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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.


The estimated displacement fields using (CB,SB)=(25,55) pixels: a, b as a function of x and y on the 512 × 512 pixels phantom image. The imposed displacement fields are shown in 3b–c. c Error eu in the horizontal displacement field plotted at every pixel location (x,y) for (CB,SB)=(25,55). d Error in the vertical displacement field (ev) plotted at every pixel location (x,y) for (CB,SB)=(25,55). The white lines in c) and d) correspond to /du/=3 pixels and /dv/=3 pixels, respectively. The boundaries of the myocardium, i.e. the grid-like region in the 512 × 512 pixels phantom image are marked approximately by the black circles. The colorbars in c) and d) are saturated at an error of 0.2 to bring out the features in the plots clearly
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Fig5: The estimated displacement fields using (CB,SB)=(25,55) pixels: a, b as a function of x and y on the 512 × 512 pixels phantom image. The imposed displacement fields are shown in 3b–c. c Error eu in the horizontal displacement field plotted at every pixel location (x,y) for (CB,SB)=(25,55). d Error in the vertical displacement field (ev) plotted at every pixel location (x,y) for (CB,SB)=(25,55). The white lines in c) and d) correspond to /du/=3 pixels and /dv/=3 pixels, respectively. The boundaries of the myocardium, i.e. the grid-like region in the 512 × 512 pixels phantom image are marked approximately by the black circles. The colorbars in c) and d) are saturated at an error of 0.2 to bring out the features in the plots clearly

Mentions: Figure 5a and b show the estimated horizontal () and vertical () displacement fields, respectively. The qualitative patterns of distribution of the imposed displacement fields, du and dv shown in Fig. 3b) and c), are captured well by the computed displacement fields, and . We recall that the pre-processed phantom image (Fig. 1b) contains grid lines in only an annular-like domain, and therefore it is important not to associate much significance to the estimated displacements in the non-grid, i.e. dark regions in the images.Fig. 5


Estimation of myocardial deformation using correlation image velocimetry
The estimated displacement fields using (CB,SB)=(25,55) pixels: a, b as a function of x and y on the 512 × 512 pixels phantom image. The imposed displacement fields are shown in 3b–c. c Error eu in the horizontal displacement field plotted at every pixel location (x,y) for (CB,SB)=(25,55). d Error in the vertical displacement field (ev) plotted at every pixel location (x,y) for (CB,SB)=(25,55). The white lines in c) and d) correspond to /du/=3 pixels and /dv/=3 pixels, respectively. The boundaries of the myocardium, i.e. the grid-like region in the 512 × 512 pixels phantom image are marked approximately by the black circles. The colorbars in c) and d) are saturated at an error of 0.2 to bring out the features in the plots clearly
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig5: The estimated displacement fields using (CB,SB)=(25,55) pixels: a, b as a function of x and y on the 512 × 512 pixels phantom image. The imposed displacement fields are shown in 3b–c. c Error eu in the horizontal displacement field plotted at every pixel location (x,y) for (CB,SB)=(25,55). d Error in the vertical displacement field (ev) plotted at every pixel location (x,y) for (CB,SB)=(25,55). The white lines in c) and d) correspond to /du/=3 pixels and /dv/=3 pixels, respectively. The boundaries of the myocardium, i.e. the grid-like region in the 512 × 512 pixels phantom image are marked approximately by the black circles. The colorbars in c) and d) are saturated at an error of 0.2 to bring out the features in the plots clearly
Mentions: Figure 5a and b show the estimated horizontal () and vertical () displacement fields, respectively. The qualitative patterns of distribution of the imposed displacement fields, du and dv shown in Fig. 3b) and c), are captured well by the computed displacement fields, and . We recall that the pre-processed phantom image (Fig. 1b) contains grid lines in only an annular-like domain, and therefore it is important not to associate much significance to the estimated displacements in the non-grid, i.e. dark regions in the images.Fig. 5

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.