Sequence research requires translating imaging objectives into executable protocols meeting physical and acquisition constraints, refined via physical simulation, signal reconstruction, and scientific evaluation. Sequence construction, physical verification, parameter search, and assessment involve many tools and manual decisions, limiting automation from objective to verifiable sequence. We develop SmartBloch-Agent, an integrated framework for automated MRI sequence design and optimization. Given objectives and scientific constraints in natural language, an AI agent interprets goals, plans tasks, orchestrates tools, and makes feedback-informed decisions, drawing on a construction module and optimizers for structural design and parameter search. Our GPU-accelerated SmartBloch engine supplies physical computation for both stages, so the framework configures, per sequence and objective, event validation, Full-Bloch magnetization evolution, signal acquisition, image reconstruction, and scientific evaluation, and closes the loop without external MRI simulators. Results return to the agent to guide sequence revision, parameter optimization, and candidate selection, and the design files carry simulation, reconstruction, and evaluation evidence, forming an output package for a real-scanner workflow. Experiments show the framework automatically designs and optimizes MRI sequences for varied objectives and constraints, refines candidates with physical feedback, and improves several parameter-estimation metrics and downstream quantitative reconstruction. SmartBloch-Agent unifies objectives, design, physical execution, and evaluation into a continuous workflow for end-to-end automation of MRI sequence research.

physics.med-ph↗

Evaluation of Image Noise from Computed Tomography in an Organ-Specific Anthropomorphic Phantom

Computed tomography (CT) is used ubiquitously for diagnostic imaging, with novel iterative reconstructions such as hybrid iterative reconstruction with partial model-based component (ASiR-V) and deep learning image reconstruction (DLIR) increasingly utilized. In conjunction, CT vendors have developed a noise index (NI) to reduce radiation dose during protocol development. However, it is unclear how these reconstruction algorithms reduce noise as NI changes, particularly across multiple organs. The goal of this study was to utilize an anthropomorphic phantom to evaluate image noise against the prescribed NI across different organs, determine if DLIR reduces image noise compared to ASiR-V, and use this relationship to determine the maximum volume CT dose index (CTDIvol) reduction at equivalent noise levels. Repeat images were acquired on a GE HealthCare Revolution Apex CT scanner across NI values in an anthropomorphic adult-sized phantom from Kyoto Kagaku with ASiR-V and DLIR. Organs-of-interest were contoured using TotalSegmentator in 3D Slicer. The correlation between the CTDIvol and NI was determined. The standard deviation of the difference in the repeat images was used as a noise metric (NM), correlated with NI, used to determine the NI at equivalent NM between ASiR-V and DLIR, then applied to determine the dose reduction and difference in NM. The relationship between NI and the resulting CTDIvol was found to be proportional to a power law of exponent -2.23. The maximum potential absolute dose reduction from using DLIR was found in the liver at 1.3 mGy with an NI of 7.9. Across all organs, the relative dose reduction increased, the NM-to-NI ratio decreased, and the noise reduction increased across NI. The relationship between image noise and the prescribed NI differed between ASiR-V and DLIR and varied by organ, with model-based dose reductions of up to 1.3 mGy in the liver.

physics.med-ph↗