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Extraction of Peak Velocity Profiles from Doppler Echocardiography Using Image Processing
The objective of this study is to extract positive and negative peak velocity profiles from Doppler echocardiographic images. These profiles are currently estimated using tedious manual approaches. Profiles can be used to establish realistic boundary conditions for computational hemodynamic studies...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6784240/ https://www.ncbi.nlm.nih.gov/pubmed/31357566 http://dx.doi.org/10.3390/bioengineering6030064 |
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author | Taebi, Amirtahà Sandler, Richard H. Kakavand, Bahram Mansy, Hansen A. |
author_facet | Taebi, Amirtahà Sandler, Richard H. Kakavand, Bahram Mansy, Hansen A. |
author_sort | Taebi, Amirtahà |
collection | PubMed |
description | The objective of this study is to extract positive and negative peak velocity profiles from Doppler echocardiographic images. These profiles are currently estimated using tedious manual approaches. Profiles can be used to establish realistic boundary conditions for computational hemodynamic studies and to estimate cardiac time intervals, which are of clinical utility. In the current study, digital image processing algorithms that rely on intensity calculations and two different thresholding methods were proposed and tested. Image intensity histograms were used to guide threshold choices, which were selected such that the resulting velocity profiles appropriately represent Doppler shift envelopes. The resulting peak velocity profiles contained artifacts in the form of sudden velocity changes and possible outliers. To reduce these artifacts, image smoothing using a moving average process was then implemented. Bland–Altman analysis suggested good agreement between the two thresholding methods. Artifacts decreased when image smoothing was performed. Results also suggested that one thresholding method tended to provide the lower limit (i.e., underestimate) of velocities, while the second tended to provide the velocity upper limit (i.e., overestimate). Combining estimates from both methods appeared to provide a smoother peak velocity profile estimate. The proposed automated approach may be useful for objective estimation of peak velocity profiles, which may be helpful for computational hemodynamic studies and may increase the efficiency of current clinical diagnostic tools. |
format | Online Article Text |
id | pubmed-6784240 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-67842402019-10-16 Extraction of Peak Velocity Profiles from Doppler Echocardiography Using Image Processing Taebi, Amirtahà Sandler, Richard H. Kakavand, Bahram Mansy, Hansen A. Bioengineering (Basel) Article The objective of this study is to extract positive and negative peak velocity profiles from Doppler echocardiographic images. These profiles are currently estimated using tedious manual approaches. Profiles can be used to establish realistic boundary conditions for computational hemodynamic studies and to estimate cardiac time intervals, which are of clinical utility. In the current study, digital image processing algorithms that rely on intensity calculations and two different thresholding methods were proposed and tested. Image intensity histograms were used to guide threshold choices, which were selected such that the resulting velocity profiles appropriately represent Doppler shift envelopes. The resulting peak velocity profiles contained artifacts in the form of sudden velocity changes and possible outliers. To reduce these artifacts, image smoothing using a moving average process was then implemented. Bland–Altman analysis suggested good agreement between the two thresholding methods. Artifacts decreased when image smoothing was performed. Results also suggested that one thresholding method tended to provide the lower limit (i.e., underestimate) of velocities, while the second tended to provide the velocity upper limit (i.e., overestimate). Combining estimates from both methods appeared to provide a smoother peak velocity profile estimate. The proposed automated approach may be useful for objective estimation of peak velocity profiles, which may be helpful for computational hemodynamic studies and may increase the efficiency of current clinical diagnostic tools. MDPI 2019-07-26 /pmc/articles/PMC6784240/ /pubmed/31357566 http://dx.doi.org/10.3390/bioengineering6030064 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Taebi, Amirtahà Sandler, Richard H. Kakavand, Bahram Mansy, Hansen A. Extraction of Peak Velocity Profiles from Doppler Echocardiography Using Image Processing |
title | Extraction of Peak Velocity Profiles from Doppler Echocardiography Using Image Processing |
title_full | Extraction of Peak Velocity Profiles from Doppler Echocardiography Using Image Processing |
title_fullStr | Extraction of Peak Velocity Profiles from Doppler Echocardiography Using Image Processing |
title_full_unstemmed | Extraction of Peak Velocity Profiles from Doppler Echocardiography Using Image Processing |
title_short | Extraction of Peak Velocity Profiles from Doppler Echocardiography Using Image Processing |
title_sort | extraction of peak velocity profiles from doppler echocardiography using image processing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6784240/ https://www.ncbi.nlm.nih.gov/pubmed/31357566 http://dx.doi.org/10.3390/bioengineering6030064 |
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