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Erschienen in: Journal of Medical Ultrasonics 2/2019

24.01.2019 | Original Article

Identification of vascular lumen by singular value decomposition filtering on blood flow velocity distribution

verfasst von: Ryo Nagaoka, Hideyuki Hasegawa

Erschienen in: Journal of Medical Ultrasonics | Ausgabe 2/2019

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Abstract

Purpose

In the present study, we proposed a novel method for identification of the vascular lumen by employing singular value decomposition (SVD), and the feasibility of the proposed method was validated by in vivo measurement of the common carotid artery.

Method

SVD filtering was applied to a velocity map that was estimated using an autocorrelation method to identify the lumen region. In this study, the packet size was set at 999 frames with a frame rate of 1302 Hz. The region estimated by the proposed SVD filtering was compared with that estimated by the conventional power Doppler method.

Result

The averaged differences in feature values between vascular wall and lumen regions obtained by the proposed and conventional methods were 34 dB and 26 dB, respectively. The proposed method was hardly influenced by the cardiac phase and could separate the wall and lumen regions more stably. The proposed method could identify the lumen region by setting a threshold of − 28 dB from the averaged difference amplitude.

Conclusion

We proposed a novel method for identification of the vascular lumen. The proposed method could suppress the effects of wall motion, which was present in the conventional power Doppler image. The lumen region identified by the proposed method well conformed with the anatomical information in the B-mode image of the corresponding section.
Literatur
1.
Zurück zum Zitat Yiu BYS, Lai SSM, Yu ACH, et al. Vector projectile imaging: time-resolved dynamic visualization of complex flow patterns. Ultrasound Med Biol. 2014;40:2295–309.CrossRefPubMed Yiu BYS, Lai SSM, Yu ACH, et al. Vector projectile imaging: time-resolved dynamic visualization of complex flow patterns. Ultrasound Med Biol. 2014;40:2295–309.CrossRefPubMed
2.
Zurück zum Zitat Takahashi H, Hasegawa H, Kanai H. Echo speckle imaging of blood particles with high-frame-rate echocardiography. Jpn J Appl Phys. 2014;53:1–7.CrossRef Takahashi H, Hasegawa H, Kanai H. Echo speckle imaging of blood particles with high-frame-rate echocardiography. Jpn J Appl Phys. 2014;53:1–7.CrossRef
3.
Zurück zum Zitat Takahashi H, Hasegawa H, Kanai H. Echo motion imaging with adaptive clutter filter for assessment of cardiac blood flow. Jpn J Appl Phys. 2015;54:1–8.CrossRef Takahashi H, Hasegawa H, Kanai H. Echo motion imaging with adaptive clutter filter for assessment of cardiac blood flow. Jpn J Appl Phys. 2015;54:1–8.CrossRef
4.
Zurück zum Zitat Takahashi H, Hasegawa H, Kanai H. Temporal averaging of two-dimensional correlation functions for velocity vector imaging of cardiac blood flow. J Med Ultrason. 2015;42:323–30.CrossRef Takahashi H, Hasegawa H, Kanai H. Temporal averaging of two-dimensional correlation functions for velocity vector imaging of cardiac blood flow. J Med Ultrason. 2015;42:323–30.CrossRef
5.
Zurück zum Zitat Fadnes S, Bjærum S, Torp H, et al. Clutter filtering influence on blood velocity estimation using speckle tracking. IEEE Trans Ultrason Ferroelectr Freq Control. 2015;62:2079–91.CrossRefPubMed Fadnes S, Bjærum S, Torp H, et al. Clutter filtering influence on blood velocity estimation using speckle tracking. IEEE Trans Ultrason Ferroelectr Freq Control. 2015;62:2079–91.CrossRefPubMed
6.
