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Real time SVD-based clutter filtering using randomized singular value decomposition and spatial downsampling for micro-vessel imaging on a Verasonics ultrasound system.

ABSTRACT: Singular value decomposition (SVD)-based clutter filters can robustly reject the tissue clutter as compared with the conventional high pass filter-based clutter filters. However, the computational burden of SVD makes real time SVD-based clutter filtering challenging (e.g. frame rate at least 10-15 Hz with region of interest of about 4 × 4 cm2). Recently, we proposed an acceleration method based on randomized SVD (rSVD) clutter filtering and randomized spatial downsampling, which can significantly reduce the computational complexity without compromising the clutter rejection capability. However, this method has not been implemented on an ultrasound scanner and tested for its performance. In this study, we implement this acceleration method on a Verasonics scanner using a multi-core CPU architecture, and evaluate the selections of the imaging and processing parameters to enable real time micro-vessel imaging. The Blood-to-Clutter Ratio (BCR) performance was evaluated on a Verasonics machine with different settings of parameters such as block size and ensemble size. The demonstration of real time process was implemented on a 12-core CPU (downsampling factor of 12, 12-threads in this study) host computer. The processing time of the rSVD-based clutter filter was less than 30 ms and BCRs were higher than 20 dB as the block size, ensemble size and the rank of tissue clutter subspace were set as 30 × 30, 45 and 26 respectively. We also demonstrate that the micro-vessel imaging frame rate of the proposed architecture can reach approximately 22 Hz when the block size, ensemble size and the rank of tissue clutter subspace were set as 20 × 20 pixels, 45 and 26 respectively (using both images and supplementary videos). The proposed method may be important for real time 2D scanning of tumor microvessels in 3D to select and store the most representative 2D view with most abnormal micro-vessels for better diagnosis.

SUBMITTER: Lok UW

PROVIDER: S-EPMC7293562 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundBoth microarrays and quantitative real-time PCR are convenient tools for studying the transcriptional levels of genes. The former is preferable for large scale studies while the latter is a more targeted technique. Because of platform-dependent systematic effects, simple comparisons or merging of datasets obtained by these technologies are difficult, even though they may often be desirable. These difficulties are exacerbated if there is only partial overlap between the experimental conditions and genes probed in the two datasets.ResultsWe show here that the generalized singular value decomposition provides a practical tool for merging a small, targeted dataset obtained by quantitative real-time PCR of specific genes with a much larger microarray dataset. The technique permits, for the first time, the identification of genes present in only one dataset co-expressed with a target gene present exclusively in the other dataset, even when experimental conditions for the two datasets are not identical. With the rapidly increasing number of publically available large scale microarray datasets the latter is frequently the case. The method enables us to discover putative candidate genes involved in the biosynthesis of the (1,3;1,4)-beta-D-glucan polysaccharide found in plant cell walls.ConclusionWe show that the generalized singular value decomposition provides a viable tool for a combined analysis of two gene expression datasets with only partial overlap of both gene sets and experimental conditions. We illustrate how the decomposition can be optimized self-consistently by using a judicious choice of genes to define it. The ability of the technique to seamlessly define a concept of "co-expression" across both datasets provides an avenue for meaningful data integration. We believe that it will prove to be particularly useful for exploiting large, publicly available, microarray datasets for species with unsequenced genomes by complementing them with more limited in-house expression measurements.

Dataset Information

Real time SVD-based clutter filtering using randomized singular value decomposition and spatial downsampling for micro-vessel imaging on a Verasonics ultrasound system.

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