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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.


Tracking landmark positions (shown as white dots in the first column of images) at each heart level (a) Apex b) Mid c) Base over a full cardiac cycle. The red and green lines represent ground truth and calculated positions, respectively. The x and y coordinates of the landmark position, indicated using the subscript p, are plotted in the second and third columns, respectively. Comparing ground truth (red) and calculated (green) landmark instantaneous displacements at each heart level over a full cardiac cycle. Ground truth includes data from two separate observers to show inter-observer variability. Both x (fourth column) and y (fifth column) components are plotted, with the error bars being based on inter-observer variability. The time t=0 corresponds to end diastole in all the plots
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Fig7: Tracking landmark positions (shown as white dots in the first column of images) at each heart level (a) Apex b) Mid c) Base over a full cardiac cycle. The red and green lines represent ground truth and calculated positions, respectively. The x and y coordinates of the landmark position, indicated using the subscript p, are plotted in the second and third columns, respectively. Comparing ground truth (red) and calculated (green) landmark instantaneous displacements at each heart level over a full cardiac cycle. Ground truth includes data from two separate observers to show inter-observer variability. Both x (fourth column) and y (fifth column) components are plotted, with the error bars being based on inter-observer variability. The time t=0 corresponds to end diastole in all the plots

Mentions: For further validation, we tracked the positions of landmarks given along with the data set and compared our results with the ground truth. Landmark positions at any phase of the cardiac cycle are updated by adding the estimated instantaneous displacement to the current location. Instantaneous displacement at the landmark locations are obtained using spatial interpolation of the estimated displacement fields at the corresponding time. As shown in 2 nd and 3 rd columns of Fig. 7, our results are close to the ground truth without any significant bias or accumulation of errors over the cardiac cycle. Specifically, the base and the mid level landmark locations are tracked better than at the apex. The errors at the apex are possibly due to three-dimensional effects.Fig. 7


Estimation of myocardial deformation using correlation image velocimetry
Tracking landmark positions (shown as white dots in the first column of images) at each heart level (a) Apex b) Mid c) Base over a full cardiac cycle. The red and green lines represent ground truth and calculated positions, respectively. The x and y coordinates of the landmark position, indicated using the subscript p, are plotted in the second and third columns, respectively. Comparing ground truth (red) and calculated (green) landmark instantaneous displacements at each heart level over a full cardiac cycle. Ground truth includes data from two separate observers to show inter-observer variability. Both x (fourth column) and y (fifth column) components are plotted, with the error bars being based on inter-observer variability. The time t=0 corresponds to end diastole in all the plots
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig7: Tracking landmark positions (shown as white dots in the first column of images) at each heart level (a) Apex b) Mid c) Base over a full cardiac cycle. The red and green lines represent ground truth and calculated positions, respectively. The x and y coordinates of the landmark position, indicated using the subscript p, are plotted in the second and third columns, respectively. Comparing ground truth (red) and calculated (green) landmark instantaneous displacements at each heart level over a full cardiac cycle. Ground truth includes data from two separate observers to show inter-observer variability. Both x (fourth column) and y (fifth column) components are plotted, with the error bars being based on inter-observer variability. The time t=0 corresponds to end diastole in all the plots
Mentions: For further validation, we tracked the positions of landmarks given along with the data set and compared our results with the ground truth. Landmark positions at any phase of the cardiac cycle are updated by adding the estimated instantaneous displacement to the current location. Instantaneous displacement at the landmark locations are obtained using spatial interpolation of the estimated displacement fields at the corresponding time. As shown in 2 nd and 3 rd columns of Fig. 7, our results are close to the ground truth without any significant bias or accumulation of errors over the cardiac cycle. Specifically, the base and the mid level landmark locations are tracked better than at the apex. The errors at the apex are possibly due to three-dimensional effects.Fig. 7

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.