Zurück zum Zitat Rouco J, Azevedo E, Campilho A. Automatic lumen detection on longitudinal ultrasound b-mode images of the carotid using phase symmetry. Sensors. 2016;16:1–21.CrossRef Rouco J, Azevedo E, Campilho A. Automatic lumen detection on longitudinal ultrasound b-mode images of the carotid using phase symmetry. Sensors. 2016;16:1–21.CrossRef
7.
Zurück zum Zitat Santos AMF, Tavares JMRS, Souca L, et al. Automatic segmentation of the lumen of the carotid artery in ultrasound B-mode images. Conf Proc in SPIE Med Imge. 2013;8670:1–16. Santos AMF, Tavares JMRS, Souca L, et al. Automatic segmentation of the lumen of the carotid artery in ultrasound B-mode images. Conf Proc in SPIE Med Imge. 2013;8670:1–16.
8.
Zurück zum Zitat Mendizabal-Ruiz G, Rivera M, Kakadiaris IA. A probabilistic segmentation method for the identification of luminal borders in intravascular ultrasound images. In: Proc in 2008 IEEE Conf. on computer vision and pattern recognition. 2008;53:pp 1–4. Mendizabal-Ruiz G, Rivera M, Kakadiaris IA. A probabilistic segmentation method for the identification of luminal borders in intravascular ultrasound images. In: Proc in 2008 IEEE Conf. on computer vision and pattern recognition. 2008;53:pp 1–4.
9.
Zurück zum Zitat Ibrahim N, Hasegawa H, Kanai H. Detection of boundaries of carotid arterial wall by analyzing ultrasonic RF signals. Jpn J Appl Phys. 2012;51:1–8.CrossRef Ibrahim N, Hasegawa H, Kanai H. Detection of boundaries of carotid arterial wall by analyzing ultrasonic RF signals. Jpn J Appl Phys. 2012;51:1–8.CrossRef
10.
Zurück zum Zitat Ibrahim N, Hasegawa H, Kanai H. Detection of arterial wall boundaries using an echo model composed of multiple ultrasonic pulses. Jpn J Appl Phys. 2013;51:1–10. Ibrahim N, Hasegawa H, Kanai H. Detection of arterial wall boundaries using an echo model composed of multiple ultrasonic pulses. Jpn J Appl Phys. 2013;51:1–10.
11.
Zurück zum Zitat Kinugawa T, Hasegawa T, Kanai H. Automated segmentation of heart wall using coherence among ultrasonic RF echoes”. Jpn J Appl Phys. 2008;47:4155–64.CrossRef Kinugawa T, Hasegawa T, Kanai H. Automated segmentation of heart wall using coherence among ultrasonic RF echoes”. Jpn J Appl Phys. 2008;47:4155–64.CrossRef
12.
Zurück zum Zitat Nillesen MM, Lopata RGP, Huisman HJ, Thijssen JM, Kapusta L, de Korte CL. Correlation based 3-D segmentation of the left ventricle in pediatric echocardiographic images using radio-frequency data. Ultrasound Med Biol. 2011;37:1409–20.CrossRefPubMed Nillesen MM, Lopata RGP, Huisman HJ, Thijssen JM, Kapusta L, de Korte CL. Correlation based 3-D segmentation of the left ventricle in pediatric echocardiographic images using radio-frequency data. Ultrasound Med Biol. 2011;37:1409–20.CrossRefPubMed
13.
Zurück zum Zitat Takahashi H, Hasegawa H, Kanai H. Automated identification of the heart wall throughout the entire cardiac cycle using optimal cardiac phase for extracted freatures. Jpn J Appl Phys. 2011;50:1–9.CrossRef Takahashi H, Hasegawa H, Kanai H. Automated identification of the heart wall throughout the entire cardiac cycle using optimal cardiac phase for extracted freatures. Jpn J Appl Phys. 2011;50:1–9.CrossRef
14.
Zurück zum Zitat Takahashi H, Hasegawa H, Kanai H. Improvement of automated identification of the heart wall in echocardiography by suppressing clutter component. Jpn J Appl Phys. 2013;50:1–7. Takahashi H, Hasegawa H, Kanai H. Improvement of automated identification of the heart wall in echocardiography by suppressing clutter component. Jpn J Appl Phys. 2013;50:1–7.
15.
Zurück zum Zitat Nakahara K, Hasegawa H, Kanai H. Optimization of feature extraction for automated identification of heart wall regions in different cross sections. Jpn J Appl Phys. 2014;50:1–9. Nakahara K, Hasegawa H, Kanai H. Optimization of feature extraction for automated identification of heart wall regions in different cross sections. Jpn J Appl Phys. 2014;50:1–9.
