Accelerated Singular Value-Based Ultrasound Blood Flow Clutter Filtering with Randomized Singular Value Decomposition and Randomized Spatial Downsampling

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11 Citations (Scopus)

Abstract

Singular value decomposition (SVD)-based ultrasound blood flow clutter filters have recently demonstrated substantial improvement in clutter rejection for ultrafast plane wave microvessel imaging, and have become the commonly used clutter filtering method for many novel ultrafast imaging applications such as functional ultrasound and super-resolution imaging. At present, however, the computational burden of SVD remains as a major hurdle for practical implementation and clinical translation of this method. To address this challenge, in the study we present two blood flow clutter filtering methods based on randomized SVD (rSVD) and randomized spatial downsampling to accelerate SVD clutter filtering with minimal compromise to the clutter filter performance. rSVD accelerates SVD computation by approximating the $k$ largest singular values, while random downsampling accelerates both full SVD and rSVD by decomposing the original large data matrix into small matrices that can be processed in parallel. An in vitro blood flow phantom study with the presence of heavy tissue clutter showed significantly improved computational performance using the proposed methods with minimal deterioration to the clutter filter performance (less than 3-dB reduction in blood to clutter ratio, less than 0.2-cm2/s2 increase in flow mean squared error, less than 0.1-cm/s increase in the standard deviation of the vessel blood flow signal, and less than 0.3-cm/s increase in tissue clutter velocity for both full SVD and rSVD when the downsampling factor was less than 20x). The maximum acceleration was about threefold from randomized spatial downsampling, and approximately another threefold from rSVD. An in vivo rabbit kidney perfusion study showed that rSVD provided comparable performance to full SVD in clutter rejection in vivo (maximum difference of blood to clutter ratio was less than 0.6 dB), and random downsampling provided artifact-free perfusion imaging results when combined with both full SVD and rSVD. The blood to clutter ratio was still above 10 dB with a downsampling factor of 60x. We also demonstrated real-time microvessel imaging feasibility (∼40-ms processing time) by combining rSVD with random downsampling. The findings and methods presented in this paper may greatly facilitate the new area of ultrafast microvessel imaging research.

Original languageEnglish (US)
Article number7845720
Pages (from-to)706-716
Number of pages11
JournalIEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
Volume64
Issue number4
DOIs
StatePublished - Apr 1 2017

Fingerprint

clutter
Singular value decomposition
blood flow
Blood
Ultrasonics
decomposition
Imaging techniques
blood
filters
rejection
Tissue
rabbits
kidneys
matrices
deterioration
Blood vessels
vessels
artifacts
standard deviation
plane waves

Keywords

  • Clutter filter
  • microvessel imaging
  • parallel computing
  • random
  • singular value decomposition (SVD)
  • ultrafast imaging
  • ultrasound doppler

ASJC Scopus subject areas

  • Instrumentation
  • Acoustics and Ultrasonics
  • Electrical and Electronic Engineering

Cite this

@article{4825172b1d864ba5a0b8a774b8639904,
title = "Accelerated Singular Value-Based Ultrasound Blood Flow Clutter Filtering with Randomized Singular Value Decomposition and Randomized Spatial Downsampling",
abstract = "Singular value decomposition (SVD)-based ultrasound blood flow clutter filters have recently demonstrated substantial improvement in clutter rejection for ultrafast plane wave microvessel imaging, and have become the commonly used clutter filtering method for many novel ultrafast imaging applications such as functional ultrasound and super-resolution imaging. At present, however, the computational burden of SVD remains as a major hurdle for practical implementation and clinical translation of this method. To address this challenge, in the study we present two blood flow clutter filtering methods based on randomized SVD (rSVD) and randomized spatial downsampling to accelerate SVD clutter filtering with minimal compromise to the clutter filter performance. rSVD accelerates SVD computation by approximating the $k$ largest singular values, while random downsampling accelerates both full SVD and rSVD by decomposing the original large data matrix into small matrices that can be processed in parallel. An in vitro blood flow phantom study with the presence of heavy tissue clutter showed significantly improved computational performance using the proposed methods with minimal deterioration to the clutter filter performance (less than 3-dB reduction in blood to clutter ratio, less than 0.2-cm2/s2 increase in flow mean squared error, less than 0.1-cm/s increase in the standard deviation of the vessel blood flow signal, and less than 0.3-cm/s increase in tissue clutter velocity for both full SVD and rSVD when the downsampling factor was less than 20x). The maximum acceleration was about threefold from randomized spatial downsampling, and approximately another threefold from rSVD. An in vivo rabbit kidney perfusion study showed that rSVD provided comparable performance to full SVD in clutter rejection in vivo (maximum difference of blood to clutter ratio was less than 0.6 dB), and random downsampling provided artifact-free perfusion imaging results when combined with both full SVD and rSVD. The blood to clutter ratio was still above 10 dB with a downsampling factor of 60x. We also demonstrated real-time microvessel imaging feasibility (∼40-ms processing time) by combining rSVD with random downsampling. The findings and methods presented in this paper may greatly facilitate the new area of ultrafast microvessel imaging research.",
keywords = "Clutter filter, microvessel imaging, parallel computing, random, singular value decomposition (SVD), ultrafast imaging, ultrasound doppler",
author = "Pengfei Song and Trazasko, {Joshua D} and Armando Manduca and Bo Qiang and Kadirvel, {Ramanathan D} and Kallmes, {David F} and Chen, {Shigao D}",
year = "2017",
month = "4",
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doi = "10.1109/TUFFC.2017.2665342",
language = "English (US)",
volume = "64",
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journal = "IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control",
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TY - JOUR

