arXiv subjects

Kate M. Knoll

Publications and source records attributed to Kate M. Knoll.

4 recordsLinked to original sources

arXiv2026

Reducing Spectral Oscillations for Robust Reference Frequency-Based Ultrasound Attenuation Estimation in Harmonic Imaging

Ultrasound attenuation coefficient estimation (ACE) has emerged as a quantitative imaging biomarker for noninvasive assessment of hepatic steatosis. A system-independent technique based on spectral normalization, known as the reference frequency method (RFM), was previously proposed to estimate ACE without requiring a well-calibrated reference phantom. Furthermore, incorporating harmonic imaging can significantly suppress reverberation signals. In previous clinical study, RFM has achieved high correlation with MRI-PDFF, demonstrating its potential for clinical application. However, a major challenge of RFM is the presence of oscillations in the frequency power-ratio decay curves (FPDCs), which can distort the linear fitting used to estimate the attenuation coefficient and consequently degrade ACE accuracy. These oscillations arise from constructive and destructive interference among backscattered echoes, resulting in oscillatory fluctuations in the measured power spectrum that propagate into the FPDCs. We propose a transmission scheme combining multiple frequencies and steering angles to mitigate oscillations in the FPDCs. Averaging these FPDCs suppresses the interference-induced oscillations while preserving the attenuation-dependent decay trend, thereby improving linearity and the accuracy of ACE results. In in-vitro experiments using calibrated phantoms (0.5 and 0.76 dB/cm/MHz) demonstrated that the proposed method improved FPDC linearity and ACE accuracy, achieving an R2 of 0.99 and attenuation coefficient estimates of 0.51 and 0.77 dB/cm/MHz, versus an R2 of 0.89 and estimates of 0.56 and 0.70 dB/cm/MHz for conventional RFM. The proposed method also demonstrated superior performance in a pilot patient study (n=15), achieving a stronger correlation with MRI-PDFF (R = 0.89 vs. 0.83) while reducing inter-measurement variability, indicating improved robustness and clinical potential.

physics.med-ph↗

arXiv2025

Contrast-Free Ultrasound Microvascular Imaging via Radiality and Similarity Weighting

Microvascular imaging has advanced significantly with ultrafast data acquisition and improved clutter filtering, enhancing the sensitivity of power Doppler imaging to small vessels. However, the image quality remains limited by spatial resolution and elevated background noise, both of which impede visualization and accurate quantification. To address these limitations, this study proposes a high-resolution cross-correlation Power Doppler (HR-XPD) method that integrates spatial radiality weighting with Doppler signal coherence analysis, thereby enhancing spatial resolution while suppressing artifacts and background noise. Quantitative evaluations in simulation and in vivo experiments on healthy human liver, transplanted human kidney, and pig kidney demonstrated that HR-XPD significantly improves microvascular resolvability and contrast compared to conventional PD. In vivo results showed up to a 2 to 3-fold enhancement in spatial resolution and an increase in contrast by up to 20 dB. High-resolution vascular details were clearly depicted within a short acquisition time of only 0.3 s-1.2 s without the use of contrast agents. These findings indicate that HR-XPD provides an effective, contrast-free, and high-resolution microvascular imaging approach with broad applicability in both preclinical and clinical research.

physics.med-ph↗

arXiv2025

Self-supervised Deep Learning for Denoising in Ultrasound Microvascular Imaging

Ultrasound microvascular imaging (UMI) is often hindered by low signal-to-noise ratio (SNR), especially in contrast-free or deep tissue scenarios, which impairs subsequent vascular quantification and reliable disease diagnosis. To address this challenge, we propose Half-Angle-to-Half-Angle (HA2HA), a self-supervised denoising framework specifically designed for UMI. HA2HA constructs training pairs from complementary angular subsets of beamformed radio-frequency (RF) blood flow data, across which vascular signals remain consistent while noise varies. HA2HA was trained using in-vivo contrast-free pig kidney data and validated across diverse datasets, including contrast-free and contrast-enhanced data from pig kidneys, as well as human liver and kidney. An improvement exceeding 15 dB in both contrast-to-noise ratio (CNR) and SNR was observed, indicating a substantial enhancement in image quality. In addition to power Doppler imaging, denoising directly in the RF domain is also beneficial for other downstream processing such as color Doppler imaging (CDI). CDI results of human liver derived from the HA2HA-denoised signals exhibited improved microvascular flow visualization, with a suppressed noisy background. HA2HA offers a label-free, generalizable, and clinically applicable solution for robust vascular imaging in both contrast-free and contrast-enhanced UMI.

eess.IV↗

arXiv2024

Optimizing In Vivo Data Acquisition for Robust Clinical Microvascular Imaging Using Ultrasound Localization Microscopy

Ultrasound localization microscopy (ULM) enables microvascular imaging at spatial resolutions beyond the acoustic diffraction limit, offering significant clinical potentials. However, ULM performance relies heavily on microbubble (MB) signal sparsity, the number of detected MBs, and signal-to-noise ratio (SNR), all of which vary in clinical scenarios involving bolus MB injections. These sources of variations underscore the need to optimize MB dosage, data acquisition timing, and imaging settings in order to standardize and optimize ULM of microvasculature. This pilot study investigated temporal changes in MB signals during bolus injections in both pig and human models to optimize data acquisition for clinical ULM. Quantitative indices were developed to evaluate MB signal quality, guiding selection of acquisition timing that balances the MB localization quality and adequate MB counts. The effects of transmitted voltage and dosage were also explored. In the pig model, a relatively short window (approximately 10 seconds) for optimal acquisition was identified during the rapid wash-out phase, highlighting the need for real-time MB signal monitoring during data acquisition. The slower wash-out phase in humans allowed for a more flexible imaging window of 1-2 minutes, while trade-offs were observed between localization quality and MB density (or acquisition length) at different wash-out phase timings. Guided by these findings, robust ULM imaging was achieved in both pig and human kidneys using a short period of data acquisition, demonstrating its feasibility in clinical practice. This study provides insights into optimizing data acquisition for consistent and reproducible ULM, paving the way for its standardization and broader clinical applications.

physics.med-ph↗