16.
Zurück zum Zitat Otazo R, Candes E, Soldickson DK, et al. Low-rank and sparse matrix decomposition for accelerated dynamics MRI with separation of background and dynamic domponetns. Magn Reson Med. 2014;73:1125–36.CrossRefPubMedPubMedCentral Otazo R, Candes E, Soldickson DK, et al. Low-rank and sparse matrix decomposition for accelerated dynamics MRI with separation of background and dynamic domponetns. Magn Reson Med. 2014;73:1125–36.CrossRefPubMedPubMedCentral
17.
Zurück zum Zitat Gao H, Yu H, Osher S, et al. Multi-energy CT based on a prior rank, intensity and sparsity model (PRISM). Inverse Probl. 2011;27:1–30.CrossRef Gao H, Yu H, Osher S, et al. Multi-energy CT based on a prior rank, intensity and sparsity model (PRISM). Inverse Probl. 2011;27:1–30.CrossRef
18.
Zurück zum Zitat Demené C, Deffieux T, Pernot M, et al. Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and ultrasound sensitivitiy. IEEE Trans Med Image. 2015;34:2271–85.CrossRef Demené C, Deffieux T, Pernot M, et al. Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and ultrasound sensitivitiy. IEEE Trans Med Image. 2015;34:2271–85.CrossRef
19.
Zurück zum Zitat Ikeda H, Nagaoka R, Lafond M, et al. Singular value decomposition of received ultrasound signal for separation among tissue, blood flow and cavitation signals. Jpn J Appl Phys. 2018;57:1–6. Ikeda H, Nagaoka R, Lafond M, et al. Singular value decomposition of received ultrasound signal for separation among tissue, blood flow and cavitation signals. Jpn J Appl Phys. 2018;57:1–6.
20.
Zurück zum Zitat Hasegawa H, Kanai H. Simultaneous imaging of artery-wall strain and blood flow by high frame rate acquisition of RF signals. IEEE Trans Ultrason Ferroelectr Freq Control. 2008;55:2626–39.CrossRefPubMed Hasegawa H, Kanai H. Simultaneous imaging of artery-wall strain and blood flow by high frame rate acquisition of RF signals. IEEE Trans Ultrason Ferroelectr Freq Control. 2008;55:2626–39.CrossRefPubMed
21.
Zurück zum Zitat Hasegawa H. Improvement of penetration of modified amplitude and phase estimation beamformer. J Med Ultrason. 2017;44:3–11.CrossRef Hasegawa H. Improvement of penetration of modified amplitude and phase estimation beamformer. J Med Ultrason. 2017;44:3–11.CrossRef
22.
Zurück zum Zitat Hasegawa H. Apodized adaptive beamformer. J Med Ultrason. 2017;44:155–65.CrossRef Hasegawa H. Apodized adaptive beamformer. J Med Ultrason. 2017;44:155–65.CrossRef
23.
Zurück zum Zitat Kasai C, Namekawa K, Koyano A, et al. Real-Time Two-Dimensional Blood Flow Imaging Using an Autocorrelation Technique. IEEE Trans Sonics Ultrason. 1985;32:458–64.CrossRef Kasai C, Namekawa K, Koyano A, et al. Real-Time Two-Dimensional Blood Flow Imaging Using an Autocorrelation Technique. IEEE Trans Sonics Ultrason. 1985;32:458–64.CrossRef
24.
Zurück zum Zitat Song H, Trzasko JD, Manduca A, et al. Accelerated singular value-based ultrasound blood flow clutter filtering with randomized singular value decomposition and randomized spatial downsanpling. IEEE Trans Ultrason Ferroelectr Freq Control. 2017;64:706–16.CrossRefPubMed Song H, Trzasko JD, Manduca A, et al. Accelerated singular value-based ultrasound blood flow clutter filtering with randomized singular value decomposition and randomized spatial downsanpling. IEEE Trans Ultrason Ferroelectr Freq Control. 2017;64:706–16.CrossRefPubMed
Metadaten
Titel
Identification of vascular lumen by singular value decomposition filtering on blood flow velocity distribution
verfasst von
Ryo Nagaoka
Hideyuki Hasegawa
Publikationsdatum
24.01.2019
Verlag
Springer Singapore
Erschienen in
Journal of Medical Ultrasonics / Ausgabe 2/2019
Print ISSN: 1346-4523
Elektronische ISSN: 1613-2254
DOI
https://doi.org/10.1007/s10396-019-00928-4

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