Low-Cost 3-D Flow Estimation of Blood With Clutter
| Autor: | Ming Yang, J. Brian Fowlkes, Jian Zhou, Oliver D. Kripfgans, Chaitali Chakrabarti, Thomas F. Wenisch, Richard Sampson, Siyuan Wei |
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| Rok vydání: | 2017 |
| Předmět: |
Acoustics and Ultrasonics
Computer science 01 natural sciences Standard deviation 030218 nuclear medicine & medical imaging Constant false alarm rate 03 medical and health sciences Speckle pattern 0302 clinical medicine Imaging Three-Dimensional Motion estimation 0103 physical sciences Electronic engineering Image Processing Computer-Assisted Humans Electrical and Electronic Engineering 010301 acoustics Instrumentation Ultrasonography Phantoms Imaging Subpixel rendering Power iteration Kernel (statistics) Clutter Algorithm Algorithms Blood Flow Velocity |
| Zdroj: | IEEE transactions on ultrasonics, ferroelectrics, and frequency control. 64(5) |
| ISSN: | 1525-8955 |
| Popis: | Volumetric flow rate estimation is an important ultrasound medical imaging modality that is used for diagnosing cardiovascular diseases. Flow rates are obtained by integrating velocity estimates over a cross-sectional plane. Speckle tracking is a promising approach that overcomes the angle dependency of traditional Doppler methods, but suffers from poor lateral resolution. Recent work improves lateral velocity estimation accuracy by reconstructing a synthetic lateral phase (SLP) signal. However, the estimation accuracy of such approaches is compromised by the presence of clutter. Eigen-based clutter filtering has been shown to be effective in removing the clutter signal; but it is computationally expensive, precluding its use at high volume rates. In this paper, we propose low-complexity schemes for both velocity estimation and clutter filtering. We use a two-tiered motion estimation scheme to combine the low complexity sum-of-absolute-difference and SLP methods to achieve subpixel lateral accuracy. We reduce the complexity of eigen-based clutter filtering by processing in subgroups and replacing singular value decomposition with less compute-intensive power iteration and subspace iteration methods. Finally, to improve flow rate estimation accuracy, we use kernel power weighting when integrating the velocity estimates. We evaluate our method for fast- and slow-moving clutter for beam-to-flow angles of 90° and 60° using Field II simulations, demonstrating high estimation accuracy across scenarios. For instance, for a beam-to-flow angle of 90° and fast-moving clutter, our estimation method provides a bias of −8.8% and standard deviation of 3.1% relative to the actual flow rate. |
| Databáze: | OpenAIRE |
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