High Spatiotemporal Resolution Microvessel Imaging Based on Microbubble Gradient Convergence and Accumulation Reconstruction

the application of medical ultrasound imaging instruments, microvessel detection is the basis for accurate measurement of vessel parameters and hemodynamic parameters, which directly affects the instrument's ability to diagnose diseases early and evaluate treatment effects. Ultrasound localization microscopy enables superresolution imaging and detecting of deep microvessels via microbubble (MB) localization and tracking, but with low temporal resolution. To balance spatial and temporal resolution, higher-order statistics improves spatial resolution and enables the reconstruction of a microvessel image with shorter acquisitions. However, in plane wave imaging, the point spread function (PSF) broadens and changes with depth, limiting spatial resolution gains. To overcome this and further improve spatiotemporal resolution, a nonlocalized dynamic microvessel imaging method called MB gradient convergence and accumulation reconstruction (GCAR) was proposed. The GCAR method is implemented via spatial and temporal transformations of data at high MB concentrations; the spatial transformation anisotropically shrinks and separates MBs toward the center by using the symmetry of the MB gradient, and the temporal transformation reconstructs microvessels by measuring the statistical independence of the MB signals through high-order cross accumulation. The simulation results revealed that GCAR decreased the full width at half maximum of the MBs by 80%, and was able to separate parallel vessels with spacings of 0.25λ (4 MHz) and 0.125λ (10 MHz). The in vivo results revealed that GCAR could quickly reconstruct the microvasculature with a spatial resolution of 18.19 μm (12 MHz) for the rat brain and 76.28 μm (4 MHz) for the rabbit kidney. GCAR dynamically displays blood flow with a temporal resolution of 50 ms. The GCAR approach improves the spatiotemporal resolution of medical ultrasound imaging instruments, detects smaller and more microvessels with shorter acquisition times, which may provide a useful tool for hemodynamic measurement and examination of microvascular diseases.

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Publication Details

Journal
IEEE Transactions on Instrumentation and Measurement
Published
2025-01-01
DOI
https://doi.org/10.1109/tim.2025.3602536
Citations
2
Primary Topic
Photoacoustic and Ultrasonic Imaging
Type
article
Field-Weighted Citation Impact
1.35

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article

High Spatiotemporal Resolution Microvessel Imaging Based on Microbubble Gradient Convergence and Accumulation Reconstruction

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2 citations
IEEE Transactions on Instrumentation and Measurement
Photoacoustic and Ultrasonic Imaging
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article

High Spatiotemporal Resolution Microvessel Imaging Based on Microbubble Gradient Convergence and Accumulation Reconstruction

Xiao Su, Hanbing Chu, Liyuan Jiang, Yichen Yan, Y. H. Chen, Mingxi Wan, Yujin Zong, Jiacheng Liu, Meiling Liang, Jinxuan Ma
article en
2 citations

Abstract

the application of medical ultrasound imaging instruments, microvessel detection is the basis for accurate measurement of vessel parameters and hemodynamic parameters, which directly affects the instrument's ability to diagnose diseases early and evaluate treatment effects. Ultrasound localization microscopy enables superresolution imaging and detecting of deep microvessels via microbubble (MB) localization and tracking, but with low temporal resolution. To balance spatial and temporal resolution, higher-order statistics improves spatial resolution and enables the reconstruction of a microvessel image with shorter acquisitions. However, in plane wave imaging, the point spread function (PSF) broadens and changes with depth, limiting spatial resolution gains. To overcome this and further improve spatiotemporal resolution, a nonlocalized dynamic microvessel imaging method called MB gradient convergence and accumulation reconstruction (GCAR) was proposed. The GCAR method is implemented via spatial and temporal transformations of data at high MB concentrations; the spatial transformation anisotropically shrinks and separates MBs toward the center by using the symmetry of the MB gradient, and the temporal transformation reconstructs microvessels by measuring the statistical independence of the MB signals through high-order cross accumulation. The simulation results revealed that GCAR decreased the full width at half maximum of the MBs by 80%, and was able to separate parallel vessels with spacings of 0.25λ (4 MHz) and 0.125λ (10 MHz). The in vivo results revealed that GCAR could quickly reconstruct the microvasculature with a spatial resolution of 18.19 μm (12 MHz) for the rat brain and 76.28 μm (4 MHz) for the rabbit kidney. GCAR dynamically displays blood flow with a temporal resolution of 50 ms. The GCAR approach improves the spatiotemporal resolution of medical ultrasound imaging instruments, detects smaller and more microvessels with shorter acquisition times, which may provide a useful tool for hemodynamic measurement and examination of microvascular diseases.

IEEE Transactions on Instrumentation and MeasurementVol. 74
Ministry of Education of the People's Republic of China (CN), Ministry of Education (TW), Xi'an Jiaotong University (CN)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 18%
Photoacoustic and Ultrasonic Imaging
1.35
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