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Estimation of myocardial deformation using correlation image velocimetry

View Article: PubMed Central - PubMed

ABSTRACT

Background: Tagged Magnetic Resonance (tMR) imaging is a powerful technique for determining cardiovascular abnormalities. One of the reasons for tMR not being used in routine clinical practice is the lack of easy-to-use tools for image analysis and strain mapping. In this paper, we introduce a novel interdisciplinary method based on correlation image velocimetry (CIV) to estimate cardiac deformation and strain maps from tMR images.

Methods: CIV, a cross-correlation based pattern matching algorithm, analyses a pair of images to obtain the displacement field at sub-pixel accuracy with any desired spatial resolution. This first time application of CIV to tMR image analysis is implemented using an existing open source Matlab-based software called UVMAT. The method, which requires two main input parameters namely correlation box size (CB) and search box size (SB), is first validated using a synthetic grid image with grid sizes representative of typical tMR images. Phantom and patient images obtained from a Medical Imaging grand challenge dataset (http://stacom.cardiacatlas.org/motion-tracking-challenge/) were then analysed to obtain cardiac displacement fields and strain maps. The results were then compared with estimates from Harmonic Phase analysis (HARP) technique.

Results: For a known displacement field imposed on both the synthetic grid image and the phantom image, CIV is accurate for 3-pixel and larger displacements on a 512 × 512 image with (CB,SB)=(25,55) pixels. Further validation of our method is achieved by showing that our estimated landmark positions on patient images fall within the inter-observer variability in the ground truth. The effectiveness of our approach to analyse patient images is then established by calculating dense displacement fields throughout a cardiac cycle, and were found to be physiologically consistent. Circumferential strains were estimated at the apical, mid and basal slices of the heart, and were shown to compare favorably with those of HARP over the entire cardiac cycle, except in a few (∼4) of the segments in the 17-segment AHA model. The radial strains, however, are underestimated by our method in most segments when compared with HARP.

Conclusions: In summary, we have demonstrated the capability of CIV to accurately and efficiently quantify cardiac deformation from tMR images. Furthermore, physiologically consistent displacement fields and circumferential strain curves in most regions of the heart indicate that our approach, upon automating some pre-processing steps and testing in clinical trials, can potentially be implemented in a clinical setting.

Electronic supplementary material: The online version of this article (doi:10.1186/s12880-017-0195-7) contains supplementary material, which is available to authorized users.

No MeSH data available.


a 512 × 512 synthetic grid image with grids of dimensions 23 ×23 pixels, bounded by 8 pixels thick white lines. The red horizontal line represents the cross-sectional cut used to estimate the errors shown in Fig. 4a and b. Imposed displacement field: bdu(x,y), the x component and cdv(x,y), the y component, as described by Eqs. (5) and (6). The minimum and maximum values of /du/ are 0 & 11 pixels, respectively; the corresponding values for /dv/ are 0 & 9 pixels
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Fig3: a 512 × 512 synthetic grid image with grids of dimensions 23 ×23 pixels, bounded by 8 pixels thick white lines. The red horizontal line represents the cross-sectional cut used to estimate the errors shown in Fig. 4a and b. Imposed displacement field: bdu(x,y), the x component and cdv(x,y), the y component, as described by Eqs. (5) and (6). The minimum and maximum values of /du/ are 0 & 11 pixels, respectively; the corresponding values for /dv/ are 0 & 9 pixels

Mentions: Typically, PIV involves analysis of images of particles seeded in a fluid flow, with around 5 to 10 particles occupying every correlation box. To validate the use of CIV algorithms to quantify motion in grid-like images, we perform a systematic quantitative analysis of the errors associated with our estimates of known displacement fields. The error analysis is done for two kinds of images: (i) a noise-free 512 × 512 image of a black and white regular grid (Fig. 3a) that is similar to the grids formed by the tag lines in the 512 × 512 interpolated patient images, and (ii) the pre-processed phantom image shown in Fig. 1b. The images are subject to the following displacement field that represents a simplified model of the heart motion: 5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$\begin{array}{@{}rcl@{}} u_{r} &=& 0.02r, \end{array} $$ \end{document}ur=0.02r,Fig. 3


