Literature DB >> 33468358

Improved Ultrasound Microvessel Imaging Using Deconvolution with Total Variation Regularization.

U-Wai Lok1, Joshua D Trzasko1, Chengwu Huang1, Shanshan Tang1, Ping Gong1, Yohan Kim2, Fabrice Lucien2, Matthew R Lowerison3, Pengfei Song3, Shigao Chen4.   

Abstract

Singular value decomposition-based clutter filters can robustly reject tissue clutter, allowing for detection of slow blood flow in imaging microvasculature. However, to identify microvessels, high ultrasound frequency must be used to increase the spatial resolution at the expense of shorter depth of penetration. Deconvolution using Tikhonov regularization is an imaging processing method widely used to improve spatial resolution. The ringing artifact of Tikhonov regularization, though, can produce image artifacts such as non-existent microvessels, which degrade image quality. Therefore, a deconvolution method using total variation is proposed in this study to improve spatial resolution and mitigate the ringing artifact. Performance of the proposed method was evaluated using chicken embryo brain, ex ovo chicken embryo chorioallantoic membrane and tumor data. Results revealed that the reconstructed power Doppler (PD) images are substantially improved in spatial resolution compared with original PD images: the full width half-maximum (FWHM) of the cross-sectional profile of a microvessel was improved from 132 to 83 µm. Two neighboring microvessels that were 154 µm apart were better separated using the proposed method than conventional PD imaging. Additionally, 223 FWHMs measured from the cross-sectional profiles of 223 vessels were used to determine the improvement in FWHM with the proposed method statistically. The mean ± standard deviation of the FWHM without and with the proposed method was 233.19 ± 85.08 and 172.31 ± 75.11 μm, respectively; the maximum FWHM without and with the proposed method was 693.01 and 668.69 μm; and the minimum FWHM without and with the proposed method was 73.92 and 45.74 μm. There were statistically significant differences between FWHMs with and without the proposed method according to the rank-sum test, p < 0.0001. The contrast-to-noise ratio improved from 1.06 to 4.03 dB with use of the proposed method. We also compared the proposed method with Tikhonov regularization using ex ovo chicken embryo chorioallantoic membrane data. We found that the proposed method outperformed Tikhonov regularization as false microvessels appeared using the Tikhonov regularization but not with the proposed method. These results indicate that the proposed method is capable of providing more robust PD images with higher spatial resolution and higher contrast-to-noise ratio.
Copyright © 2021 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Deconvolution; Microvessel imaging; Power Doppler imaging

Mesh:

Year:  2021        PMID: 33468358      PMCID: PMC7908678          DOI: 10.1016/j.ultrasmedbio.2020.12.025

Source DB:  PubMed          Journal:  Ultrasound Med Biol        ISSN: 0301-5629            Impact factor:   2.998


  16 in total

1.  Debiasing-Based Noise Suppression for Ultrafast Ultrasound Microvessel Imaging.

Authors:  Chengwu Huang; Pengfei Song; Ping Gong; Joshua D Trzasko; Armando Manduca; Shigao Chen
Journal:  IEEE Trans Ultrason Ferroelectr Freq Control       Date:  2019-05-22       Impact factor: 2.725

2.  Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution.

Authors:  Nicolas Dey; Laure Blanc-Feraud; Christophe Zimmer; Pascal Roux; Zvi Kam; Jean-Christophe Olivo-Marin; Josiane Zerubia
Journal:  Microsc Res Tech       Date:  2006-04       Impact factor: 2.769

3.  Ultrasonic imaging using a computed point spread function.

Authors:  Ramsharan Rangarajan; C V Krishnamurthy; Krishnan Balasubramaniam
Journal:  IEEE Trans Ultrason Ferroelectr Freq Control       Date:  2008-02       Impact factor: 2.725

4.  A New Method for Deblurring and Denoising of Medical Images using Complex Wavelet Transform.

Authors:  Ashish Khare; Uma Shanker Tiwary
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2005

5.  The curvelet transform for image denoising.

Authors:  Jean-Luc Starck; Emmanuel J Candès; David L Donoho
Journal:  IEEE Trans Image Process       Date:  2002       Impact factor: 10.856

6.  Sensitivity to point-spread function parameters in medical ultrasound image deconvolution.

Authors:  Ho-Chul Shin; Richard Prager; James Ng; Henry Gomersall; Nick Kingsbury; Graham Treece; Andrew Gee
Journal:  Ultrasonics       Date:  2008-10-26       Impact factor: 2.890

7.  Spatiotemporal Clutter Filtering of Ultrafast Ultrasound Data Highly Increases Doppler and fUltrasound Sensitivity.

Authors:  Charlie Demené; Thomas Deffieux; Mathieu Pernot; Bruno-Félix Osmanski; Valérie Biran; Jean-Luc Gennisson; Lim-Anna Sieu; Antoine Bergel; Stéphanie Franqui; Jean-Michel Correas; Ivan Cohen; Olivier Baud; Mickael Tanter
Journal:  IEEE Trans Med Imaging       Date:  2015-04-30       Impact factor: 10.048

8.  Ultrasound Small Vessel Imaging With Block-Wise Adaptive Local Clutter Filtering.

Authors:  Pengfei Song; Armando Manduca; Joshua D Trzasko; Shigao Chen
Journal:  IEEE Trans Med Imaging       Date:  2016-09-02       Impact factor: 10.048

9.  The Generalized Contrast-to-Noise Ratio: A Formal Definition for Lesion Detectability.

Authors:  Alfonso Rodriguez-Molares; Ole Marius Hoel Rindal; Jan D'hooge; Svein-Erik Masoy; Andreas Austeng; Muyinatu A Lediju Bell; Hans Torp
Journal:  IEEE Trans Ultrason Ferroelectr Freq Control       Date:  2019-11-29       Impact factor: 2.725

10.  Robust Image Restoration for Motion Blur of Image Sensors.

Authors:  Fasheng Yang; Yongmei Huang; Yihan Luo; Lixing Li; Hongwei Li
Journal:  Sensors (Basel)       Date:  2016-06-09       Impact factor: 3.576

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