T1 - Accelerated Singular Value-Based Ultrasound Blood Flow Clutter Filtering with Randomized Singular Value Decomposition and Randomized Spatial Downsampling

AU - Song, Pengfei

AU - Trazasko, Joshua D

AU - Manduca, Armando

AU - Qiang, Bo

AU - Kadirvel, Ramanathan D

AU - Kallmes, David F

AU - Chen, Shigao D

PY - 2017/4/1

Y1 - 2017/4/1

N2 - Singular value decomposition (SVD)-based ultrasound blood flow clutter filters have recently demonstrated substantial improvement in clutter rejection for ultrafast plane wave microvessel imaging, and have become the commonly used clutter filtering method for many novel ultrafast imaging applications such as functional ultrasound and super-resolution imaging. At present, however, the computational burden of SVD remains as a major hurdle for practical implementation and clinical translation of this method. To address this challenge, in the study we present two blood flow clutter filtering methods based on randomized SVD (rSVD) and randomized spatial downsampling to accelerate SVD clutter filtering with minimal compromise to the clutter filter performance. rSVD accelerates SVD computation by approximating the $k$ largest singular values, while random downsampling accelerates both full SVD and rSVD by decomposing the original large data matrix into small matrices that can be processed in parallel. An in vitro blood flow phantom study with the presence of heavy tissue clutter showed significantly improved computational performance using the proposed methods with minimal deterioration to the clutter filter performance (less than 3-dB reduction in blood to clutter ratio, less than 0.2-cm2/s2 increase in flow mean squared error, less than 0.1-cm/s increase in the standard deviation of the vessel blood flow signal, and less than 0.3-cm/s increase in tissue clutter velocity for both full SVD and rSVD when the downsampling factor was less than 20x). The maximum acceleration was about threefold from randomized spatial downsampling, and approximately another threefold from rSVD. An in vivo rabbit kidney perfusion study showed that rSVD provided comparable performance to full SVD in clutter rejection in vivo (maximum difference of blood to clutter ratio was less than 0.6 dB), and random downsampling provided artifact-free perfusion imaging results when combined with both full SVD and rSVD. The blood to clutter ratio was still above 10 dB with a downsampling factor of 60x. We also demonstrated real-time microvessel imaging feasibility (∼40-ms processing time) by combining rSVD with random downsampling. The findings and methods presented in this paper may greatly facilitate the new area of ultrafast microvessel imaging research.

AB - Singular value decomposition (SVD)-based ultrasound blood flow clutter filters have recently demonstrated substantial improvement in clutter rejection for ultrafast plane wave microvessel imaging, and have become the commonly used clutter filtering method for many novel ultrafast imaging applications such as functional ultrasound and super-resolution imaging. At present, however, the computational burden of SVD remains as a major hurdle for practical implementation and clinical translation of this method. To address this challenge, in the study we present two blood flow clutter filtering methods based on randomized SVD (rSVD) and randomized spatial downsampling to accelerate SVD clutter filtering with minimal compromise to the clutter filter performance. rSVD accelerates SVD computation by approximating the $k$ largest singular values, while random downsampling accelerates both full SVD and rSVD by decomposing the original large data matrix into small matrices that can be processed in parallel. An in vitro blood flow phantom study with the presence of heavy tissue clutter showed significantly improved computational performance using the proposed methods with minimal deterioration to the clutter filter performance (less than 3-dB reduction in blood to clutter ratio, less than 0.2-cm2/s2 increase in flow mean squared error, less than 0.1-cm/s increase in the standard deviation of the vessel blood flow signal, and less than 0.3-cm/s increase in tissue clutter velocity for both full SVD and rSVD when the downsampling factor was less than 20x). The maximum acceleration was about threefold from randomized spatial downsampling, and approximately another threefold from rSVD. An in vivo rabbit kidney perfusion study showed that rSVD provided comparable performance to full SVD in clutter rejection in vivo (maximum difference of blood to clutter ratio was less than 0.6 dB), and random downsampling provided artifact-free perfusion imaging results when combined with both full SVD and rSVD. The blood to clutter ratio was still above 10 dB with a downsampling factor of 60x. We also demonstrated real-time microvessel imaging feasibility (∼40-ms processing time) by combining rSVD with random downsampling. The findings and methods presented in this paper may greatly facilitate the new area of ultrafast microvessel imaging research.

KW - Clutter filter

KW - microvessel imaging

KW - parallel computing

KW - random

KW - singular value decomposition (SVD)

KW - ultrafast imaging

KW - ultrasound doppler

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