Estimation of myocardial deformation using correlation image velocimetry
a 512 × 512 synthetic grid image with grids of dimensions 23 ×23 pixels, bounded by 8 pixels thick white lines. The red horizontal line represents the cross-sectional cut used to estimate the errors shown in Fig. 4a and b. Imposed displacement field: bdu(x,y), the x component and cdv(x,y), the y component, as described by Eqs. (5) and (6). The minimum and maximum values of /du/ are 0 & 11 pixels, respectively; the corresponding values for /dv/ are 0 & 9 pixels
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5382518&req=5

Fig3: a 512 × 512 synthetic grid image with grids of dimensions 23 ×23 pixels, bounded by 8 pixels thick white lines. The red horizontal line represents the cross-sectional cut used to estimate the errors shown in Fig. 4a and b. Imposed displacement field: bdu(x,y), the x component and cdv(x,y), the y component, as described by Eqs. (5) and (6). The minimum and maximum values of /du/ are 0 & 11 pixels, respectively; the corresponding values for /dv/ are 0 & 9 pixels
Mentions: Typically, PIV involves analysis of images of particles seeded in a fluid flow, with around 5 to 10 particles occupying every correlation box. To validate the use of CIV algorithms to quantify motion in grid-like images, we perform a systematic quantitative analysis of the errors associated with our estimates of known displacement fields. The error analysis is done for two kinds of images: (i) a noise-free 512 × 512 image of a black and white regular grid (Fig. 3a) that is similar to the grids formed by the tag lines in the 512 × 512 interpolated patient images, and (ii) the pre-processed phantom image shown in Fig. 1b. The images are subject to the following displacement field that represents a simplified model of the heart motion: 5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$\begin{array}{@{}rcl@{}} u_{r} &=& 0.02r, \end{array} $$ \end{document}ur=0.02r,Fig. 3

View Article: PubMed Central - PubMed

ABSTRACT

Background: Tagged Magnetic Resonance (tMR) imaging is a powerful technique for determining cardiovascular abnormalities. One of the reasons for tMR not being used in routine clinical practice is the lack of easy-to-use tools for image analysis and strain mapping. In this paper, we introduce a novel interdisciplinary method based on correlation image velocimetry (CIV) to estimate cardiac deformation and strain maps from tMR images.

Methods: CIV, a cross-correlation based pattern matching algorithm, analyses a pair of images to obtain the displacement field at sub-pixel accuracy with any desired spatial resolution. This first time application of CIV to tMR image analysis is implemented using an existing open source Matlab-based software called UVMAT. The method, which requires two main input parameters namely correlation box size (CB) and search box size (SB), is first validated using a synthetic grid image with grid sizes representative of typical tMR images. Phantom and patient images obtained from a Medical Imaging grand challenge dataset (http://stacom.cardiacatlas.org/motion-tracking-challenge/) were then analysed to obtain cardiac displacement fields and strain maps. The results were then compared with estimates from Harmonic Phase analysis (HARP) technique.

Results: For a known displacement field imposed on both the synthetic grid image and the phantom image, CIV is accurate for 3-pixel and larger displacements on a 512 × 512 image with (CB,SB)=(25,55) pixels. Further validation of our method is achieved by showing that our estimated landmark positions on patient images fall within the inter-observer variability in the ground truth. The effectiveness of our approach to analyse patient images is then established by calculating dense displacement fields throughout a cardiac cycle, and were found to be physiologically consistent. Circumferential strains were estimated at the apical, mid and basal slices of the heart, and were shown to compare favorably with those of HARP over the entire cardiac cycle, except in a few (∼4) of the segments in the 17-segment AHA model. The radial strains, however, are underestimated by our method in most segments when compared with HARP.

Conclusions: In summary, we have demonstrated the capability of CIV to accurately and efficiently quantify cardiac deformation from tMR images. Furthermore, physiologically consistent displacement fields and circumferential strain curves in most regions of the heart indicate that our approach, upon automating some pre-processing steps and testing in clinical trials, can potentially be implemented in a clinical setting.

Electronic supplementary material: The online version of this article (doi:10.1186/s12880-017-0195-7) contains supplementary material, which is available to authorized users.

No MeSH data available.