Fast 3D Ultrasound Localization Microscopy via Projection-based Processing Framework Article Swipe
Jingyi Yin
,
U‐Wai Lok
,
Ryan M. DeRuiter
,
Tao Wu
,
Kaipeng Ji
,
Yanzhe Zhao
,
James D. Krier
,
Xiangyang Zhu
,
Lilach O. Lerman
,
Chengwu Huang
,
Shigao Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2511.21647
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2511.21647
Three-dimensional ultrasound localization microscopy (ULM) enables comprehensive visualization of the vasculature, thereby improving diagnostic reliability. Nevertheless, its clinical translation remains challenging, as the exponential growth in voxel count for full 3D reconstruction imposes heavy computational demands and extensive post-processing time. In this row-column array (RCA)-based 3D in vivo pig kidney ULM study, we reformulate each step of the full 3D ULM pipeline, including beamforming, clutter filtering, motion estimation, microbubble separation and localization into a series of computational-efficient 2D operations, substantially reducing the number of voxels to be processed while maintaining comparable accuracy. The proposed framework reconstructs each 0.75-s ensemble acquired at frame rate of 400 Hz, covering a 25*27.4*27.4 mm3 volume, in 0.52 s (70% of the acquisition time) on a single RTX A6000 Ada GPU, while maintaining ULM image quality comparable to conventional 3D processing. Quantitatively, it achieves a structural similarity index (SSIM) of 0.93 between density maps and a voxel-wise velocity agreement with slope of 0.93 and R2 = 0.88, closely matching conventional 3D results, and for the first time, demonstrating potential for real-time feedback during scanning, which could improve robustness, reduce operator dependence and accelerate clinical workflows.
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- http://arxiv.org/abs/2511.21647
- https://arxiv.org/pdf/2511.21647
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All OpenAlex metadata
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https://doi.org/10.48550/arxiv.2511.21647Digital Object Identifier
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Fast 3D Ultrasound Localization Microscopy via Projection-based Processing FrameworkWork title
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preprintOpenAlex work type
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2025Year of publication
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2025-11-26Full publication date if available
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Jingyi Yin, U‐Wai Lok, Ryan M. DeRuiter, Tao Wu, Kaipeng Ji, Yanzhe Zhao, James D. Krier, Xiangyang Zhu, Lilach O. Lerman, Chengwu Huang, Shigao ChenList of authors in order
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https://arxiv.org/abs/2511.21647Publisher landing page
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https://arxiv.org/pdf/2511.21647Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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0Total citation count in OpenAlex
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