Multiple Measurement Vector Model for Sparsity-Based Vascular Ultrasound Imaging

Didem Dogan, Pieter Kruizinga, Hans Bosch, Geert Leus

Research output: Chapter/Conference proceedingConference proceedingAcademic

1 Citation (Scopus)
44 Downloads (Pure)

Abstract

Ultrasound imaging of the vasculature has major significance for the detection of cardiovascular diseases and cancer. However, limited spatial resolution or long acquisition times of existing techniques limit the visualization of the microvascular structures. Enforcing sparsity in the underlying vasculature as well as exploiting statistical independence between voxels have become prominent for fast super-resolution imaging. However, such a statistical independence may not be valid for all voxels and may hence lead to a distorted signal model. Here we present an image reconstruction method that exploits the sparsity of the vasculature data without distorting the original signal model. We employ a multiple measurement vector (MMV) model to enforce the joint sparsity over the images at different time instants. To reduce the computational complexity of obtaining the solution, the ℓ 1 -SVD method is applied to the MMV model. We demonstrate that our method improves spatial resolution and provides a clear separation between blood vessels. Although our method is slightly slower than existing approaches, it outperforms them in terms of image reconstruction quality.
Original languageEnglish
Title of host publication2021 IEEE Statistical Signal Processing Workshop (SSP) 11-14 July 2021
Subtitle of host publicationINSPEC Accession Number: 21102349
Pages501-505
Number of pages5
ISBN (Electronic)9781728157672
DOIs
Publication statusPublished - 11 Jul 2021

Bibliographical note

Funding Information:
This publication is part of the project TOUCAN (with project number 17208) of the research programme TTW-OTP which is financed by the Dutch Research Council (NWO).

Publisher Copyright:
© 2021 IEEE.

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