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Spatially segmented SVD clutter filtering in cardiac blood flow imaging with diverging waves by Ehsan Jafarzadeh; Christine EM Démoré; Peter N Burns; David E Goertz is a Engineering article available to read on EtoBox.

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Ultrafast ultrasound imaging enables the visualization of rapidly changing blood flow dynamics in the chambers of the heart. Singular value decomposition (SVD) filters outperform conventional high pass clutter rejection filters for ultrafast blood flow imaging of small and shallow fields of view (e.g., functional imaging of brain activity). However, implementing SVD filters can be challenging in cardiac imaging due to the complex spatially and temporally varying tissue characteristics. To address this challenge, we describe a method that involves excluding the proximal portion of the image (near the chest wall) and divides the reduced field of view into overlapped segments, within which tissue signals are expected to be spatially and temporally coherent. SVD filtering with automatic selection of cut-off singular vector orders to remove tissue and noise signals is implemented for each segment. Auto-thresholding is based on the coherence of spatial singular vectors, delineating tissue, blood, and noise subspaces within a spatial similarity matrix calculated for each segment. Filtered blood flow signals from the segments are reconstructed and then combined and Doppler processing is us

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Author

Ehsan Jafarzadeh; Christine EM Démoré; Peter N Burns; David E Goertz

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)